articles.md

# Articles

## Table of Contents

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* [To read](#to-read)
	* [*Edtech:*](#edtech)
* [Baccino, T., Bellino, C. & Colombi, T. (2004). Mesure de l’utilisabilté des interfaces. Hermes Science Publications.](#baccino-t-bellino-c--colombi-t-2004-mesure-de-lutilisabilté-des-interfaces-hermes-science-publications)
* [Banihashem et al., 2018 : Learning Analytics: A systematic Literature Review](#banihashem-et-al-2018--learning-analytics-a-systematic-literature-review)
* [Bauckhage et al., 2015 – Clustering Game Behavior Datahttps://www.semanticscholar.org/paper/Clustering-Game-Behavior-Data-Bauckhage-Drachen/1970498fa49800a388e7920566478b95ca353d10](#bauckhage-et-al-2015--clustering-game-behavior-datahttpswwwsemanticscholarorgpaperclustering-game-behavior-data-bauckhage-drachen1970498fa49800a388e7920566478b95ca353d10)
* [Bediou et al., 2023 : Not screens but their context of use impact cognitive development: a commentary on Yang et al. (2023)](#bediou-et-al-2023--not-screens-but-their-context-of-use-impact-cognitive-development-a-commentary-on-yang-et-al-2023)
* [Bediou & Wac, 2022 : The role of cognition in mediating the relationship between media use and health in a media saturated world](#bediou--wac-2022--the-role-of-cognition-in-mediating-the-relationship-between-media-use-and-health-in-a-media-saturated-world)
* [(YT) Bogost, 2014 – Persuasive games: the expressive power of videogames](#yt-bogost-2014--persuasive-games-the-expressive-power-of-videogames)
* [(YT) Boshchenko, 2025 : How to collect application metrics if it needs to be done yesterday](#yt-boshchenko-2025--how-to-collect-application-metrics-if-it-needs-to-be-done-yesterday)
* [(YT) Blow, 2024 – On Burning out](#yt-blow-2024--on-burning-out)
* [(YT) Blow, 2024 – Jonathan Blow on leading an indie game company](#yt-blow-2024--jonathan-blow-on-leading-an-indie-game-company)
* [(YT) Blow, 2024 – Jonathan Blow on Simplicity](#yt-blow-2024--jonathan-blow-on-simplicity)
* [(YT) Blow, 2022 – Jonathan Blow on work-life balance and working hard](#yt-blow-2022--jonathan-blow-on-work-life-balance-and-working-hard)
* [(YT) Blow, 2018 – Techniques for dealing with lack of motivation malaise, depression](#yt-blow-2018--techniques-for-dealing-with-lack-of-motivation-malaise-depression)
* [Blumberg et al., 2019 : Digital Games as a Context for Children's Cognitive Development: Research Recommendation and Policy Considerations](#blumberg-et-al-2019--digital-games-as-a-context-for-childrens-cognitive-development-research-recommendation-and-policy-considerations)
* [Blume et al., 2023 : Individualisation in cognitive skills training: essential or superfluous? Examining the effectiveness of an adaptive game for training executive functions in young adults](#blume-et-al-2023--individualisation-in-cognitive-skills-training-essential-or-superfluous-examining-the-effectiveness-of-an-adaptive-game-for-training-executive-functions-in-young-adults)
* [Bourgeois, 2011 : Chapitre 1. Les théories de l’apprentissage: Un peu d’histoire... In Apprendre et faire apprendre (pp. 23–39)](#bourgeois-2011--chapitre-1-les-théories-de-lapprentissage-un-peu-dhistoire-in-apprendre-et-faire-apprendre-pp-2339)
* [Bowman et al., 2024 : A Continuous-Space Description of Video Games: A Preliminary Investigationhttps://psycnet.apa.org/doi/10.1037/ppm0000522](#bowman-et-al-2024--a-continuous-space-description-of-video-games-a-preliminary-investigationhttpspsycnetapaorgdoi101037ppm0000522)
* [Brom et al., 2018 : Do a Game Goal, Choice,Pointsm and Praise Enhance Learning ?](#brom-et-al-2018--do-a-game-goal-choicepointsm-and-praise-enhance-learning-)
* [Brown, 1992 : Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings](#brown-1992--design-experiments-theoretical-and-methodological-challenges-in-creating-complex-interventions-in-classroom-settings)
* [(YT) Cassavaugh, 2019 – If I Could Turn Back Time: A Game Developers Ultimate Quest](#yt-cassavaugh-2019--if-i-could-turn-back-time-a-game-developers-ultimate-quest)
* [(YT) Clark, 2023 : AI: work & learning, Can AI teach ?](#yt-clark-2023--ai-work--learning-can-ai-teach-)
* [Clow, 2013 : An overview of learning analytics](#clow-2013--an-overview-of-learning-analytics)
* [Collin, 2023 (conf) : Pour que l'IA parte sur de bonnes bases en éducation](#collin-2023-conf--pour-que-lia-parte-sur-de-bonnes-bases-en-éducation)
* [Cosnefroy, 2010 : L'apprentissage autorégulé : perspectives en formation d'adultes](#cosnefroy-2010--lapprentissage-autorégulé--perspectives-en-formation-dadultes)
* [Darejeh & Salim, 2016 : Gamification Solutions to Enhance Software User Engagement—A Systematic Review](#darejeh--salim-2016--gamification-solutions-to-enhance-software-user-engagementa-systematic-review)
* [Demszky et al., 2023 : Using large language models in psychology](#demszky-et-al-2023--using-large-language-models-in-psychology)
* [Deterding et al., From game design elements to gamefulness: defining "gamification"](#deterding-et-al-from-game-design-elements-to-gamefulness-defining-gamification)
* [Dicheva et al., 2015 : Gamification in Education: A Systematic Mapping Study](#dicheva-et-al-2015--gamification-in-education-a-systematic-mapping-study)
* [Dillenbourg et al., 1995 : The evolution of research on collaborative learning](#dillenbourg-et-al-1995--the-evolution-of-research-on-collaborative-learning)
* [(YT) Dillenbourg, 2023 : Les mythes qui sabordent les technologies éducatives](#yt-dillenbourg-2023--les-mythes-qui-sabordent-les-technologies-éducatives)
* [Dimov et al., 2023 : An Integrated Model of Collaborative Skill Acquisition: Anticipation, Control Tuning, and Role Adoption](#dimov-et-al-2023--an-integrated-model-of-collaborative-skill-acquisition-anticipation-control-tuning-and-role-adoption)
* [Dörner et al., 2016 : Serious Video Games](#dörner-et-al-2016--serious-video-games)
	* [CHAPTER 9 : Wiemeyer et al., 2016 : Player Experience](#chapter-9--wiemeyer-et-al-2016--player-experience)
	* [CHAPTER 10 : Wiemeyer et al., 2016 : Performance Assessment in Serious Games](#chapter-10--wiemeyer-et-al-2016--performance-assessment-in-serious-games)
* [Dye et al., 2009 - Increasing Speed of Processing with Action Video Games](#dye-et-al-2009---increasing-speed-of-processing-with-action-video-games)
* [Ergolab, 2005 : L'analyse concurrentielle en ergonomie](#ergolab-2005--lanalyse-concurrentielle-en-ergonomie)
* [(YT) Erhel – Comprendre les déterminants des usages problématiques d’Internet](#yt-erhel--comprendre-les-déterminants-des-usages-problématiques-dinternet)
* [Ermi & Mäyrä, 2011 : Fundamental Components of the Gameplay Experience: Analysing Immersion](#ermi--mäyrä-2011--fundamental-components-of-the-gameplay-experience-analysing-immersion)
* [Fischer et al., 2004 : Meta-Design a Manifesto for End-User Development](#fischer-et-al-2004--meta-design-a-manifesto-for-end-user-development)
* [Fleck et al., 2017 : “ Teegi, il est trop beau ”: Exemple d’évaluation du potentiel pédagogique d’une interface tangible interactive pour enfants en contexte scolaire](#fleck-et-al-2017---teegi-il-est-trop-beau--exemple-dévaluation-du-potentiel-pédagogique-dune-interface-tangible-interactive-pour-enfants-en-contexte-scolaire)
* [George et al., 2013 : Usages reflexifs des traves dans les environnements informatiques pour l'apprentissage humain](#george-et-al-2013--usages-reflexifs-des-traves-dans-les-environnements-informatiques-pour-lapprentissage-humain)
* [(YT) GGJ – IP Mistakes too many indies make (and how to avoid them)](#yt-ggj--ip-mistakes-too-many-indies-make-and-how-to-avoid-them)
* [(YT) GGJ – Getting IP Right: Tips for indie devs](#yt-ggj--getting-ip-right-tips-for-indie-devs)
* [Gregory et al., 2022 : An onboarding model for integrating newcomers into agile project teams](#gregory-et-al-2022--an-onboarding-model-for-integrating-newcomers-into-agile-project-teams)
* [Hoseini et al., 2022 : The Effect of 2D and 3D Action Video Game Interventions on Executive Functions in Male Students](#hoseini-et-al-2022--the-effect-of-2d-and-3d-action-video-game-interventions-on-executive-functions-in-male-students)
* [Houry, 2024 – Utiliser l'IA en cours de mandarin](#houry-2024--utiliser-lia-en-cours-de-mandarin)
* [Johnson & Senges, 2010 : Learning to be a programmer in a complex organization: A case study on practice‐based learning during the onboarding process at Google](#johnson--senges-2010--learning-to-be-a-programmer-in-a-complex-organization-a-case-study-on-practicebased-learning-during-the-onboarding-process-at-google)
* [Jolicoeur-Martineau, 2025 : Less is More: Recursive Reasoning with Tiny Networks](#jolicoeur-martineau-2025--less-is-more-recursive-reasoning-with-tiny-networks)
* [Jonassen, 2000 : Toward a design theory of problem solving](#jonassen-2000--toward-a-design-theory-of-problem-solving)
* [(YT) Kapur, 2022 : Productive Failure](#yt-kapur-2022--productive-failure)
* [Ladyman et al., 2013 : What is a complex system?](#ladyman-et-al-2013--what-is-a-complex-system)
* [Lallemand et al., 2015 : Création et validation d’une version franc¸ aise du questionnaire AttrakDiff pour l’évaluation de l’expérience utilisateur des systèmes interactifs](#lallemand-et-al-2015--création-et-validation-dune-version-franc-aise-du-questionnaire-attrakdiff-pour-lévaluation-de-lexpérience-utilisateur-des-systèmes-interactifs)
* [Lallemand & Gronier, 2016 : Méthodes de design UX. 30 méthodes fondamentales pour concevoir et évaluer les systèmes interactifs.](#lallemand--gronier-2016--méthodes-de-design-ux-30-méthodes-fondamentales-pour-concevoir-et-évaluer-les-systèmes-interactifs)
* [Lang et al. 2022 – Handbook of Learning Analytics (2nd ed.)](#lang-et-al-2022--handbook-of-learning-analytics-2nd-ed)
	* [CHAPTER 1 : Lang et al. 2022 – What is learning analytics](#chapter-1--lang-et-al-2022--what-is-learning-analytics)
	* [CHAPTER 2 : Gray & Berner, 2022 – A  Practitioner's Guide to Measurement in Learning Analytics - Decisions, Opportunities, and Challenges](#chapter-2--gray--berner-2022--a--practitioners-guide-to-measurement-in-learning-analytics---decisions-opportunities-and-challenges)
	* [CHAPTER 3 : Predictive Modelling in Teaching and Learning](#chapter-3--predictive-modelling-in-teaching-and-learning)
	* [CHAPTER 4 : Cacophony of Networks in Learning Analytics](#chapter-4--cacophony-of-networks-in-learning-analytics)
	* [CHAPTER 7 : Temporal Aspects of Learning Analytics - Grounding Analyses in Concepts of Time](#chapter-7--temporal-aspects-of-learning-analytics---grounding-analyses-in-concepts-of-time)
	* [CHAPTER 15: Game Learning Analytics](#chapter-15-game-learning-analytics)
* [Lankoski & Björk, 2015: Game Research Methods, an overview](#lankoski--björk-2015-game-research-methods-an-overview)
	* [CHAPTER 10. Landers & Bauer, 2015 : Quantitative methods and analyses for the study of players and their Behaviour, ](#chapter-10-landers--bauer-2015--quantitative-methods-and-analyses-for-the-study-of-players-and-their-behaviour-)
	* [CHAPTER 14. Wallner & Kriglstein, 2015 : An Introduction to Gameplay Data Vizualization](#chapter-14-wallner--kriglstein-2015--an-introduction-to-gameplay-data-vizualization)
	* [CHAPTER 19. Mohseni et al. 2015 : Extensive modding for experimental game research](#chapter-19-mohseni-et-al-2015--extensive-modding-for-experimental-game-research)
	* [CHAPTER 20. Waern & Back, 2015. Experimental Design.](#chapter-20-waern--back-2015-experimental-design)
* [Le Guennec, 2016 : Le métadesign, ou comment l’expérience doit échapper au designer](#le-guennec-2016--le-métadesign-ou-comment-lexpérience-doit-échapper-au-designer)
* [Lemercier et al., 2001 : Quels apprentissages sont-ils possibles avec des exerciseurs multimédia en classe ? Réflexions théoriques et compte rendu d’une expérience](#lemercier-et-al-2001--quels-apprentissages-sont-ils-possibles-avec-des-exerciseurs-multimédia-en-classe--réflexions-théoriques-et-compte-rendu-dune-expérience)
* [Lloyd, 2001 : Measures of Complexity: A nonexhaustive List](#lloyd-2001--measures-of-complexity-a-nonexhaustive-list)
* [Lövdén et al., 2020 - Human skill learning: expansion, exploration, slection, and refinement](#lövdén-et-al-2020---human-skill-learning-expansion-exploration-slection-and-refinement)
* [Lugan, 2009 : La modélisation des systèmes complexe chez E-Morin et J.-L. Le Moigne](#lugan-2009--la-modélisation-des-systèmes-complexe-chez-e-morin-et-j-l-le-moigne)
* [Luo et al., 2024 : Large language models surpass human experts in predicting neuroscience results](#luo-et-al-2024--large-language-models-surpass-human-experts-in-predicting-neuroscience-results)
* [Mayer, 2010 : Apprentissage et technologie](#mayer-2010--apprentissage-et-technologie)
* [Meri et al., 2022 : Higher access to screens is related to decreased functional connectivity between neural networks associated with basic attention skills and cognitive control in children](#meri-et-al-2022--higher-access-to-screens-is-related-to-decreased-functional-connectivity-between-neural-networks-associated-with-basic-attention-skills-and-cognitive-control-in-children)
* [Molinari et al., 2017 : Les émotions dans les situations de collaboration et d'apprentissage collaboratif médiatisées par ordinateur](#molinari-et-al-2017--les-émotions-dans-les-situations-de-collaboration-et-dapprentissage-collaboratif-médiatisées-par-ordinateur)
* [(YT) Morard, 2023 – AL2049, a PLayful Museum's Visit to Grasp the Issues of Complexity](#yt-morard-2023--al2049-a-playful-museums-visit-to-grasp-the-issues-of-complexity)
* [Morin, 1999 – Les sept savoirs nécessaires à l'éducation du futur](#morin-1999--les-sept-savoirs-nécessaires-à-léducation-du-futur)
* [Mosconi, 2004 : Effets et limites de la mixité scolaire](#mosconi-2004--effets-et-limites-de-la-mixité-scolaire)
* [Mulet et al., 2017 : Quels tuteurs informatisés pour réduire les comportements d’évitement de la recherche d’aides des apprenants.](#mulet-et-al-2017--quels-tuteurs-informatisés-pour-réduire-les-comportements-dévitement-de-la-recherche-daides-des-apprenants)
* [Nelson-Le Gall, 1985 : Help-Seeking Behavior in Learning](#nelson-le-gall-1985--help-seeking-behavior-in-learning)
* [Nguyen & Bavelier, 2023 : Play in Video Games](#nguyen--bavelier-2023--play-in-video-games)
* [Oliveira et al., 2022 : Game Design for a Museum Visit: Insights into the Co-design of AL2049, a Game About Food Systems](#oliveira-et-al-2022--game-design-for-a-museum-visit-insights-into-the-co-design-of-al2049-a-game-about-food-systems)
* [Ortiz-Rojas et al., 2017 : Gamification and learning performance. A systematic review of the literature](#ortiz-rojas-et-al-2017--gamification-and-learning-performance-a-systematic-review-of-the-literature)
* [Oviedo-García, 2021 : Journal citation reports and the difinition of a predatory journal: The case of the Multidisciplinary Digital Publishing Institute (MDPI)](#oviedo-garcía-2021--journal-citation-reports-and-the-difinition-of-a-predatory-journal-the-case-of-the-multidisciplinary-digital-publishing-institute-mdpi)
* [Parmentier, 2023 : Jeux expressifs :iLe récit suffit-il à générer du sens ? Étude du lien entre la richesse du gameplay d’un jeu expressif et la perception de sens chez le « joueur eudémonique ».](#parmentier-2023--jeux-expressifs-ile-récit-suffit-il-à-générer-du-sens--Étude-du-lien-entre-la-richesse-du-gameplay-dun-jeu-expressif-et-la-perception-de-sens-chez-le--joueur-eudémonique-)
* [Plass et al., 2015 : Foundations of Game-Based Learning](#plass-et-al-2015--foundations-of-game-based-learning)
* [Rétaux, 2023 : Analyse d'ouvrage par Xavier Rétaux](#rétaux-2023--analyse-douvrage-par-xavier-rétaux)
* [(YT) Rigby, 2017 : The Freedom Fallacy: Understanding "Player Autonomy" in Game Design](#yt-rigby-2017--the-freedom-fallacy-understanding-player-autonomy-in-game-design)
* [Roth, 2023 : Learning analytics ou comment lire les traces numériques pour aider les étudiant·e·s en difficulté](#roth-2023--learning-analytics-ou-comment-lire-les-traces-numériques-pour-aider-les-étudiantes-en-difficulté)
* [Sanchez, 2022 : Le paradoxe du marionnettiste, jeu et apprentissage](#sanchez-2022--le-paradoxe-du-marionnettiste-jeu-et-apprentissage)
* [Scheffel et al., 2014 : Quality indicators for Learning Analytics](#scheffel-et-al-2014--quality-indicators-for-learning-analytics)
* [Stepping Outside the Ivory Tower: How Can We Implement Multimodal Learning Analytics in Ecological Settings, and Turn Complex Temporal Data Sources into Actionable Insights?](#stepping-outside-the-ivory-tower-how-can-we-implement-multimodal-learning-analytics-in-ecological-settings-and-turn-complex-temporal-data-sources-into-actionable-insights)
* [Seif El-Nasr, 2013 : Game Analytics: Maximizing the Value of Player Data](#seif-el-nasr-2013--game-analytics-maximizing-the-value-of-player-data)
	* [CHAPTER 12 : Drachen et al., 2013 – Game Data Mining](#chapter-12--drachen-et-al-2013--game-data-mining)
	* [CHAPTER 19 : Seif El-Nasr – Visual Analytics Tools – A Lens into Player’s Temporal Progression and Behavior](#chapter-19--seif-el-nasr--visual-analytics-tools--a-lens-into-players-temporal-progression-and-behavior)
* [Sloane, 2017 : ‘Where no man has gone before!’ – Exploring new knowledge in design-based research projects: A treatise on phenomenology in design studies](#sloane-2017--where-no-man-has-gone-before--exploring-new-knowledge-in-design-based-research-projects-a-treatise-on-phenomenology-in-design-studies)
* [Sokolov et al., 2020 : Serious video games and virtual reality for prevention and neurorehabilitation of cognitive decline because of aging and neurodegeneration. Current opinion in neurology, 33(2), 239-248.](#sokolov-et-al-2020--serious-video-games-and-virtual-reality-for-prevention-and-neurorehabilitation-of-cognitive-decline-because-of-aging-and-neurodegeneration-current-opinion-in-neurology-332-239-248)
* [Stuckler, 2021 : 5 steps to write a research paper in a weekend](#stuckler-2021--5-steps-to-write-a-research-paper-in-a-weekend)
* [Szilas, 2022 : Serious game design in practice: lessons learned from a corpus of games developed in an academic context](#szilas-2022--serious-game-design-in-practice-lessons-learned-from-a-corpus-of-games-developed-in-an-academic-context)
* [Szilas, 2024 : Intelligence Artificielle dans l’éducation : le point de vue de la Terre](#szilas-2024--intelligence-artificielle-dans-léducation--le-point-de-vue-de-la-terre)
* [Taylor et al., 2020 : The Paradox of Help Seeking in the Entropy Mastermind Game](#taylor-et-al-2020--the-paradox-of-help-seeking-in-the-entropy-mastermind-game)
* [Tchounikine & Tricot, 2011 : Environnements informatiques et apprentissages humains](#tchounikine--tricot-2011--environnements-informatiques-et-apprentissages-humains)
* [Tondello et al., 2016 : The Gamification User Types Hexad Scale](#tondello-et-al-2016--the-gamification-user-types-hexad-scale)
* [Tricot, 2017 : Quels apports de la théorie de la charge cognitive à la différenciation pédagogique ? Queles pistes conctrètes pour adapter des situations d'apprentissage.](#tricot-2017--quels-apports-de-la-théorie-de-la-charge-cognitive-à-la-différenciation-pédagogique--queles-pistes-conctrètes-pour-adapter-des-situations-dapprentissage)
* [Tricot, 2021 : Le numérique permet-il des apprentissages scolaires moins contraints ? Une revue de la littérature](#tricot-2021--le-numérique-permet-il-des-apprentissages-scolaires-moins-contraints--une-revue-de-la-littérature)
* [(YT) Van Beckhoven, 2025 : Cracking the code review](#yt-van-beckhoven-2025--cracking-the-code-review)
* [Van Maanen & Schein, 1977 : Toward a Theory of Organizational Socialization](#van-maanen--schein-1977--toward-a-theory-of-organizational-socialization)
* [van Opheusden et al., 2023 : Expertise increases planning depth in human gameplay](#van-opheusden-et-al-2023--expertise-increases-planning-depth-in-human-gameplay)
* [Vienneau, 2005 : Apprentissage et enseignement : théories et pratiques](#vienneau-2005--apprentissage-et-enseignement--théories-et-pratiques)
* [Wang & Nunes, 2019 : Matching serious games with museum’s educational roles: smart education in practice](#wang--nunes-2019--matching-serious-games-with-museums-educational-roles-smart-education-in-practice)
* [Wood & Wood, 1999 : Help seeking, learning and contingent tutoring](#wood--wood-1999--help-seeking-learning-and-contingent-tutoring)
* [Zuboff, 2021 : L'âge du capitalisme de surveillance](#zuboff-2021--lâge-du-capitalisme-de-surveillance)
* [](#)

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## To read

### *Edtech:*

- Amadieu & Tricot (2014), Apprendre avec le numérique. Mythes et réalités. Ed. Retz.
- Barrette, C. (2011). Un voyage au pays des TIC. Pédagogie Collégiale, 24(4), 4'9.
- Bétrancourt, M. & Sutter-Widmer, D. (2013). Integrer les MITIC dans mon etablissement : enjeux et defis. In Actes du 84e seminaire de la CRoTCES - Conference Romande et tessinoise des chefs d'etablissement secondaire (pp 10 ' 15), Montana, 18-21 Septembre 2012.Boéchat-Heer, S. (2011). L'adaptation des enseignants aux usages des MITIC: sentiment d'auto-efficacité, formation et pratiques en classe, Formation et pratiques d'enseignement en question, 12, 81'97.
- Depover, C., Karsenti, T., & Komis, V. (2007). Enseigner avec les technologies : favoriser les apprentissages, développer des compétences. Québec: Presses de l'Université du Québec.
- Karsenti, T., & Fiévez, A. (2013). L'iPad à l'école: usages, avantages et défis. Rapport de recherche. Retrieved from http://s442047004.onlinehome.fr/images/classenumerique/ipadecole.pdf?link_credit=KennethRioja
- Shin, T., Koehler, M., & Mishra, P. (2009). Changing technological pedagogical content knowledge (TPACK) through course experiences. In International Conference of the Society for the Information and Technology & Teacher Education. Charleston, South Carolina.
- Zucker, A., & Light, D. (2009). Laptop programs for students. Science, 323(January), 82-85.
- https://www.solaresearch.org/events/lak/lak24/table-of-contents/

## Baccino, T., Bellino, C. & Colombi, T. (2004). Mesure de l’utilisabilté des interfaces. Hermes Science Publications.
http://tecfa.unige.ch/tecfa/maltt/ergo/articles/P1/focus_group_(Baccino2004).pdf

**THM** : 

**Tags** : :focus-group:

**Notes**
- What : 7/8 groupes d'users cibles interagissent autour d'un sujet pertinent pour l'étude à réaliser
- Objectif est de recueillir :
	- Opinion/avis : p.ex sur avantages que devraient présenter un service, les valeurs d'un service
	- Motivations
	- Attitudes à partir d'usages, pratiques
	- Expériences vécues sur utilisation
	- Idées sur fonctionalités
	- Cas d'utilisation
- Quand ?
	- En conception : afin de réaliser un produit qui convienne - plus sur caractéristiques des users, besoins, priorités, préférences, buts, représentations du prod et motivation à l'utiliser, etc.
	- En évaluation : pour discuter du produit et revueillir impressions positives et négatives
- Problématiques, situations, contextes :
	- Comment : simuler interaction et dialogue entre pp
	- Comment : pour déboucher sur idées, productions et arguments par le groupe
	- Comment : plusieurs groupes
	- Output : pas d'infos objectives sur efficacité du produit
	- Output : MAIS impressions subjectives des users par rapports aux paramètres de facilité d'utilisation/apprentissage/satisfaction
- Principe, description :
	- Comment : évaluateur doit favoriser échanges, modérer débats, lancer activités, rester neutre
	- Comment : organisés sous forme d'entretien de groupe OU activités par petits groupes
	- Output : perception ou représentation que les pp ont de l'objet, sentiments sur formes, couleurs, mots qu'ils voient (ou voudraient voir) associés au produit
- Protocole :
	- Préparation : liste d'activité en fonction des objectifs de l'étude
	- Briser la glace : mettre en confiance les pp, ex. points communs sur domaines spécifiques, discuter ensemble, debout pour bouger
	- Présentation des activités : chacun doit pouvoir prendre la parole sans la monopoliser, tous s'écoutent, toutes idée bonnes à écouter à parir du moment où concernent **opinions, motivations et pratiques des pp**, durée entre 1.5 et 3h
	- Déroulement : relancer, "concours de création du produit x", discussions et échanges d'arguments sur thèmes spécifiques, ex. liste de fonctionalités, argumenter et les classer par ordre d'activité
	- Analyse : recencement des principaux thèmes abordés et idées pour formuler les éléments de conception, pistes d'amélioration; conclusion à dire tout en demandant à être completée
	- Livrable/support : adjectifs revenant le plus souvent; remarques intéressantes/fréquentes, idées exploitables; matrice des éléments; synthèse générale avec degré d'importance, souhaits (fonctionalités, aspect général, valeurs) et risques et obstacles à éviter
- Ressources, coûts :
	- Lieu/salle : convivialité de la pièce, se voient tous, boissons/rafraîchissements, environnement inhabituel,
	- Enregistrement : évaluateur·trice ne prenant pas de notes, caméra
	- Evaluateur·trice : temps de préparation, présence sur tous les groupes, et temps nécessaire à l'analyse des enregistrements
	- Participant·e·s : non rémunérés, mais bons d'achat, billets, ou autres compensation
- Avantages :
	- Plus à l'aise en groupe que face à évaluateur·trice (ex. entretien) et meilleure verbalisation des représentations, ressentis
	- Faire émerger avis ou problématiques non-visibles en test user
	- Identifie aspects de l'utilisabilité
- Limites :
	- Difficulté à gérer groupe et échange entre les pp,, humain en groupe
- Techniques complémentaires/similaires :
	- Entretien : bien quand timides ou difficultés à exprimer en public et échanger informations avec d'autres personnes
	- Brainstorming : bien pour laisser créativité, spontanéité, à condition de ne pas s'éloigner du thème global
	- Technique d'échelle bipolaire (Bisseret et al., 1999) : bien pour se positionnner et évaluer selon 1 j'aime pas, 7 j'aime

## [Banihashem et al., 2018 : Learning Analytics: A systematic Literature Review](https://ijvlms.sums.ac.ir/article_44834.html)

**THM** : List of benefits and challenges, but we should not forget that LA aims at learning.

**Authors** : Seyyed Kazem Banihashem, Khadijeh Aliabadi, Saeid Pourroostaei Ardakani, Ali Delaver and Mohammadreza Nili Ahmadabadi

**Tags** : :learning-analytics: :technology: :education:

**Abstract** : Context: Learning analytics is considered as the third wave in educational technology and it is a new and promising field of study. This study was conducted to clarify benefits and challenges of learning analytics in education. Methods: Cooper’s systematic literature review was used as the research method. This method has five steps as follows: a) formulation of the problem, b) collection of data, c) evaluation of the appropriateness of the data, d) analysis and interpretation of relevant data, and e) organization and presentation of the results. Based on the study selection process, 36 articles were finally selected to be analyzed. Results: The results showed that ethics and privacy were one of the most important challenges of learning analytics in education along with the lack of attention to theoretical foundations and scope and quality of data. The results also showed that learning analytics could bring remarkable benefits for education, such as increased engagement of students, improvement of learning outcomes, identification of students at risk, providing real - time feedback, and personalization of learning. Conclusions: Based on the results, it can be concluded that learning analytics offer new insights in education, however, there are ethical, educational, and technical issues in the use of learning analytics in education

**Notes**
- Since 2010, the concept of Learning Analytics has been isolated from the field of analytics and has emerged as an independent area (Ferguson, 2012)
- Learning analytics seek to use educational data to improve learning, teachning and learning environments (Clow, 2013)
- Definition of LA was first presented by Society of Learning ANalytics Research (SOLAR) at First Learning Analytics and Knowledge Conference in 2011 (LAK-11) : "the measurement, colelction, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs" (Long et al., 2011)
- Fiaidhi (2014) said LA is the 3rd wave of EdTech
- Benefits of LA in education :
	- For learners : enhance engagement, improve learning outcomes, personalization of learning, increase in students adaptivity, enriche personalized learning environments
	- For teachers : Assessment services, efficient intervention, get RT feedback/insight, understand students learning habits, modifies content for students' desire, monitoring students' activities, get a deeper understand of teaching and learning, predicting student perf, provide warning signal, improve teaching strat, improve instructor perf, sources recommendations
	- For institutions : improve educational decision making, increase student success, student success modeling, monitoring stu activities, boost cost efficiency, increase retention rate, make envidence-based decisions, prevent stu drop-out, identifiy students at risk, curriculum improvement, improve accountability
	- For researchers : increase efficiency of education and serious games, identify knowledge gaps
	- For cours designers : identifying target course, improve learning design
	- For parents : monitoring stu activities
- Challenges of LA in education :
	- Ethics and privacy
	- Scope and Quality of Data : Gasevic et al (2015) pointed out that LA cannot fit one size to all. LA models should not be general and collect general data. It should focus on a specific scope of specific data with high quality that could  give true insights in learning and teaching. (link with PISA in Finland?)
	- Theoretical and Educational Foundation : (good section, to read) LA is about learning Gasevic et al (2014)

## [Bauckhage et al., 2015 – Clustering Game Behavior Data]()https://www.semanticscholar.org/paper/Clustering-Game-Behavior-Data-Bauckhage-Drachen/1970498fa49800a388e7920566478b95ca353d10

**THM** : Key points and questions before clustering game data

**Authors** : C. Bauckhage, Anders Drachen, R. Sifa

**Tags** : :cluster: :game-data: :play-analysis:

**Abstract** : Recent years have seen a deluge of behavioral data from players hitting the game industry. Reasons for this data surge are many and include the introduction of new business models, technical innovations, the popularity of online games, and the increasing persistence of games. Irrespective of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering and other techniques for player profiling and play style analysis have, therefore, become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics are pointed out.

**Notes**
- Yet, there are questions which can inform the choice of a cluster model:
	- Are the data high-dimensional and/or sparse? If so, consider models tailored to sparse data (AA or NMF).
	- What is the overall goal? To build general models of player behavior or to detect extreme behaviors (e.g., cheating, gold-farming)? For the former, consider centroid-seeking models ( -means, -medoids); for the latter, consider models such as AA.
	- Are the data numerical or relational? For the latter, use spectral clustering or kernel methods.
	- Are the players tightly grouped in variance space, so that -means might have difficulties distinguishing them? If so, consider density-based approaches that do not operate on Euclidean distances.
	- Are the data noisy? If so density-based methods might be appropriate as they are better tunable

## [Bediou et al., 2023 : Not screens but their context of use impact cognitive development: a commentary on Yang et al. (2023)](https://doi.org/10.1111/jcpp.13932)

**THM** : Context of use is dearly needed when talking about phone use

**Authors** :Benoit Bediou, Sezen Cekic, Daphné Bavelier

**Tags** : :child-development: :screen: :cognitive-development:

**Abstract** :There have been extensive debates about the impact of the digital transformation on human development. A recent study by Yang and colleagues highlights the importance of considering context of use, beyond amount of use. In their study, children from parents who reported having TV-on during family meals when they were 2 years old showed poorer cognitive development at age 3.5 as compared to those with TV-off during family meals. This highlights the importance of considering the context of use when studying effect of screen use. While Yang et al. discuss the distracting effects of TV-on sensory processing, we propose an alternative – and not mutually exclusive – interpretation based on TV induced deprivation of family interactions. On a more practical note, this should encourage to preserve screen-free time, especially during structured time such as family meals, in order to maintain family interactions known to be critical to development.

**Notes**
- Yang et al. ([2023](https://acamh.onlinelibrary.wiley.com/doi/10.1111/jcpp.13887)) most notable feature : effect of TV-on during family meals at 2yo on Child Development Index predicted 1% of variability in cognition
- BUT also detrimental effect of sleep depriation on cog is also 1% (Durmer et al., 2005) and beneficial effect of caffeine on memory in shift workers (Ker et al., 2010)
- Parent's tech consumption during meals : phubbing (phone & snubbing) + evidence that parental smartphone use is deleterious to parent-child interaction which in turn are critical to the dev of socioemotional and language skills
- TV-on during family meals at 2 years of age result in weaker cognition at 3.5 years of age.
- Screen time does not predict cognitive development.
- TV during meals may deprive from social interactions that promote healthy cognitive development.
- TV and other screens should be turned off during structured family time, such as meals to promote familial interactions critical to healthy development.

## [Bediou & Wac, 2022 : The role of cognition in mediating the relationship between media use and health in a media saturated world](https://doi.org/10.1016/B978-0-12-818872-9.00190-4)

**THM** : EF important to consider in childhood when using media (due to its high plasticity and not being fully mature) + EF influence, mediate and moderate relationship bw media use and media effects

**Authors** : Benoit Bediou, Katarzyna Wac

**Tags** : :digital-technologies: :EF: :media-content: :health:

**Abstract** : Digital technologies are transforming the way we live, work, communicate and entertain, putting our cognitive functions (in particular executive functions, EFs) under continuous stimulation and challenge. A growing body of work shows that carefully designed media content, combined with healthy use, can produce positive effects on cognition, education and health. Yet, excessive or other maladaptive consumption of digital media is often held responsible for worsening physical (obesity) and mental (depression) health, emotion (aggression) or cognition (drop in sustained attention and working memory capacity). In particular, smartphones, social media and video games are being increasingly held responsible for worsening the health and well-being of children and adolescents like increase in suicide, cyberbullying, eating disorders, problematic or addictive behaviors.

**Notes**
- When playing educational app, children of 2-3 yo were more likely to delay gratification (showing greater WM) vs viewing a cartoon ([Huber et al., 2018](https://doi.org/10.1016/j.jecp.2018.01.006))
- Problematic interactive media use (PIMU) is defined as the uncontrolled use of interactive screen media that results in negative consequences affecting an individuel's functionning ([Pluhar et al., 2019](https://doi.org/10.2147/PRBM.S208968))
- Cognitive and behavioral model of internet gaming disorder ([Dong & Potenza, 2014](https://doi.org/10.1016/j.jpsychires.2014.07.005)) & Interaction of person-affect-cognition-execution (I-PACE) model ([Brand et al., 2019](https://www.sciencedirect.com/science/article/pii/S0149763419303707?via%3Dihub])) are models for PIMU
- The concept of overall screen time has no predictive value regarding an effect on cognition or health (apart from extreme cases reflexting overuse); it has to be further differentiated for its context of use ([Kaye et al., 2020](https://doi.org/10.3390/ijerph17103661))
- Record passive device use data, especially Android OS (Aharony et al., 2011; Cornell, n.d.; Lathia et al., 2013; Tangmunarunkit et al., 2015)
- Pb of maintenance of such libraries collecting data because of rapide evolution of mobile OS
- Theoretical framework :
	1. EFs *influences* MU (incl. total screen time), which in turn impacts EFs (pos/neg feedback loops that can affect cog dev)
	2. EFs *mediate* the relationship bw MU and media effects (media effects reflect individual diffeerences in EF)
	3. EFs *moderate* the relationship between media use and media effects (EF skills can enhance or attenuate the effects of media use on health)
- See media use like eating food : quantity does not matter, but quality, physiology of the individual and other factors

## [(YT) Bogost, 2014 – Persuasive games: the expressive power of videogames](https://www.youtube.com/watch?v=h6onXZUbgbE)
- Teaching is about institutional politics
- Procedural rhetoric :
	- Processesses : the logics by which things work
	- Orator, Persuasion toward correct judgment (Aristotle); the art of effective expression
- 16

## [(YT) Boshchenko, 2025 : How to collect application metrics if it needs to be done yesterday](https://www.youtube.com/watch?v=3lJLnP5b4xU&list=PLRsbF2sD7JVoIn_ONlDxzXB6B7gB-ZZE6&index=19)

**THM** : 

**Authors** : Alina Boshchenko

**Tags** : :metrics:

**Notes**
- Metrics collection and aggregation: Dropwizard, Micrometer, Prometheus
- Storage: InfluxDB, Elasticsearch, Prometheus
- Visualisation: Grafana, Kibana, Prometheus dashboards
- All-in-one solutions: NewRelic, Sentry, Datadog
- ...

## [(YT) Blow, 2024 – On Burning out](https://www.youtube.com/watch?v=HkhHvP8emRM)
- Goggins
- Learn effectively by finding a project you really wnat to work on, that requires more Kn or Sk than you currently have so it's pushing or enlarging your boundaries + find what you can't do and have a specific thing to learn and very specific motivation to learn it

## [(YT) Blow, 2024 – Jonathan Blow on leading an indie game company](https://www.youtube.com/watch?v=_vcdDsaPiVA)
- You can't control what happens
- What you can control is how you comport yourself when things happen
- Respond to things with reasonable amount of grace
- When you're leading, other people are very limited in their capacity to do damage to the thing "that's a bummer but YOU are the one"

## [(YT) Blow, 2024 – Jonathan Blow on Simplicity](https://www.youtube.com/watch?v=4oky64qN5WI)
- Be reality based in engineering discipline means designing the thing that will solve the problem that you have
- People do not solve problems because they become attached to some system of doing things that's overly complex and doesn't address the problem
- A modern game is reading input, you simulate game state and you render -> this order that should happen in frame and also in order of how much they cost: reading and simulating is cheap, rendering is super expensive
- We're not allergic to complexity as programmers
- Keep it simple, because modern environment is "so screwed up"

## [(YT) Blow, 2022 – Jonathan Blow on work-life balance and working hard](https://www.youtube.com/watch?v=alfCi8QoZQ8)
- Putting in hours is how you get good
- Train your brain not with small portion, amount of time, you must be able to work longer if you like it
- Don't think that when working 10-20h/w will make you as better as someone who works 80h/w
- Everything is opportunity cost, there's positive and negative trade-offs

## [(YT) Blow, 2018 – Techniques for dealing with lack of motivation malaise, depression](https://www.youtube.com/watch?v=i7kh8pNRWOo)
- Look around, aware of this audio-visual experience, thought about something emotionally neutral => thoughts are actually small
- Feel sensations and put attention to it
- The actual sensation is neutral, your interpretation gives valence
- Turning attention on emotion then you feel it for a small portion of time
- Thoughts:
	- Do not necessarily identify so closely with thoughts because you are not your thoughts and these are very small
	- Have a viewpoint of standing back from your thoughts and seeing them happen because they won't instantly reactivate
- Sensations:
	- Fundamentally neutral
	- See the lower level of what's happening
- Emotional states:
	- They disappear instanly once you look at them
1. Observe bodily sensation, see what your body is feeling
2. Observe thoughts

## [Blumberg et al., 2019 : Digital Games as a Context for Children's Cognitive Development: Research Recommendation and Policy Considerations](https://doi.org/10.1002/sop2.3)

**THM** : Parental presence is essential when children are consumming media

**Authors** :Fran C. Blumberg, Kirby Deater-Deckard, Sandra L. Calvert, Rachel M. Flynn, C. Shawn Green, David Arnold, Patricia J. Brooks

**Tags** : :video-games: :children: :developmental: :digital-games: :policy:

**Abstract** : We document the need to examine digital game play and app use as a context for cognitive development, particularly during middle childhood. We highlight this developmental period as 6- through 12-year olds comprise a large swath of the preadult population that plays and uses these media forms. Surprisingly, this age range remains understudied with regard to the impact of their interactive media use as compared to young children and adolescents. This gap in knowledge about middle childhood may reflect strong and widely held concerns about the effects of digital games and apps before and after this period. These concerns include concurrent and subsequent influences of game use on very young children's and adolescents’ cognitive and socioemotional functioning. We highlight here what is currently known about the impact of media on young children and adolescents and what is not known about this impact in middle childhood. We then offer recommendations for the types of research that developmental scientists can undertake to examine the efficacy of digital games within the rapidly changing media ecology in which children live. We conclude with a discussion of media policies that we believe can help children benefit from their media use. Our hope is that this review will foster greater investigation of the cognitive socialization, as raised over 20 years ago by developmental psychologist and early games researcher Patricia Greenfield, that digital games serve during the middle childhood period, and childhood more generally.

**Notes**
- 2/3 of parents play on a weekly basis with their children perceiving it as beneficial to their children ([Entertainment Software Association, 2018](www.theesa./about-esa/industry-facts))
- Minecraft use in classroom ([Lane & Yi, 2017](https://doi.org/10.1016/B978-0-12-809481-5.00007-9))
- [American Academy of Pediatrics (2016)](https://doi.org/10.1542/peds.2016-2591) encourages co-viewing of television
- Parental warmth, engagement, playfulness and autonomy support predicted more positive child affect during joint interaction with edu game apps ([Griffith & Arnold, 2017](https://doi.org/10.1080/17482798.2018.1489866))
- An examination of children's learning from educational programs, including those designated by commercial broadcasters as E/I programs, revealed that second-to sixth-grade children preferred programs with socioal-emotional themes and learned social-emotional content after viweing those programs; childrent who viewed television programs with an academic focus also learned academic content ([Calvert & Kotler, 2003](https://www.sciencedirect.com/science/article/pii/S0193397303000601?via%3Dihub))
- Move forward by integrating media experiences into the lives of children, and those responsible for their care and education, in ways that foster 21st-century learning and thinking skills.

## [Blume et al., 2023 : Individualisation in cognitive skills training: essential or superfluous? Examining the effectiveness of an adaptive game for training executive functions in young adults](https://osf.io/preprints/psyarxiv/dzqjp)

**THM** :  no s* impact on the development of executive functions when using adapt vs. non-adapt games

**Authors** : Friederike Blume, Shashank Pawar, Manuel Ninaus, and Jan L. Plass

**Tags** : :cognitive-training: :adaptive-game:

**Abstract** : Considerable effort has been devoted to the development of training programmes aimed at effectively enhancing executive functions. Recently, specifically designed digital games have shown promise as a useful tool for this goal. The present study investigated the overall effect of a 120-minute training employing an executive function game that aimed to improve shifting abilities. Young adults played the game over four consecutive days, and the effects on shifting (near transfer), updating, and inhibition (far transfer) were examined. The study also investigated the differential effects of playing an adaptive versus a non-adaptive version of the game. Findings demonstrated that engaging in the shifting game enhanced individuals’ abilities in shifting, updating, and inhibition. In spite of the adaptive version’s greater challenge, there were no notable variations in training outcomes between the two game versions. The present study thus exhibits the effectiveness of games that train executive function, and contributes to the expanding evidence base in this area.

**Notes**
- In the adaptive game version, feeding rule complexity and speed were adjusted based on accuracy and reaction time, respectively. The percentage of aliens fed correctly in the previous level determined the feeding rule complexity adjustment. The game has three difficulty tracks, normal, difficult, and very difficult, which were used to make the necessary adjustments. Players were moved to a simpler track if they scored below 50%, remained on the same track for scores between 50% and 80%, and were moved to a more challenging one for scores greater than 80%. These numbers were established through playtesting the game
- Difference bw non-adapt & adapt game :
	- More challenging gameplay
	- No impact on shifting abilities in a crucial way
	- No diff in improvement of pps' updating abilities
	- No further improvement in pps' inhibition ability
 - "Finally, although the current study did not reveal any significant differences between the adaptive and non-adaptive versions of the game in terms of their impact on the development of executive functions, the potential for heightened participant motivation resulting from the adaptive version remained unexplored (e.g., Hidi & Renninger, 2006; Rotgans & Schmidt, 2011). Future research should investigate the impact of playing the adaptive versus nonadaptive version of the game on participants' motivation to play, especially during prolonged gameplay."

## [Bourgeois, 2011 : Chapitre 1. Les théories de l’apprentissage: Un peu d’histoire... In Apprendre et faire apprendre (pp. 23–39)](https://doi.org/10.3917/puf.brgeo.2011.01.0023)

**THM** : Pragmastisme - Behaviorisme - Gestaltisme - Constructivisme Piagétien - Apprentissage social - Socio-constructivisme - (néo-)vygotskien

**Authors** : Étienne Bourgeois

**Tags** : :learning-theory: :history:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- L'histoire des théories de l'apprentissage coïncide avec l'histoire de la psychologie scientifique moderne :
- 1879 - Wilheim Wundt avec premier laboratoire de psychologie expérimentale au monde à Leipzig. 
	- Modèle *structuraliste* = conscience humaine vue comme *somme de composants élémentaires*
- William James > G. Stanley Hall > John Dewey rejettent la vision atomiste de Wundt.
	- Paradigme *pragmatiste* - voient l'apprentissage comme un processus de transformation comprenant nécessairement deux composantes principales : l'*action* et la *réflexion* sur l'action.
	- "Learning by doing" de Dewey.
	- Composante méta-cognitive aussi importante : pour eux, pour enseigner il faut amener l'apprenant·e à agir avec son environnement physique et social et en même temps à réfléchir, s'interroger, à raisonner sur ce qu'ielle est en train de faire. Et pour que cela soit efficace, il faut que l'activité doit être perçue comme ayant du sens et utile pour lui/elle
- Edward Thorndike étudié avec James - apprentissage chez les animaux
	- *Behaviorisme* : essais/erreur, connexion entre un stimulus et une réponse comportementale - s'oppose au fonctionnalisme et au structuralisme car se focus que sur le comportement observable et non la conscience subjective
	- John B. Watson (élève de Dewey) - le comportement et sa modification résultent uniquement d el'effet de l'environnement
	- Ivan Pavlov : *Conditionnement classique*
	- B. F. Skinner - *Renforcements*
- *Gestaltisme* - Max Wertheimer, Kurt Koffka, Wolfgang Köhler, psychologie de la forme, insiste sur le rôle central de l'activité mentale du sujet dans l'apprentissage
	- Influence la psychologie d el'éducation via les paradigmes cognitivistes et constructivistes
- Théories du ttt de l'information = Sciences cognitives - reçoit, sélectionne, organise info, stocke en mémoire, récupère et communique - n'est pas un courant unifié
- Constructivisme piagétien - théorie de l'équilibration - entre le behaviorisme et la gestalt
	- Déséquilibre, destabilisation des connaissances initiales ("conflit cognitif") et afin de rétablir l'équilibre, soit rend compatible avec connaissances initiales, soit transforme ces dernières pour rendre compte l'information perturbante = apprentissage
- 1960 - apprentissage social, Albert Bandura, importance de l'observation d'autrui dans l'apprentissage - théorie "sociale-cognitive"
- Plus de théories unifiées/générales de l'apprentissage actuellement, mais plus "locales" pour l'apprentissage de différents aspects/thèmes ou situé dans différents contextes spécifiques

## [Bowman et al., 2024 : A Continuous-Space Description of Video Games: A Preliminary Investigation]()https://psycnet.apa.org/doi/10.1037/ppm0000522

**THM** : Following up on Dale et al., 2020. Space : x as action-like VG/control VG (e.g., condition control in psychog domain), y as narrative

**Authors** : Bowman, Nicholas David Klecka, Hanna Li, Zeyu Yoshimura, Koji Green, C. Shawn

**Tags** : :video-games: :brain: :behavior: :classification:

**Abstract** : Research on the impact of video games on the brain and behavior typically focuses, at its root, on particular experiences inherent in different types of gameplay. Historically, genre labels have been used to quickly classify games into (at least somewhat) nonoverlapping types of experiences. Genre labels have been successfully used in previous research to describe differences in experiences across games. However, modern video games increasingly borrow conventions from myriad genres. This increased complexity of modern games calls to question the applicability of genre labels to such research endeavors. Here we sought to determine whether it would be possible to create a valid continuous description of video games instead. We crowdsourced recognizable lists of 100 comparative retro (1985–1995) and 100 modern (2013–2020) video games. Fifty thousand human triplet comparison decisions regarding those games were then used to seed a machine learning algorithm to establish a continuous two-dimensional space in which individual video games could be situated. These two game spaces (modern/retro) were found to have reasonable validity and could be used to visualize and support beliefs about the game space, and how it has changed over the past 30 years.

**Notes**
- 50k individual human decision of "Is Game A more similar to Game B or Game C?"
- Next: include more dimensions and more games (like mobile and VR)

## [Brom et al., 2018 : Do a Game Goal, Choice,Pointsm and Praise Enhance Learning ?](https://doi.org/10.1177/0735633118797330)

**THM** : interesting integrated model of SDT, cognitive load and CATML theories - only a gamification effect on the perceived difficulty

**Authors** : Cyril Brom, Tereza Stárková, Edita Bromová, and Filip Děchtěrenko

**Tags** : :gamification: :learning: :serious-game: :motivation:

**Abstract** : Despite the increased interest in gamification approaches, there is a lack of comparative studies that shed light on the applicability of these approaches in educational contexts. In this explorative study, with an experimental design, university learners (N = 98) studied a complex process (i.e., how to brew beer) in a 2-hour-long computerized simulation. In the experimental condition, the simulation featured the following game design elements: game goals, increased freedom of choice, points, virtual currency, and praise (i.e., a gamified simulation). These elements were absent in the simulation versions used in the two control conditions. No differences in learning outcomes and intrinsic motivation variables between the gamified simulation and its nongamified versions were observed. The gamified simulation was perceived to be significantly easier than the nongamified versions (η2p = 0.10; d = 0.74, 0.42). Of the game elements used in this study, the participants perceived most positively a clear, game-like goal. The findings are consistent with self-determination theory, cognitive-affective theory of learning from media, and cognitive load theory. The findings also support the emerging notion that caution should be applied when using gamification approaches in educational contexts.

**Notes**
- Intro : 
    - *edu-gamification* : points, achievement badges and leaderboards; clear, game-like goals, and increased challenges; narratives; increased freedom of choice; and immediate feedback ([Darejeh & Salim, 2016](#darejeh--salim-2016--gamification-solutions-to-enhance-software-user-engagementa-systematic-review), [Dicheva et al., 2015](#dicheva-et-al-2015--gamification-in-education-a-systematic-mapping-study))
    - gamification puts emphasis only on the individual elements of games
    - PB : the alledged claims are not well supported by solid empirical evidence ([Ortiz-Rojas et al., 2017](#ortiz-rojas-et-al-2017--gaemification-and-learning-performance-a-systematic-review-of-the-literature)) + lack of information about which added game design elements are + or - in edu settings
    - explorative experimental study of whether a common edu-gamification approach can improve learning using a stand-alone computer simulation of a non-Information and Communication Technology topic
    - focus on game-like goal, points, increased freedom of choice, and praise that provides immediate feedback
    - Edu-Gamification Studies With Comparative Design :
        - badges and/or points with leaderboards *sometimes increase participation* in various activities or *improve students' time management* (see refs in article)
        - Hanus and Fox (2015), used badges, coins and leaderboard and found *no improvements on grades and sfrp class effort*.
        - 3 studies for ICT courses showed competition-driven gamified course version (ST goals, trophies, badges and leaderboard) helped complete homework assignments but 3/4 of pp rarely or never used gamified add-ons (see refs in article) => *not everyone needs it*
        - Katz et al. (2014) : achieved points not displayed showed best training perf + when all game-based elements almost worst in terms of training perf
    - Digital Game-Based Learning studies :
        - to assess the effect of individual game elements but inconclusive and conflicting results regarding the presence of ompetitive elements in edu games (see refs in article)
    - Seductive Details : 
        - *do not improve learning in general* (see refs) except presence of a narrative in game-based context beneficial for 10-yo children
    - Theoretical Standpoints : 
        - **Self-Determination Theory** (Deci & Ryan, 1985) : *Competence, Relatedness, Autonomy* 
            - expected tangible rewards may redzce the perception of autonomy
        - & **Intrinsic motivation** (Ryan & Deci, 2000) => doing an activity for its own sake because it is inherently interesting and enjoyable
        - -(+)-> **Cognitive-Affective Theory of Learning from Media** (CATLM, Moreno, 2005) and affective-motivational factors => "motivating and engaging"
        - while **Cognitive load theory** (Sweller et al., 2011) => most gamification methods are distracting; some can ease cognitive processing
            - segmenting of learning can reduce cognitive load
        - https://journals.sagepub.com/cms/10.1177/0735633118797330/asset/images/large/10.1177_0735633118797330-fig1.jpeg
- This study :
    - Game elements under investigation : 1) clear game goal; 2) increased feedom in choice of tasks to work on; 3) points/money given for doing well; 4) verbal rewards in the form of praise with some learning-related information
    - These are common ecologically-talking, suggested by pp in (Brom et al., 2014).
    - https://journals.sagepub.com/cms/10.1177/0735633118797330/asset/images/large/10.1177_0735633118797330-fig2.jpeg
- Methods : 
    - n=106 Czech uni students, course credits, Mage = 23.05 +- 2.53, 55% men, low-prior knowledge
    - Gamified grp n=31 vs Nongamified Conversational n=34, Nongamified formal n=33, gender and areas of study balanced
    - simulation of brewing beer in Czech language

**Comments**
- Good articulation between cognitive load theory and SDT
- Very well described and controled

**French resume made with Cappelle S.**
- L’article s’intéresse à l’effet de la gamification, c’est-à-dire « l’utilisation d’éléments de game design dans des contextes hors-jeu » (Deterding et al., 2011), induite dans un contexte éducationnel (‘edu-gamification’). L’expérience compare trois versions d’une simulation de brassage de bière : une gamifiée (nG = 31), deux non-gamifiée (nNC =34 ; nNF = 33). La version gamifiée inclut 1) un but du jeu clair, 2) une liberté de choix, 3) de l’argent virtuel, 4) des récompenses verbales. L’article essaye de créer un modèle unifié des théories d’auto-détermination (Deci & Ryan, 1985), de charge cognitive (Sweller et al., 2011) et de la théorie cognitivo-affective de l’apprentissage par les média (CATML, Moreno, 2005). Des questions de recherche ont été énumérées pour corroborer ce modèle : effets de la gamification sur la motivation intrinsèque et sur l’apprentissage, ainsi qu’une médiation entre ces deux derniers. Malheureusement, les résultats ne seront pas concluants. Seule la version gamifiée a été perçue comme plus facile. Nous noterons cependant qu’ils n’ont pas discuté de la partie visuelle de leur interface, élément qui aurait pu être pris en compte dans l’analyse.

## [Brown, 1992 : Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings](https://www.jstor.org/stable/1466837?seq=1&cid=pdf-reference#references_tab_contents)

**THM** : Jigsaw method (learners as teachers and researchers), it's ok to have Hawthorne effects in the classroom, be careful on selective and not representative examples you can give/share, focus more on general, reliable and repeatable anecdotes

**Authors** : Ann L. Brown

**Tags** : :ecological-conditions-experimentation: :teach: :teach-guidelines: :jigsaw-method:

**Notes**
- In an Intentional Learning Environment :
	- The computer are seen as tools for intentional reflextion and are meant to learn and collaborate.
	- An assessment is allowing knowledge discovery and utilization, also it can add material for a portfolio since they are considered as projects
- Jigsaw method (Aronson, 1978) :
	- Assigning curriculum theme, each divided in 5 subtopics
	- Students form 5 research groups, assigned responsibility for one of the five subtopics
	- These groups prepare the teaching material using state-of-the-art computer technology
	- Jigsaw method = students regroup into *learning groups* in which each student is expert in one subtopic, holding 1/5 of the information
	- Each one-fifth need to be combined with the remaining fifths to make a whole unit
	- All children in a learning group are expert on one part of the material, teach it to others, and prepare questions for the test that all will take on the complete unit
	- Children acquire expertise and learn how to learn from texts and other media (Brown & Campione, 1990)
- Methodological issues associated with attempts to assess conceptual change in situ :
	- Relationship between laboratory and classroom work :
		- Trade-off between experimental control and richness and reality
		- Lab work informs classroom observation and vice versa
	- Idiographic versus nomothetic approaches, oth the grain-size issue
		- Study one subject in depth vs. single variable in many subjects for discoveing general laws or principles of behavior ?
	- Bartlett Effect, or the problem of data selection
		- Select among a large data base : how to avoid misrepresenting the data, however unintentionally ?
		- Convert unfamiliar content into familiar material : we see the world through our existing knowledge and beliefs
- Lessons to be learned from some of the most famous design experiments :
	- Hawthorne Effect (Roethlisberger & Dickson, 1939) :
		- Nature of control in mutifaceted interventions
		- Description : Any intervention tends to have positive effects merely because of the attention of the experimental team to the subjects' welfare
		- Findings :
			1. All manipulations did not result in improvement
			2. When improvements occurred, they did so under three general conditions :
				- Workers *perceived* there to be improvements in the conditions being manipulated, whether or not this was so
				- Workers *perceived* the changes to be in their interest
				- Workers *perceived* that they were in control of their own condition of work = they were truly consultats of coinvestigators in the research endeavor
		- But having an Hawthorne effect still improves results, it only is perceived as a failure for the experimenters, not the students
		- Simple controls can never be entirely satisfactory; but they can provide insights into the operation of some of the major variables.
		- Brown's major defense is that " a "Hawthorne effect" is what I want: improved cognitive productivity under the control of the learners, eventually with minimal expense, and with a theoretical rationale for why things work"
	- Dewey Effect :
		- Romanticization of the process of discovery
		- Readiness to learn :
			- Child's cognitive level and his/her current place in society (Dewey, 1929)
			- Link with ZPD
		- Discovery learning :
			- The children learn best when discovering for themselves the verities of life (Dewey, 1929)
			- Link with constructivitst
			- Guided discorvery is in the middle, but it is a role difficult to maintain
			- It demands competence and confidents on the teacher-side
		- Curriculum and society :
			- Curricula should reflext the child's lived experiene and provide continuity with the family and community life (Dewey, 1929)
	- Reality Principle :
		- The problem of the shelf life of successful interventions
		- Lasting effects ?
		- Exhilaration > scientific credibility > disappointment and blame
		- = software dev : alpha (developmental phase, it works under ideal supportive condition) > beta (tryouts at carefully chosen sites with less, but still considerable, support) > gamma (widespread adoption with minimal support)
		- "What are the absolutely essential features that must be in place to cause change under conditions that one can reasonably hope to exist in normal school settings ?"
- Seduce students into the world of learning, to make them engaged in the learning content
- But tendency to romanticize research through claiming the success on a few engaging anecdotes or particulary exciting transcripts -> must convey general, reliable and repeatable anectdotes (not only the selective and not necessarily representative)
- Videotapes are invaluable for documenting conceptual change, in teachers as well as students, and they provide a common data base for discussion and reflextive action on the part of teachers and researchers. We can reanalyse and have new and more powerful theories of learning and concomitant methodologies emerging

## [(YT) Cassavaugh, 2019 – If I Could Turn Back Time: A Game Developers Ultimate Quest](https://www.youtube.com/watch?v=A7UGNevWx-4)

**THM** : When you've seen the point, you've missed it entirely

**Authors** : Joe Cassavaugh

**Tags** : :game-dev:

**Notes**
- No fear-based decision-making
- "Prod & Art directors 'marking' their territory with irrelevant game changes" e.g., make it bluer
- Do games that you love
- Cons for him of going solo (to solve):
	- Sloppy Dev (no checks & balances -> do UXR, no well-defined methods -> pre-document/register like a paper, no doc -> github), No synergy -> group of relatives to talk to?, no camaraderie -> SGDA
- Do prefabs
- Make something that is uniquerly yours
- Proving people wrong is still a great motivator
- Find better techniques
- Most things don't make a difference
- Do not sweat any decision, enough is enough
- The game of making original IP, the score is in sales
- Digital original-IP, the gift that keeps on giving

## [(YT) Clark, 2023 : AI: work & learning, Can AI teach ?](https://www.youtube.com/watch?v=K9TOD65BuXQ)

**THM** : 

**Authors** :

**Tags** :

**Abstract** :

**Notes**

## [Clow, 2013 : An overview of learning analytics](https://doi.org/10.1080/13562517.2013.827653)

**THM** : 

**Authors** : Doug Clow

**Tags** : :learning-analytics: :analytics: :metrics: :education:

**Abstract** : Learning analytics, the analysis and representation of data about learners in order to improve learning, is a new lens through which teachers can understand education. It is rooted in the dramatic increase in the quantity of data about learners and linked to management approaches that focus on quantitative metrics, which are sometimes antithetical to an educational sense of teaching. However, learning analytics offers new routes for teachers to understand their students and, hence, to make effective use of their limited resources. This paper explores these issues and describes a series of examples of learning analytics to illustrate the potential. It argues that teachers can and should engage with learning analytics as a way of influencing the metrics agenda towards richer conceptions of learning and to improve their teaching.

**Notes**
- Definition LA, see first sentence abstract
- 

## [Collin, 2023 (conf) : Pour que l'IA parte sur de bonnes bases en éducation](https://www.youtube.com/watch?v=5v3Fj9WJL_U)

**THM** : 

**Authors** : Simon Collin

**Tags** : :AI: :artificial-intelligence: :chatgpt: :education:

**Notes**
- IA : "emsemble des techniques qui permettent à une machine de simuler l'intelligence humaine, notamment pour apprendre prédire, prendre des décisions et percevoir le monde environnant." (p.19, Déclaration de Montréal pour une développement responsable de l'IA, 2018)
- Algo can't fix broken systems (Hao, 2020)
- Performance de la technologie par rapport au niveau de compétence des élèves : pour que la technologie soutienne l'apprentissage des élèves il faut leur fournir une techologie adaptée à leur niveau, e.g., si trop performance se trouve à faire leur job.
- Avoir des données standardisées
- Modèle prédictif converge-t-il avec modèle scientifique existant ?
- Augmenter la freq de collecte de données = charge admin supplémentaire
- Conséquences non-voulues :
	- Pression sur les enseignant·es par la comparaison et la hierarchisation des résultats de leurs groupes-classes
		- Dérivés pédago possibles (abandon de contenus d'enseignement non évalués; teach to the test, inflation des notes, etc.)
	- Intervention auprès des enseignant·es jugé·es "sous-performant·es"
		- Moyens de remédiation (conseiller·e pédago, etc.)
		- Poids des données probantes face à l'expérience subjective des enseignant·es
- Difficulté à sensibiliser les acteur·rices scolaires
- Reconfiguer l'expertise pédago entre :
	- Enseignant·es
	- Equipe de conception des SIA-ED : choix pédago de plus en plus automatisés en amont de la salle de classe
		- Choix opaques, difficiles à comprendre
		- Possibilité réduite pour les enseignant·es de les modifier
	- Administrateur·rices scolaires : contrôles des pratiques pédago des enseignant·es
		- SIA-ED de gestion scolare > SIA-ED de gestion scolaire ET pédago
- Faire preuve de discernement
- Aller doucement dans l'implémentation de l'IA dans l'éducation
- Les équipes de conception en éducation doivent être capables d'expliquer choix qui ont été faits, les risques, etc. ; si l'acteur·rice scolaire n'a pas compris, c'est que l'équipe de conception n'a pas assez pédagogisé son dispositif.
- e.g., Safer Technologies Schools, Australia
- 

## [Cosnefroy, 2010 : L'apprentissage autorégulé : perspectives en formation d'adultes](https://doi.org/10.3917/savo.023.0009)

**THM** : atteinte d'un but, non-linéaire, see the 5 models of autoregulation

**Authors** : Laurent Cosnefroy

**Tags** : :learning: :motivation: :autoregulation: :volition:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Comment l'apprenant·e réussit-ielle à se mettre au travail?
- Comment arrive-t-ielle à persévérer malgré les difficultés à atteindre les objectifs qu'ielle s'est fixé·e ?
- Comment contrôle-t-ielle ses processus d'apprentissage et sa motivation ?
- Apprentissage autorégulé = ensemble des processus par lesquels les sujets activent et maintiennent des cognitions, des affects et des conduites systématiquement orientés vers l'attente d'un but (Schunk, 1994). Il définit l'intensità avec laquelle l'individu est aux plans de la métacognition, de la motivation et de la conduite un participant actif dans ses processus d'apprentissage ([Zimmerman, 1989](https://psycnet.apa.org/fulltext/1990-06085-001.html))
	- 1er obstacle : métacognitif - *protection de l'intention d'apprendre* et maintenir la priorité sur l'activité en cours ([Corno, 1986](https://www.sciencedirect.com/science/article/abs/pii/0361476X86900299), [Eccles, 2005](https://psycnet.apa.org/record/2005-08058-007), [Lens & Vansteenkiste, 2008](https://psycnet.apa.org/record/2008-03967-006))
	- 2ème obstacle : motivation - *contrôle de l'action*, confrontation à la difficulté
- Déterminants motivationnels
- Effort
- Autonomie
- Conditions de l'autorégulation :
	- Motivation initiale suffisante, dépend de
		- *Perception de compétence* suffisante pour mener activité à son terme
		- Croyances motivationnelles concernant la *valeur de la tâche* (raisons pour s'engager dans celles-ci, [Eccles, 2005](https://psycnet.apa.org/record/2005-08058-007)) et le *sentiment d'efficacité personnelle* (see refs)
	- Définition d'un but à atteindre
	- Répertoire de stratégies d'autorégulation
		- Règles générales d'action qui orientent l'activité en vue de la rendre optimale par rapport au but visé.
	- Observation de soi (voire des autres apprenant·e·s)
		- Juger le travail accompli et de décider, si besoin est, de changer le fonctionnement actuel.
		- Auto-observation et auto-évaluation
- Modèles de l'apprentissage autorégulé :
	- Métacognition ([Winne, 1996](https://www.sciencedirect.com/science/article/abs/pii/S1041608096900229))
		1. Flux montant = *monitoring*, jugements métacog. informent de l'activité en cours
		2. Flux descendant = *control*, suite aux informations du monitoring, conduit à intervenir sur l'action en cours
		- L'importance accordée aux croyances épistémiques, et plus généralement à la dimension non consciente de l'autorégulation, est une spécifité du modèle de Winne.
	- Buts d'accomplissements ([Pintrich, 2000](https://doi.org/10.1016/B978-012109890-2/50043-3))
		- Reprend la distinction entre monitoring et contrôle
		- Ces buts orientent l'activité dans des contextes, créant un cadre pour interpréter et réagir aux événements qui se présentent.
		- Quatre façons de définir ce que signifie être compétent, selon deux axes approche/évitement et maîtrise/performance :
			1. Apprendre et progresser
			2. Faire mieux que les autres
			3. Eviter l'échec
			4. Maintenir la performance au niveau antérieur
		- Intérêt porté à l'influence du contexte sur les processus d'autorégulation : spécificités disciplinaires et les règles
	- Volition (Corno)
		- La volition est la protection de l'intention d'apprendre (= de la motivation), le fait de vouloir rester dans cet état.
		- Gestion de la distraction, comment garder ses capacités attentionnels sur l'objet d'étude ?
			1. Contrôle des processus internes (cognition, motivation et émotion), p.ex. une pause prévient la surcharge cognitive et permet un retour au calme
			2. Contrôle de l'environnement (Corno, 2001), p.ex. travailler avec des personnes studieuses
	- Auto-observation et l'auto récompense (Zimmerman)
		1. Agentivité
			- Ce modèle insistera donc sur deux facettes indissociables d el'autorégulation :
			1. AR proactive créatrice de buts et de plans d'action
			2. AR réactive afin de dépasser les obstacles empêchant l'atteinte du but
		2. Sentiment d'efficacité personnelle
			- Sentiment d'efficacité personnelle élevé favorise : (Schunk & Pajarès, 2005; Zimmerman, [1989](https://www.researchgate.net/publication/232534584_A_Social_Cognitive_View_of_Self-Regulated_Academic_Learning), [2000](https://doi.org/10.1016/B978-012109890-2/50031-7))
				- L'utilisation de stratégies cognitives performantes
				- L'engagement dans des activités plus difficiles
				- Le niveau d'effort
				- Le temps passé à étudier
		3. Auto-observation
			- Enregistrement des traces de l'activité
			- Dans les phases initiales d'un apprentissage, l'apprenant à besoin d'un guidage actif qui l'invite à analyser le déroulement de l'activité et l'enchaînement des opéations mises en oeuvre. Si livré à lui-même, si perf. peu satisfaisantes, va dvper des attributions en termes de manque de capacités qui viendront saper la poursuite ultérieure de l'effort ([Zimmerman & Kitsantas, 1997](https://psycnet.apa.org/doiLanding?doi=10.1037%2F0022-0663.89.1.29))
		4. Autorécompense
			- Privilégié dans l'approche siciocognitive pour réguler la motivation, renforcent le sentiment d'efficacité personnelle
	- Emotions (Boekaerts [1996](https://psycnet.apa.org/doiLanding?doi=10.1027%2F1016-9040.1.2.100), [1997](https://www.sciencedirect.com/science/article/abs/pii/S0959475296000151?via%3Dihub), [Boekaerts & Niemivirta, 2000](https://www.sciencedirect.com/science/article/pii/B9780121098902500421?via%3Dihub))
		- Une évaluation menaçante de la situation survient lorsqu'un but important est en jeu et que l'apprenant détecte un décalafe entre les exigences de la tâche et les ressources dont il dispose.
		- Maintien et configuration de deux buts/priorités d'égale importance :
		1. Accroître ses connaissances et ses compétences
			- Si gains escomptés > inconvérients (évaluation simulante) alors la situation d'apprentissage est perçue comme réelle opportunité d'apprentissage et l'intention d'apprendre se forme -> se met en *mode "maitrise"*
		2. Maintenir le sentiment de bien-être dans des limites raisonnables :
			- Si émotions négatives -> *Coping*, "mode défensif" (= maintenir ou restaurer un minimum de bien-être =/= réussir au mieux)
- Apprentissage autorégulé
	- Trois temps :
		1. Phase initial : plans formés, situation d'app. évaluée du point de vue des avantages et des menaces (aspect motivationnel)
		2. Phase centrale : engagement, contrôle de l'action pour atteindre le but fixé
		3. Evaluation de l'activité : restructuration des connaissances, réussite/échec, perception de compétence, modification des croyances métacognitives
		- Seul Corno est linéaire
	- Buts, trois facteurs à prendre en considération ([Locke & Latham, 2022](https://doi.org/10.1037//0003-066x.57.9.705); [Zimmerman, 2008](https://www.taylorfrancis.com/chapters/edit/10.4324/9780203831076-11/goal-setting-barry-zimmerman)
		1. But spécifique
		2. But proximaux
		3. Difficulté du but
		- Mais lié aux concept de soi possible ([Markus & Nurius, 1986](https://web.stanford.edu/~hazelm/publications/1986_Markus%20&%20Nurius_PossibleSelves.pdf)
- Stratégies d'autorégulation favorables à l'apprentissage :
	- Cognitives et métacognitives optimisent le ttt de l'info
	- Volitionnelles qui protègent l'intention d'apprendre
- Stratégies d'autorégulation défavorables à l'apprentissage :
	- Défensives qui protègent l'estime de soi
- Force autorégulatoire (Baumeister)
	- La répétition de l'autorégulation produit certes de la fâtigue à court terme mais conduit probablement sur le long terme à une extension de l aforce autorégulatoire ([Muraven et al., 1998](http://persweb.wabash.edu/facstaff/hortonr/articles%20for%20class/Muraven%20self-regulatoin.pdf); [Schmeichel & Baumeister, 2004](http://196.190.117.157:8080/xmlui/bitstream/handle/123456789/28342/162.pdf.pdf?sequence=1&isAllowed=y)
- Autorégulation indiviuelle
	- Définition de buts suffisamment précis ?
	- Auto-observation de l'activité avec une réflexion avant se s'engager dans l'activité ?
	- Dispositif introduit à un moment où l'apprenant a pu se constituer un répertoire de pratiques suffisamment stabilisés ?
- Autorégulation groupale
	- Volition moins nécessaire car les apprenant·e·s se protègent mutuellement
	- Comparaison groupale omniprésente : attention captée par interrogation sur sa propre valeur au détriment de la résolution de la tâche
- Autoformation
	- Apprentissage autodirigé ([Knowles, 1975](https://archive.org/details/selfdirectedlear0000know) : processus par lequel l'apprenant·e prend l'initiative, avec ou sans l'aide d'autrui, pour diagnostique ses besoins d'apprentissage, formuler des objectifs, identifier les ressources humaines et matérielles, choisir et mettre en oeuvre les stratégies d'apprentissage appropriées et évaluer les résultats obtenus.
	- Caractéristiques de l'apprenant autodirigé (Guglielmino (citée par Hoban et al., 2005)) : initiative, persistance, curiosité, désir d'apprendre, autodiscipline, confiance en soi, capacité à prendre les obstacles comme des défis à relever.

## [Darejeh & Salim, 2016 : Gamification Solutions to Enhance Software User Engagement—A Systematic Review](https://doi.org/10.1080/10447318.2016.1183330)

**THM** : 

**Authors** : Ali Darejeh & Siti Salwah Salim

**Tags** :

**Abstract** : Gamification is the use of video-game mechanics and elements in nongame contexts to enhance user engagement and performance. The purpose of this study is to conduct a systematic review to have an in-depth investigation into the existing gamification solutions targeted at solving user engagement problems in different categories of software. We carried out this systematic review by proposing a framework of gamifying process, which is the basis for comparison of existing gamification solutions. In order to report the review, the primary studies are categorized according to the following: a) gamified software and their platforms; b) elements of the gamifying process; c) gamification solutions in each software type; d) gamification solutions for software user engagement problems; e) gamification solutions in general; and f) effects of gamification on software user engagement and performance. Based on the search procedure and criteria, a total of 78 primary studies were extracted. Most of the studies focused on educational and social software, which were developed for web or mobile platforms. We concluded that the number of studies on motivating users to use software content, solving problems in learning software, and using real identity is very limited. Furthermore, few studies have been carried out on gamifying the following software categories: productivity software, cloud storage, utility software, entertainment software, search engine software, tool software, fitness software, software engineering, information worker software, and health-care software. In addition, a large number of gamification solutions are relatively simple and require improvement. Thus, for future studies, researchers can work on the items discovered in this review; they can improve the quality of the current gamified systems by using a wide variety of game mechanics and interface elements, utilizing a combination of contextual types of rewards and giving users the ability to use received rewards “in-game” and “out-game.”

**Notes**

**Comments**

## [Demszky et al., 2023 : Using large language models in psychology](https://www.nature.com/articles/s44159-023-00241-5#Sec9)

**THM** : see conflicts of interest and does not give designs and methods in psychology

**Authors** :Dorottya Demszky, Diyi Yang, David S. Yeager, Christopher J. Bryan, Margarett Clapper, Susannah Chandhok, Johannes C. Eichstaedt, Cameron Hecht, Jeremy Jamieson, Meghann Johnson, Michaela Jones, Danielle Krettek-Cobb, Leslie Lai, Nirel JonesMitchell, Desmond C. Ong, Carol S. Dweck, James J. Gross & James W. Pennebaker

**Tags** : :LLM: :psychology:

**Abstract** : Large language models (LLMs), such as OpenAI’s GPT-4, Google’s Bard or Meta’s LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we review the foundations of LLMs. We then explain how the way that LLMs are constructed enables them to effectively generate human-like linguistic output without the ability to think or feel like a human. We argue that although LLMs have the potential to advance psychological measurement, experimentation and practice, they are not yet ready for many of the most transformative psychological applications — but further research and development may enable such use. Next, we examine four major concerns about the application of LLMs to psychology, and how each might be overcome. Finally, we conclude with recommendations for investments that could help to address these concerns: field-initiated ‘keystone’ datasets; increased standardization of performance benchmarks; and shared computing and analysis infrastructure to ensure that the future of LLM-powered research is equitable.

## [Deterding et al., From game design elements to gamefulness: defining "gamification"](https://doi.org/10.1145/2181037.2181040)

**THM** : MAIN DEFINITION OF GAMIFICATION : "Gamification is the *use of game desgin elements in non-game contexts*.

**Authors** :Sebastian Deterding, Dan Dixon, Rilla Khaled, Lennart Nacke

**Tags** : :gamification: :definition:

**Abstract** : Recent years have seen a rapid proliferation of mass-market consumer software that takes inspiration from video games. Usually summarized as "gamification", this trend connects to a sizeable body of existing concepts and research in human-computer interaction and game studies, such as serious games, pervasive games, alternate reality games, or playful design. However, it is not clear how "gamification" relates to these, whether it denotes a novel phenomenon, and how to define it. Thus, in this paper we investigate "gamification" and the historical origins of the term in relation to precursors and similar concepts. It is suggested that "gamified" applications provide insight into novel, gameful phenomena complementary to playful phenomena. Based on our research, we propose a definition of "gamification" as the use of game design elements in non-game contexts.

## [Dicheva et al., 2015 : Gamification in Education: A Systematic Mapping Study](https://www.jstor.org/stable/jeductechsoci.18.3.75)

**THM** : 

**Authors** : Darina Dicheva, Christo Dichev, Gennady Agre and Galia Angelova

**Tags** :

**Abstract** :

**Notes**

**Comments**

## [Dillenbourg et al., 1995 : The evolution of research on collaborative learning](https://telearn.archives-ouvertes.fr/hal-00190626)

**THM** : cooperation =/= collaboration, socio-constructivism/socio-cultural/shared-cognition

**Authors** : Pierre Dillenbourg, Michael J. Baker, Agnès Blaye, Claire O’Malley

**Tags** : :collaborative-learning:

**Abstract** : For many years, theories of collaborative learning tended to focus on how individuals function in a group. More recently, the focus has shifted so that the group itself has become the unit of analysis. In terms of empirical research, the initial goal was to establish whether and under what circumstances collaborative learning was more effective than learning alone. Researchers controlled several independent variables (size of the group, composition of the group, nature of the task, communication media, and so on). However, these variables interacted with one another in a way that made it almost impossible to establish causal links between the conditions and the effects of collaboration. Hence, empirical studies have more recently started to focus less on establishing parameters for effective collaboration and more on trying to understand the role which such variables play in mediating interaction. In this chapter, we argue that this shift to a more process-oriented account requires new tools for analysing and modelling interactions. 

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- DAI = Distributed AI
- Cooperation = "couper" - hierarchical - separation of each portion of the problem solving
- Collaboration = "coller" - heterarchical - coordination on effort to solve the problem together
- Socio-constructivist approach (Piaget, Mugny, Doise)
	- Importance of the role of *inter*-actions with other rather than actions themselves
	- Mediating process = "socio-cognitive conflict"
- Socio-cultural approach (Vygotsky)
	- Internalisation of inter-psychological processes
	- Appropriation (Rogoff, 1991) is the socially-oriented version of Piaget's biologically-originated concept of assimilation (Newman & al., 1989)
		- Each partner gives meaning to the other's actions according to his or her own conceptual framework.
- Shared cognition approach (Suchman, 1987)
	- The environment is an integral part of cognitive activity, and not merely a set of circumstances in which context-independent processes are performed. (criticism of lab driven work?)
	- A group forms a single cognitive system.
	- Social interactions are studied as if they occur outside a social structure (Wertsch, 1991)
	- Providing explanations leads to improve knowledge (Webb, 1991)
	- From a 'group' perspective, explanation is not something delivered by the explainer to the explainee. It is instead constructed jointly by both partners trying to understand each other (Baker, 1991)
- Research paradigms
	1. Effect paradigm
		- "Is collaborative learning more efficicient than learning alone ?"
		- Contradictory resuslts but positive (Slavin, 1983; Webb, 1991) > negative (ex. low achievers progressively become passive when collaborating with high achievers (Salomon & Globerson, 1989; Mulryan, 1992).
	2. Conditions paradigm
		- "Under which conditions is collaborative learning efficient ?"
		- To determine the conditions under which collaborative learning is efficient, one has to vary these conditions systematically (composition of group, features of task, context of collaboration, medium for collaboration)
		1. Group heterogeneity :
			- The ZPD defines an optimal difference in an idnirect way, i.e. not as a difference between subjects A and B, but as a difference between how A performs alone and how A performs with B's assistance
			- Pairs > Larger groups
			- Pairs (coop) > Triad (competitive) (Trowbridge, 1987)
			- Differences between group sizes seem to disappeat when children are fiven the opportunity to interact with other in the class (Colbourn & Light, 1987)
		2. Children's individual prerequisites :
			- Must be skilled at the task (Rogoff, 1990)
			- Must reflect upon his/her own performance with respect to the task
			- Must assess whether learner's action was wrong // instruction or task, and be able to produce the next tutorial action on the basis of both a representation of the previous instruction and an evaluation of the learner's response to that instruction. e.g. 5 yo difficulty in inhbiting to allow their 'tutee' to learn, however better 'collaborators' than 3 yo comparison dyads (Wood et al.)
		3. Task features
			- The nature of the task, whether it is procedural or requires no planning, influences the results
		4. Interactions between variables
			- Rogoff (1990) argues tha w
			- t planning tasks may be difficult for very young children because they require reference to things which are not in the "here-and-now".
	3. Interaction paradigm
		- "Which interactions occur under which conditions ?"
		- "What effects do these interactions have ?"
		- Elaborated explanations are not related to the explainee's performance, but they are positively correlated with the explainer's performance (Webb, 1991)
		- Explainer's benefits (Bargh & Schul, 1980)
		- Self-explanation effect (Chi et al., 1989)
		- Providing the answer while expecting a an explanation does not help to understand the strategy, and may lead the explainee to infer an incorrect strategy or lose motivation to understand the strategy (Webb, 1991)
		- Group composition : more explanation when moderately heterogeneous (high/med, med/low ability) and homogeneously of med VS. detrimential when homo high (they all know), homo low (nobody can help) and hetero h/m/l (med feel excluded) (Webb, 1991)
		- When pairs of high ability expert and low ability novice -> tutoring or guidance by the h child. (Verba & Winnykamen, 1992)
		- When pairs of high ability novice and low ability expert -> collaboration and joint construction (Verba & Winnykamen, 1992)
- Dyadic work at the computer may enhance the benefit derived from the collaborative larning situation (Blaye et al., 1990)
- Aspects of the software can modify the socio-cognitive dynamics between the learning partners. The computerised learning environment constitutes in itself a mediational resource which can contribute to create a shared referent between the social partners (Roschelle & Teasley)
- Stop using 'collaboration' and 'explanation' as these are now too large.
- "We have to study and understand the mechanisms of negociation to a much greater depth than we have so far"

## [(YT) Dillenbourg, 2023 : Les mythes qui sabordent les technologies éducatives](https://www.youtube.com/watch?v=OS1-n9zmFdM)

**THM** : Les edtech *permettent des activités cognitives*, *individuelles ou en groupe*, qui ont un effet d'apprentissage *si dûment orchestrées par l'enseignant·e* (ET PAS : les technologies educatives ont un effet intrinseque)

**Authors** : Pierre Dillenbourg

**Tags** : :learning: :edtech: :myths:

**Notes**
- Nos méthodologies peuvent propager ces croyances, X effet sur ...
- Comment prouver les effets ?
	- Attention à l'effet de l'enseignant·e : Les edtech s'intègrent dans des séquences d'activités orchestrées par l'enseignant·e
- Technologies =/= innovation, c'est pas parce qu'utilise une techno qu'on innove
- Problem solving (preparation pour le learning) then lecture (donner du sens – [A time for telling](https://www.tandfonline.com/doi/abs/10.1207/s1532690xci1604_4)) + [Sinha & Kapur (2021)](https://journals.sagepub.com/doi/10.3102/00346543211019105)
- Prédire une des meilleures activités d'apprentissage.
- L'effet enseignant n'est pas un biais méthodologique
- "individualisation" : non, avoir un ordi utilisé par 3 personnes en mêmes temps et trois souris qui controlent chacune un élément spécifique, ça oui
- Doise & Mugny : créer des pairs qui ont des points de vue différents pour argumenter ensemble (conflit socio-cognitif)
- Réalité augmentée : rendre l'invisible visible, rendre l'impossible possible, explorer l'espace des variations possibles
- Papier et digital et l'effet de la séquence qui +++
- Insérer la classe (rôle de l'enseignant dans l'outil + classe) dans l'équation de l'évidence translationnelle
- Est-ce que cette technologie me permet de faire quelque chose en classe que je ne peux pas faire ?

## [Dimov et al., 2023 : An Integrated Model of Collaborative Skill Acquisition: Anticipation, Control Tuning, and Role Adoption](https://onlinelibrary.wiley.com/doi/10.1111/cogs.13303)

**THM** : Role adoption, anticipatory action and control parameters benefits skill acquisition

**Authors** : Cvetomir M. Dimov, John R. Anderson, Shawn A. Betts, Dan Bothell

**Tags** : :ACT-R: :Anticipation: :Collaborative-skill-acquisition: :Role-adoption: :Supervised-learning:

**Abstract** : We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress.While gaining expertise, the majority of subjects became increasingly consistent in the role theyadopted without being able to communicate. Moreover, they acted in anticipation of the future taskstate. We constructed a collaborative skill acquisition model in the cognitive architecture ACT-R thatreproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learningand predictive processes through motion extrapolation and learned relevant control parameters usingboth a reinforcement learning procedure and a new to ACT-R supervised learning procedure. This isthe first integrated cognitive model of collaborative skill acquisition and, as such, gives us valuableinsights into the multiple cognitive processes that are involved in learning to collaborate

**Notes**
- Role adoption : role consistency benefits skill acquisition : faster and more effortless to learn and proceduralize a smaller set of behavioral patterns than a larger one + mutual adaptation is easier the more stable partner behavior is – sticking on a role can be considered a coordination smoother (Vesper et al., 2010) + personal abilities lead them to choose the roles that make their joint task easier (Haiman, 1957)
- Anticipatory actions
- Control parameters
- Skill acquisition processes in collaboration : cog control is a key component of their model; the model acquires skill through production compilation and control tuning; prediction is also likely a major factor in joint action peformance.

## [Dörner et al., 2016 : Serious Video Games](http://link.springer.com/10.1007/978-3-319-40612-1)

### [CHAPTER 9 : Wiemeyer et al., 2016 : Player Experience](https://link.springer.com/chapter/10.1007/978-3-319-40612-1_9)

**THM** : Great overview of the different definitions of PE

**Authors** : Josef Wiemeyer, Lennart Nacke, Christiane Moser & Florian ‘Floyd’ Mueller 

**Tags** : :serious-video-games: :player-experience:

**Abstract** : In computer science, the concept of user experience has proven to be beneficial in order to improve the quality of interaction between software and its users, by taking users’ emotions and attitudes into account. In general, user experience focuses on interaction. As not only interaction (e.g., good usability) is of importance for players, this chapter discusses how the concept of user experience can not only be applied to serious games, but also how it can be extended in order to cover the characteristics of games as a special software. For this refined concept, the term player experience has been coined. First, the concept of player experience is introduced in this chapter. The adequate conceptualization of player experience requires differentiating specific dimensions like (game-) flow, immersion, challenge, tension, competence, and emotions. Because of the individual nature of player experience, psychological models need to be used for the conceptualization as they are able to reflect this multidimensional structure. In addition, interdisciplinary models are needed in order to address the various factors influencing player experience. This ensures a holistic approach. Second, the question how to measure player experience is discussed. Here, different levels have to be distinguished: Behavior, physiological reactions, and subjective experience. Finally, it is shown how knowledge about player experience can be employed to develop serious games systematically and to improve their quality.

**Notes**
- DEFINITION PLAYER EXPERIENCE (PX) : "player experience denotes the individual and personal experience of playing games. Player experience describes the qualities of the player-game interactions and is typically investigated during and after the interaction with games
- The goal of SG is to exploit [the fascination of the uniqueness of gaming experience] to enhance engagement, in order to foster the acquisition of the characterizing goal(s).
- Player experience more appropriate term as compared to game experience, because it is the person of the player who makes this specific experience.
- Player types, see [Nacke et al (2014) : BrainHex: A neurobio gamer typology survey](https://www.sciencedirect.com/science/article/abs/pii/S1875952113000086?via%3Dihub)
- Classic usability testing is not sufficient for testing games, since its standard metrics, such as effectiveness measured as task completion or efficiency measured as error rates, do not map directly to evaluating games
- The focus on humans as part of evaluating technology is now the de facto standard for many evaluation approaches within human-computer interaction (HCI) and has led to prominence of user experience (UX) research over usability research
- Playability, game usability (techno level) & player experience (ind and personal experience of playing games)
- In games research Player experience > Game experience
- [Gerling et al. (2011)](https://doi.org/10.1145%2F2181037.2181052) state that the term player experience “in video games describes the individual perception of the interaction process between player and game,” and is derived from the phenomenon of UX (defined in ISO 9241-210:2010) describing how a person perceives and responds to the interaction with a system—both highlighting the subjective, psychological nature of the phenomenon and focusing on the interaction process.
- [Lazzaro (2008)](https://edisciplinas.usp.br/pluginfile.php/7739573/mod_resource/content/1/Game_Usability_2008_cap20_Nicole_Lazzaro.pdf) argues that UX and PE are not the same . For her, UX is the experience of use (i.e., how easily and well suited is the system to the task or what the person expects to accomplish in order to advancing the usability), while PE is the experience of play (i.e., how well the game supports and provides the type of fun the player wants to have). She claims that UX looks at what prevents the ability to play, and PE looks at what prevents the player from having fun.
	- -> Four fun keys :
		- Hard Fun : challenge and mastery
		- Easy Fun : inspiring imagination, exploration, and RP
		- Serious fun : changing a player's internal state or doing real work
		- People fun : social interaction
	- -> "The goal of testing is to improve quality. However, there is more to software quality than ease of use. There is a lot more going on for games. Without usability no one can play a game, make it too usable and it’s no fun. That’s the difference between user experience design (UX) and player experience design (PX). Games create emotions from experiences that usability and traditional market research cannot measure"
- Generic models that have been developed for a wide range of application areas including gaming :
	- Self-determination theory :
		- Competence, autonomy, relatedness
		- Player Experience of Need Satsfaction (PENS) model ([Ryan, Rigby & Przybylski, 2006](https://link.springer.com/article/10.1007/s11031-006-9051-8)) :
			- PENS in-game autonomy : This dimension denotes the experience of the players to feel free to make decisions and choices in the game.
			- PENS in-game competence : This dimension concerns an appropriate balance between the challenges of the game and the competence level of the players.
			- PENS in-game relatedness : In-game relatedness means, how much the players are feeling connected to other players in the game.
			- PENS presence : This dimension is subdivided into three subdimensions, i.e., physical, emotional, and narrative presence.
			- PENS intuitive controls: This dimension concerns the ease of control in the game, e.g., by easy-to-remember control keys. 
	- Attention, relevance, confidence, satisfaction (ARCS, [Keller 1987](https://link.springer.com/article/10.1007/bf02905780)) :
	- Flow ([Nakamura & Csikszentmihalyi (2002)](https://scholar.google.com/scholar?hl=fr&as_sdt=0%2C5&q=Nakamura+J%2C+Csikszentmihalyi+M+%282002%29+The+concept+of+flow&btnG=))
	- Presence and immersion 
- Domain-specific models that have been developed especially for the game domain
	- GameFlow ([Sweetser & Wyeth, 2005](https://dl.acm.org/doi/abs/10.1145/1077246.1077253?casa_token=9qppgWYml5EAAAAA:Zj_BY8DUqiGDJqIKg_aL0AhFix3kJZASuE-jOlfAhdvm0Rp_DpvAXKRbp4Eva520wC7KCiORrieU)) :
		- Applied the concept of flow to gaming in order to explain enjoyment in games. Their concept of GameFlow consists of eight elements: concentration, challenge, skills, control, clear goals, feedback, immersion, and social interaction
	- Fun of gaming (FUGA, [Poels et al., 2013](https://research.tue.nl/en/publications/the-game-experience-questionnaire)) :
		- Seven factors are specified as follows: Sensory and imaginative immersion, tension, competence, flow, negative affect, positive affect, and challenge
	- Core elements of game experience (CEGE, [Calvillo-Gámez et al., 2010](https://doi.org/10.1007%2F978-1-84882-963-3_4)) :
		- Puppetry and video-game perception
	- PLAY heuristics ([Desurvire & Wiberg, 2009](https://link.springer.com/chapter/10.1007/978-3-642-02774-1_60)) :
		- Three categories: Gameplay, coolness/entertainment/humor/emotional immersion, and usability and game mechanics
- To combine the results, the following (social-)psychological elements of player experience are reported:
	- Competence
	- Autonomy and control
	- Immersion, (spatial and social) presence, flow, and GameFlow
	- Involvement and (enduring) engagement
	- Social relatedness and social interaction
	- Challenge
	- Tension
	- Curiosity
	- Fantasy
	- Positive and negative emotions
	- Intrinsic goals
	- Feedback and evaluation
- Integrative models of player experience :
	- To conclude, the integrative models described in this section extend the view on player experience by adding important perspectives, e.g., social and (neuro-) physiological aspects. This knowledge contributes to the quality of serious games design. In particular, the relationship between different disciplinary perspectives has to be considered. Therefore, the development of serious games requires interdisciplinary teamwork. Considering the multi-faceted and interdisciplinary nature of player experience, the question arises as to how the different components and dimensions can be assessed. This issue will be addressed in the following section.
- Measuring PE :
	- Physio (EEG, EMG, EDA, HR)
	- Psycho (Persona, Players, Surveys and Questionnaires, Vb reports, Interviews, Thinking aloud)
	- Behavioral (Eye track, Game logs, RT & quality, Observation, Video recordings)
-  For example, designers interested in facilitating a desired player experience can:
	- Learn from prior games. Game creators can look at (and play) other games and learn from bad as well as good examples.
	- Read post mortems as often published by game studios in industry publications, learn from them, and use them to inspire a (re)design.
	- Examine academic papers from serious game projects in a university or research organization setting. These academic papers often describe detailed learnings when it comes to fostering player experience and what the authors would do differently in future game designs.
	- Learn from books on game design.

### [CHAPTER 10 : Wiemeyer et al., 2016 : Performance Assessment in Serious Games](https://link.springer.com/chapter/10.1007/978-3-319-40612-1_10)

**THM** : 

**Authors** : Josef Wiemeyer, Michael Kickmeier-Rust & Christina M. Steiner 

**Tags** : :serious-video-games: :assessment: :performance:

**Abstract** : In every digital game, players both act in and interact with the game. They use the options of game mechanics to achieve goals. For example, players move a controller to steer the motions of an avatar, or they press buttons to trigger certain actions. These actions and interactions lead to certain results like a successful finish of a quest, solving a problem, or increasing a score. Both the quality and results of actions and interactions are subsumed under the term performance. Assessment of player performance is required for several purposes, for example for in-game or online adaptation and for offline evaluation. This chapter addresses the issue of performance assessment in serious games. Performance is a complex concept comprising results and processes of actions and interactions of the players in and with the game. First, generic and domain-specific models of performance are introduced to illustrate the variety of approaches. Based on this knowledge, online and offline assessments of performance are discussed. Finally, the integration of online and offline performance assessment into the process of game adaptation is described.

**Notes**
-  

## [Dye et al., 2009 - Increasing Speed of Processing with Action Video Games](https://journals.sagepub.com/doi/10.1111/j.1467-8721.2009.01660.x)

**THM** : 

**Authors** : Matthew Dye, C. Shawn Green, Daphne Bavelier

**Tags** : :video-games: :processing-speed: :visual-attention: :impulsivity: :learning-transfer:

**Abstract** :In many everyday situations, speed is of the essence. However, fast decisions typically mean more mistakes. To this day, it remains unknown whether reac- tion times can be reduced with appropriate training, within one individual, across a range of tasks, and without compromising accuracy. Here we review evidence that the very act of playing action video games significantly reduces reaction times without sacrificing accuracy. Critically, this increase in speed is observed across various tasks beyond game situations. Video gaming may therefore provide an efficient training regimen to induce a general speeding of perceptual reaction times without decreases in accuracy of performance.

**Notes**
- Do AVG really improve RTs on a variety of tasks without a concomitant decrease in accuracy?
- Speed-accuracy trade-off
- A Brinley plot is a type of scatter plot with each data point reflecting the performance of two different groups on the same experimental condition – performance of one group is plotted on the X-axis and that of the other group on the Y-axis.
- For each expe condition, an average RT score for AVGP/NVGP was extracted.
- Does not matter the task, as long as same expe condition and AVGP vs NVGP
- Cohen's d [0.48;1.47] + no diff acc (92.76% vs 92.75% across all conditions) + straight line
- Cross-sectionnal : 89 different expe condition from 9 different types of tasks : Spatial Cuing, Inhibition of Return, Simon Task, ANT - adults, ANT - children, Flanker Interference, Visual Search, n-back, PI task
- VGP responded 11% faster than NVGPs (VGP RTs = .89 x NVGP RTs, R^2 = .98)
- VGP accuracy = .99 x NVGP accurarcy, R^2 = .92
- Intervention : 4 tasks (Motion Discrimination, Task Switch, Visual Search (Gabor), Vis Search (Letters))
- AVGP Posttest RT = 0.87 Pretest RT
- NVGP Posttest RT = 0.94% Pretest RT
- TOVA : RT : IC Cohen's d = 1.03, SA Cohen's d = 1.19; ACC ns
- Counter decreases in the speed o finformation processing in elderly
- Most of the games found to enhance perf are unsuitable for children in terms of their content and difficult for elderly gamers in terms of the dexterity of response and visual acuity required. Identifying which aspects of the games are relevant will allow the dev of games that have a wide range of suitability and accessibility that can be used in clinical as well as educational applications.

**ToDo**
- RT/Acc * AVGP/NVGP
- AVGP/NVGP on the same expe condition
- For RT, can report cohen's d + tell if difference in accuracy
- See Fig. 1 : NVGP RT x AVGP RT, 9 types of task
- Fig. 2 : A. AVGP Pretest RT x AVGP Posttest RT; B. NVGP Pretest RT x NVGP PosttestRT

## [Ergolab, 2005 : L'analyse concurrentielle en ergonomie]()

**THM** : 

**Tags** : :ergonomie: 

**Notes**
- Utilisée dans les premières étapes du cycle de conception, en amont de toute réalisation et conception fonctionnelle d'une produit informatique
- Centrée sur les fonctionnalités offertes par les applications ou sites étudiés
- Extraire :
	- Les points +/- ainsi que les conventions d'interface du domaine d'un site ou app comparable
	- Les bonnes idées perçues pour s'en inspirer lors de la conception du site centré user
- Les résultats ont des implications non négligeables sur la stratégie
- "Existing, perhaps competing, products, are pften the best prototypes we can get of our own products" (Nielsen J., inspiré de Byrne, 1989)
- Enjeux :
	- **Comprendre le contexte concurrentiel dans lequel le projet s'inscrit :**
		- > Quels concurrents ?
		- > Comment ielles s'y prennent pour satisfaire les objectifs users ?
		- > Quels sont les services proposés, fonctionnalités et contenus disponibles ?
		- > Comment sont-ils présentés à l'internaute ?
	- **Cerner les standards existants pour des éléments d'interface précis :**
		- Standard = choix d'interface répétitif, comment est présenté l'objet, le produit, l'action
		- Comportements, réflexes et raisonnements des users
		- Eviter une totale découverte de notre siteweb
		- Préférable standard que solution meilleure en termes d'utilisabilité
		- > Comment l'internaute atteint un objectif donné, par quelles étapes doit-il passer ? Sont-elles identiques au travers du panel des sites analysés ? Quels sont les points communs ?
		- > Comment sosnt envirsages les principes de navigation ?
		- > Quelles sont les terminologies utilisées ?
		- > Quels types de feedback sont fournis à l'user ? (ex. confirmation email)
- Evaluation de l'utilité des contenus ou fonctionnalités et leur utilisabilité générale/détaillée
- Méthode :
	- Conduire des tests utilisateurs (mais limitation sur produits à étudier)
	- Conception de personas, pour avoir en tête les objectifs users et de travailler sur les sites avec ces derniers en tête
- Possibilité de focalisation seulement sur une fonctionnalité d'un site, mais ne pas aller dans le trop détail car on ne pourra pas reproduire les mêmes choix d'interface.
- Plannification :
	- Type de concurrents, nombre, nature
	- Par 'concurrent' il s'agit d'une interface présentant des fonctionnalités / pages semblables à l'app voulue
	- Quels sont les sous-buts nécessaires pour atteindre un objectif global ?
	- Dégager la liste des sites à étudier ainsi que les points à étudier sur chacun de ces sites
	- Permet de déterminer une liste des interface à analyser
- Conduire l'AC :
	- Etablir un ensemble d'actions qu'on va produire sur chaque site/app - basés sur l'analyse du contexte d'utilisation
	- Construire des scenarii d'utilisation à répéter sur chacun des sites
- Exploiter les résultats d'analyse :
	1. L'analyse doit être restituée à un client ou a une équipe projet
	2. Ne doit servir qu'à appuyer la conception
	- Cette analyse doit être suivie de spécifications sur la marche à suivre pour ne pas reproduire des erreurs ou exploiter de bonnes idées
- Avantages :
	- Rapport qualité/prix
	- Démarche rapide
	- Connaître force/faiblesses des concurrents, leur manière de satisfaire les objectifs user, les meilleurs choix d'interface, les erreurs à ne pas reproduire
	- Intégration assez facile des conventions web d'un domaine pointu
	- Stratégie et marketing viennent en plus que l'ergonomie pure en termes d'utilité des services proposés, public cible, le ton employé
- Inconvénients :
	- Pas d'intervention des users réels
	- Les résultats doivent être validés suite aux actions de conception détaillée, afin de s'assurer de la qualité ergonomique des choix effectués

## [(YT) Erhel – Comprendre les déterminants des usages problématiques d’Internet](https://www.youtube.com/watch?v=ueXvYKmH0pg&list=PLlZdvmQB3mW5U8ZvngNQxvh57Eb-9aAB3&index=9)

**Authors** : Séverine Erhel

**Tags** : :internet-problematic-use:

**Notes**
- Pas d'addiction mais trouble du JV en ligne
- L'usage problématique d'internet réfère à un trouble comportemental reposant sur l'usage compulsif et excessif d'internet qui interfère avec les activités de la vie quotidienne (Caplan, 2007)
- Les pratiques et activités qui ont conséquences délétères et pas intenret en lui-même.
- Mécanisme :
	- Coping dysfonctionnel, centré sur l'évitement (distraction, évitement social) – dans UPI, stratégie d'évitement pour réduire attentes, émotions, stress, ou stress lié à troubles mentaux plus complexes
	- Gérer le stress par la distraction
- Modèle Interaction of Person-Affect-Cognition-Execution (Brand et al)
- Le contrôle inhibiteur interviendrait pour modérer les associations entre les réponses affectives et cognitives aux déclencheurs et les décisions de s'engager dans des ocmportements addictifs
- Flow est un prédicteur de la performance, conscience de soi diminué et limiter les émotions négatives (anxiété, projection dans le futur)
- Dark Flow : La fluidité de l'UX et exploration des biais cog, algo, ml
- FoMO associé fort à UPI, anxiété sociale et besoin de relation sociale insatisfaits expliqueraient ces effets
- Anxiété état bon prédicteur
- Frustration des besoins de la relation social prédicteur modéré
- Coping d'écitement prédicteur modéré
- Solutions :
	- Politiques de prévention centrées sur les vulnérabilité psychologique VS dose, substance : donc pas addiction + pas mêmes mécanismes neuronaux
	- Approche de la métacognition pour l'autodéfense face aux designs prédateurs

## [Ermi & Mäyrä, 2011 : Fundamental Components of the Gameplay Experience: Analysing Immersion](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=281919e199e285c02bf92aab07d3d637d3c0b060)

**THM** : Sensory Challenge Imaginative (Immersion) model

**Authors** : Laura Ermi, Frans Mäyrä

**Tags** : :gameplay: :experience: :immersion:

**Abstract** : This paper presents a gameplay experience model, assesses its potential as a tool for research and presents some directions for future work. The presented model was born from observations among game-playing children and their non-player parents, which directed us to have a closer look at the complex nature of gameplay experience. Our research led into a heuristic gameplay experience model that identifies some of the key components and processes that are relevant in the experience of gameplay, with a particular focus on immersion. The model includes three components: sensory, challenge-based and imaginative immersion (SCI-model). The classification was assessed with self-evaluation questionnaires filled in by informants who played different popular games. It was found that the gameplay experiences related to these games did indeed differ as expected in terms of the identified three immersion components.

**Notes**
- Yet, the essence of a game is rooted in its interactive nature, and there is no game without a player.
- Playing a game (...) starts having an effect on cultural and social, as well as artistic and commercial realities.
- What is the act of playing > Who is the player > What is its experience of gameplay > What the game is ?
- Not just engage in ready-made gameplay but actively take part in the construction of these experiences: bringing their desires, anticipations, previous experiences, interpreting and reflecting the experience in that light.
- Experiences are largely context dependent
- Social context is central in gameplay experiences
- Gameplay : combination of pace and cog effort required by the game ([Crawford, 1984](https://www.digitpress.com/library/books/book_art_of_computer_game_design.pdf))
- **Connection** : absorption (direction attention to an experience brought to mind) to immersion (physically or virtually a part of the experience itself)
- **Participation** : active to passive
- Entertainment (abs + pas) / Educational (abs + act) / Aesthetic (imm + pas) / Escapist (imm + act)
- To create immersion : 1) match the user expectations, meaningful things to do for the player and a consistent fame world (McMahan, 2003)
- Attractions of Digital Games :
	- Audiovisual quality and style (graphics, well-functioning camera angles, but different perceptions)
	- Level of Challenge (succeed, advance, uncertainty of the final outcome; sensorimotor abilities, cognitive challenges)
	- Imaginary world and fantasy (game char, worlds, storylines)
- A Gameplay experience model, the SCI-model :
	- Sensory immersion : audiovisual execution of games
	- Challenge-based immersion : balance of challenges (motor/mental skills) and abilities
	- Imaginative immersion : absorption with the stories and the world and use her immagination, empathise with char, or enjoy the fantasy)

## [Fischer et al., 2004 : Meta-Design a Manifesto for End-User Development](https://doi.org/10.1145/1015864.1015884)

**THM** : MD seeks to empower end-users by giving them the tools and opportunities to act on the product as soon as possible while being part of the dev team on equal terms with every other actor 

**Authors** : G. Fischer, E. Giaccardi, Y. Ye, A. G. Sutcliffe , N. Mehandjiev

**Tags** : :meta-design: :end-user-development:

**Notes**
- "The future success of End-User Development depends on creating tools that end users are motivated to learn and use in daily work practices.
- EUD ideal : high scope/low cost learning -> providing graphical worlds to create programmable agents that still impose a learning burden of instructing agents with condition-action rules, and designing agent models.
- User motivation should be encouraged during the early stages of adoption by management support, training, and task forces to spread best practice and expertise.
- Constructive support and training fosters success, encourages responsibility and enables management to control by leadership.
- In a culture of high end-user motivation and low managerial influence, success is simply a matter of users taking development into their own hands => *a culture of cooperation shares the responsibility for developing accurate and effective solutions.*
- **Meta-design** characteirzes objectives, techniques, and processes for creating new media and environments allowing "owners of problems"/end-users to act as designers.
    - Fundamental objective : to create socio-technical environments that empower users to engage actively in the continuous dev of systems rather than being restricted to the use of existing systems
    - *Design time and use time* : orignally system dev create the environments and tools, users find them unfit, thus requiring modification.
        - *Underdesign* is the meta-design approach : defining activity to create design space for others, it aims to provide social and technical instruments for the owners of problems to create the solution themselves at use time.
    - MD include users in an ongoing development process as co-designers thoughout the entire existence of the system.
    - MD provides users with opportunities, tools and social structures to extend the system to fit their needs.
    - MD transcends user-centered and participatory design by changing the processes by which systems and content are designed
        - *Shifting control* from designers to users to empower them to create and contribute their own visions and objectves.
        - *Designing the design process* is promoted to a 1st-class activity = technical and social conditions are as important as creating the artifact itself => enables conditions for collaborative design in which all participants incrementally acquire ownership of problems and contribute actively to their solutions.
    - **Seeding, Evolutionary growth and Reseeding (SER) process model** : 
        - Encourage designers to coneptualize their activity as meta-design, thereby supporting users as designers rather than restricting them to passive consumers
        - *Social creativity* : all stakeholders reach a shared understanding by contributing their different POV and knowledge
        - *Open source* : designers provide a seed that can be evolved by users at use time. Users are seen as co-designers by :
            - Actively soliciting and incorportaing user contributions,
            - Sharing the control over users by grnting full and direct access to source code,
            - Promoting mutual learning among community members through mailing lists,
            - Deliberately fostering a reward and recognition struture that motivates active participation by explicitly knowledging and promoting contributors
            - => Open source in MD have a lower cost for each user because the dev cost is distirbuted among a large number of pp and ind contributions are shared.
        - *Interactive art* : focuses on participation and collaboration as forms of co-creation, in which users become co-dev of artwork.
            - Interaction by giving the tools to the users, seen as the real object of creative production
            - MD environments not only allow users to create content, but also modify the behavior and components of the system at use time through interaction
- "MD is more than a technical problem; it must address the challenges of creating new mindsets, new sources of creativity, cultural changes, and innovative societies. It has the potential to create a culture in which all participants in collaborative design processes can express themselves and engage in personally meaningful activities."

## [Fleck et al., 2017 : “ Teegi, il est trop beau ”: Exemple d’évaluation du potentiel pédagogique d’une interface tangible interactive pour enfants en contexte scolaire](https://hal.archives-ouvertes.fr/hal-01578637)

**THM** : 15 min avec un TUI en classe permet de favoriser les apprentissage d'élèves de 8 à 11 ans

**Authors** :Stéphanie Fleck, Charlotte Baraudon, Jérémy Frey, Thibault Lainé, Martin Hachet

**Tags** : :tangible-interface: :child-computer-interaction: :usability:

**Abstract** : The cerebral activity is an intangible physiological process difficult to apprehend, especially for children. With the aim of providing a new type of educational support, we studied the pedagogical potential of an interactive tangible interface (Teegi) designed to be used in educational context. This interface aims at enabling children to discover the relation between the brain activity and the human body functions. We propose in this study a methodology to evaluate its pedagogical potential in real context of use, considering the specificities of the children. This study, carried out with 29 pupils, highlights the strengths of this system, both in terms of its usability and its impact on learning. Moreover, the results provided by this methodology revealed possible improvements for a greater pedagogical effectiveness. This type of interactive interface, as well as the evaluation method proposed, paves the way for the pedagogical use of new interactive and tangible devices at school.

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Interaction Humain-Machine pour enfant (initialisé par Papert, 1980)
- Interface tangibles (Tangible User Interface - TUI) :
	- Avantage : manipulation physiques et collaboratives
	- "L'apprentissage des fonctions physiologiques représente une réelle difficulté pour un enfant car non eprceptibles sensoriellement et donc intangibles. Les enfants n'ayant pas atteint le stade des opérations formelles (ttt des opérations abstraites), ils ont fortement besoin d'ancrages physiques, concrets, et visuels pour construire leurs connaissances"
- Evaluation en classe
- Teegi :
	- Fonction initiale = permettre aux users de visualiser leur propre activité cérébrale (cette dernière intangible, càd non perceptible visuellement
	- Permet d'expliquer des phénomènes liés au cerveau en fonctionnement
	- Si manipule ses mains, pieds et yeux, Teegi déclenche les visualisations au niveau de son cerveau.
- Une technologie numérique éducative doit être conçue en respectant :
	1. Utilisable et attractive
	2. Doit rendre effective les apprentissages visés
	3. Doit permettre à l'apprenant d'identifier les savoris visés
	4. Doit stimulier la motivation scolaire
- Challenges méthodo pour évaluation de l'efficacité pédagogique d'une technologie en contexte de classe :
	- Contrôle de nombreuses variables : =/= labo : mobiliser classes, contrôler l'effet maître, climat classe, hétérogénéité du public, ou institution comme rythme scolaires, programmes, règles déontologiques (accès au savoir équitable)
	- Démarche comparative : peu d'autres études et/ou conditions identiques
- Evaluation d'interfaces :
	- Rapid desirability testing : "Voilà Teegi comment tu le trouves?" recueil vb de toutes les premières perceptions et impression générées par l'esthétique - effet de halo et désirabilité qui est cherché
	- Questionnaire Attrakdiff : assess utilisabilité et attractivité - perception pragmatique, hédonique, attractivité globale
	- Recueil de représentations par le dessin + "Plus précisément, que se produit-il?" + "Que permet-il d'apprendre?"
- Results :
	- Appropriation rapide - Facile d'appropriation
	- Exploration de l'interface
	- Pas de temps d'apprentissage supplémentaire - Utilisable
	- Attitude positive et précautionneuse, pas d'exaspération ou lassitude
	- 15 min suffisant pour favorable à apprentissage

## [George et al., 2013 : Usages reflexifs des traves dans les environnements informatiques pour l'apprentissage humain](https://www.persee.fr/doc/intel_0769-4113_2013_num_59_1_1091)

**THM** : 

**Authors** : Sébastien George, Christine Michel & Magali Ollagnier-Beldame

**Tags** : :traces: :EIAH: :auto-régulation: :learning-analytics:

**Abstract** : Les situations d’apprentissage instrumentées informatiquement sont de plus en plus fréquentes. Dans les environnements informatiques pour l’apprentissage humain, les actions des utilisateurs peuvent être enregistrées par le système, ce qui rend possible l’utilisation de « traces d’interactions ». Ces traces sont utilisées par les systèmes adaptatifs pour la personnalisation des activités ou le conseil des apprenants. Elles peuvent aussi être utilisées pour faire des observations d’usage ou du suivi pédagogique. Mais les avancées de la recherche montrent que les traces peuvent aussi être exploitées pour soutenir des processus essentiels dans l’apprentissage humain : les processus réflexifs. 

**Notes**
- Utiliser les traces pour s'adapter à chaque apprenant
- 1960 : Enseignement Assisté par Ordinateur = systèmes adaptatifs (renforcement) et génératifs (cybernétique)
- 1980 : IA, ex. Systèmes Tuteurs Intelligents (STI ou ITS) reposent de manière très schématique sur une triple expertise : modèle du domaine, pédagogique, élève
	- Dans systèmes fermés (conçus pour un enseignement bien précis et les activités tracées bien délimitées), les traces sont utilisées pour modéliser les apprenants et notamment pour construire et mettre à jour un modèle de leurs connaissances sur un domaine spécifique
	- Hypothèse forte sous-jacente est qu'un raisonnement correct de l'élève devrait être proche de celui d'un expert du domaine, mais c'est rarement le cas.
- Dans les systèmes ouverts (proposants des activités ouvertes pour favoriser l'apprentissage par l'exploration, seul ou en groupe), les traces sont utilisées pour guider app. et aider les ens. dans la scénarisations des activités et le suivi des app
	- Fondé sur le paradigme de l'apprentissage par la découverte et la théorie de l'apprentissage constructiviste (Piaget, 1935)
	- Ex. systèmes micro-mondes (LOGO), environnements supports à l'apprentissage collaboratif (Computer Support for Collaborative Learning)
	- Traces sont plus riches mais plus complexes à gérer et analyser comme l'apprenant peut utiliser des ressources et outils externes difficile à tracer
- Les outils métacognitifs pour l'apprentissage possèdent 5 caractéristiques (Azevedo, 2007), dans les situations d'apprentissage utilisant des outils métacognitifs :
	1. Les app ont à prendre des décisions d'actions en fonctions d'objecitfs définis
	2. Le contexte d'app est pris en compte pour définir les stratégies pégagogiques
	3. L'environnement utilise une technolgoie qui amplifie le processus d'autorégulation en considérant l'activité cognitive, métacog, la motivation et le comportement
	4. L'environnement utilise une technologie pour soutenir le processus d'apprentissage par une régulation externe fournie par un humain (tuteur, pair, collaborateur) ou un agenti artificiel
	5. L'environnement utilise une technologie dans laquelle l'appenant peut réguler ses activités avant, durant ou aprèes l'app en utilisant des processus d'autorégulation
- EIAH réflexif = EIAH dont la technologie va amplifier le processus d'autorégulation pendant l'apprentissage
- Trois temps :
	- 1) La collecte des traces qui peut être automatique (ex. logs)
		- Selon  un modèle de collecte construit "a priori" qui détermine le contenu des traces; permet reproducitiblité MAIS limité sur le plan de sa "fidélité" à l'activité de l'user car ne reconstitue qu'une partie des interactions.
		- "Mixte" quand intègre le point de vue de l'user, en vue de "qualifier" l'activité : limite = infos de type verbal ouvert.
	- 2) La transformation des traces par le biais de filtrages  ou de calculs d'indicateurs pour extraire un certain type de données et proposer une certaine visualisation de ces données aux app et/ou aux ens
		- Transformation de type "calcul machine" : filtrages, recherche de motifs d'interaction, comparaisons
		- Transformation de type "présentation" : rend accessible les enregistrements aux users
	- 3) Des actions possibles sur les traces, commenter ses traces ou les partager à d'autres apprenants (ex. émotions)
		- Exploitation des traces par l'apprenant sans intention pédago définie et ttt sur ses traces selon ses objectifs personnels
- Soller et al. (2005) distingue trois catégories d'outils :
	- 1) Systèmes miroirs qui fournissent aux app. une visualisation des traces brutes de leur propre activité et de celle des autres app. qui font partie de leur groupe/classe
		- ex. D'outils d'*awareness* - Objectif : renvoyer à l'user une image de son comportement et d'inciter à réfléchir sur celui-ci pour changer d'attitude si considéré comme inapproprié
		- ex. *Replayer* - Objectif : faire prendre du recul à un app par rapport à son activité
		- Souvent utilisé par chercheurs (Dyke, 2009) ou ens (Mazza & Dimitrova, 2007)
		- Peu par app car fournir ces informations sur l'acctivité ou le comportement n'est pas toujours suffisant pour provoquer une analyse méta-cog de leur part.
	- 2) Systèmes métacognitifs qui accompagnent la visualisation des traces d'activité d'aide à l'interprétation
		- Processus métacognitifs = reconaissance, évaluation, réflexion (Pena et al., 2012), ces auteurs répertorient 7 types de régulations métacognitives :
			1. Monitoring (suivi)
			2. Reconnaissance - met en évidence des infos clés sur l'activité en comparant les information du suivi avec celles du modèle d'acitviité cognitive
			3. Evaluation - processus d'examination, les informations permettant de mesurer les progrès liés à l'acitviét cognitive sur l'acitvité de base
			4. Réflexion - analyse des évaluations pour juger de l'efficacité
			5. Conscience
			6. Ajustement
			7. Planification
		- ex. Tableaux de bord - Objectif : présenter synthétiquement  les infos utiles pour analyser une situation et prendre des décisions stratégiques.
	- 3) Systèmes de coaching qui offrent des conseils sur la base d'une analyse automatique des traces
		- Donnent à l'ordinateur une place plus important en lui conférant des tâches de diagnostics et de proposition de remédiation
		- Des systèmes conçus dans l'optique d'un partenanriat homme-machine (mixte) nous semble les plus adaptés pour une plus grande implication de l'user dans son apprentissage
		- ex. Tuteurs intelligents

## [(YT) GGJ – IP Mistakes too many indies make (and how to avoid them)](https://www.youtube.com/live/JVjVm0DthHA)

**THM** : Even when small, begin to write the creative process.

**Authors** : Maria Burns Ortiz

**Tags** : :ip: :gamedev: :startup:

**Notes**
- IP is what can be monetized
- 99% of VG is IP : Graphics, drawing, trademarks, design, patents, mechanics
- How are we going to protect it from the outset?
- Failure to think about IP ownership. Write something that confirms who owns what and what our rights are. Who owns what percentage of right to a game when you're first starting. Put that on paper, get that out of the way, to avoid fighting in the future about who owns what.
- You never discuss IP when it already has value.
- For new studios, working on the game, creating ideas, source code, graphics, scenario, must begin with paperworks.
- Be careful, especially when a former founder was indeed the creator of the main character.
- Write the contact with proper IP clauses + reflects the particular law of the state where it was actually created.
- Laws of IP are unified – so do not worry about territorial details, you have already a decent level of protection
- Document as much as possible, especially the creative process, to have a proof.
- The risk is very low if you created it all from scratch/by yourself.
- What's the best way to document? e.g., Source code: Github
- If you are reusing something that is 150 years old, you are 99% safe to reuse it.
- Copyrights no need to register vs. Trademarks, patents, industrial design register.
- If marketing (caps, etc.), be sure to register.
- Contracts for the copyright, who gave the name of the studio.
- Add a copyright notice and send it by email

## [(YT) GGJ – Getting IP Right: Tips for indie devs](https://www.youtube.com/live/JPudXFHwthQ)

**THM** : 

**Authors** : Maria Burns Ortiz

**Tags** : :ip: :gamedev: :startup:

**Notes**
- See IP as an initial setup, like GitHub.
- It is the very first thing to do, it becomes exponentially harder to come back.
- No lack of clarity, who owns it, and what can the company do with it?
- Those are my assets, that create value, and these are all created by our team – it came from us, originated from us.
- Hold back a little bit on annoucements to the public until you have sorted the records out. E.g., trademakrs or patents need to be filed first at least a provisional application, because you are publishing to the public, you must secure your IP rights to it.
- Make notes on what you have discussed, concerning ownership of the game.
- Founders agreements, you need to have those discussions.
- If funders, and you are not alligned, wait.
- Seek help from the beginning.
- Having a plan, a vision of the various phases is very important, e.g., documentation, relationships in vg, who's doing what and who wants what. A rought timetable or a list is always useful.
- Prepare for the moment where there can be a dispute
- Save records and early process of your creative thinking. E.g., brainstorming, who said what.
- Who owns IP when it comes to dev? It should be agreed on what is brought to the table and by whome.
- There are crucial aspects of your game that you can protect.
- Mickey Mouse is free now, >70 yo
- Be clear about the relationship with your employees, and what you expect parties to do, and consequences of non-compliance. E.g., you're working with us, we are paying you for your work, but this doesn't make an owner or co-owner of the game, nor gives you IP rights in the game.

## [Gregory et al., 2022 : An onboarding model for integrating newcomers into agile project teams](https://doi.org/10.1016/j.infsof.2021.106792)

**THM** : 

**Authors** : Peggy Gregory, Diane E. Strode, Helen Sharp, Leonor Barroca

**Tags** : :agile: :onboarding: 

**Abstract** : Context : A stable team is deemed optimal for agile software development project success; however, all teams change membership over time. Newcomers joining an agile project team must rapidly assimilate into the organisational and project environment. They must do this while learning how to contribute effectively and without seriously interrupting project progress. Objective : This paper addresses how newcomers integrate into an established agile project team and how agile practices assist with onboarding. Method : A single, qualitative case study approach was used, investigating a co-located agile project team in a large IT department who regularly onboard inexperienced newcomers. Analysis was abductive, consisting of inductive coding and theming using categories from an existing onboarding theory. Results : We describe the team's onboarding practices and adjustments and present an agile onboarding model that encompasses onboarding activities, individual adjustments, and workplace adjustments. Conclusions : A mixture of general and specific agile onboarding practices contribute to successful onboarding in an agile team. We provide practical guidelines to improve onboarding practice in agile teams. Our major new contribution is an extended model of onboarding for agile teams.

**Notes**
- Onboarding in agile sofware dev project teas : 

**Comments**


## [Hoseini et al., 2022 : The Effect of 2D and 3D Action Video Game Interventions on Executive Functions in Male Students](https://doi.org/10.1177/10468781221110577?link_credit=KennethRioja)

**THM** : AVG effects = improved inhibition (stroop), flexibility (WCST, only "3D group"), but no effect on continuous attention.

**Authors** : Faride Sadat Hoseini, Masood Khodadadi, and Alireza Khorambakht

**Tags** : :AVG: :training: :intervention: :inhibition: :flexibility: :continuous-attention:

**Abstract** : In recent years, interest in digital games has grown significantly and at the same time a range of games in 2D and 3D has been created. While 2D games cannot give depth to objects, 3D games creates spatial depth. The growth of the gaming industry has raised concerns among some psychologists. There is still much controversy about positive or negative effects of these games on cognitive performance. Executive functions are a set of cognitive abilities that play a decisive role in one’s purposeful behavior. The present study aims to investigate the effect of 2D and 3D video games on some of these functions—flexibility, inhibition, and continuous attention—in male students aged 18–26 years. Forty-five male students at Persian Gulf University participated in the study. They were matched based on intelligence score and experience of playing video games. The participants were divided into three groups of 15 (two experimental groups and one control group). Participants in the two experimental groups played 2D and 3D video games over 20 sessions. They completed the Wisconsin Card Sorting Test, continuous performance test, Stroop test, and Raven intelligence test. Data were analyzed via covariance and the results showed that playing 2D and 3D video games positively and significantly affects inhibition and playing 3D games has a significant positive effect on flexibility. Based on our findings, we discuss the different effects of 2D and 3D video games, in line with the study’s theoretical framework, and give suggestions for future studies.

**Notes**
- study focuses on effect of AVG on EF + compares 2D vs 3D (=creating depth) games
- VD : inhibition (stroop), flexibility (WCST), continuous attention (CPT)
- training : no NVGP, only even distribution of IQ and VG xp, males 18-26yo, 15/group, 20min/d, 5d/w, 4w 
- 2D group ("cannot give depth to object") vs. 3D group ("stereoscopic 3D games (...) creating spatial depth, so objects appear closer to the player and seem touchable")  vs. passive control group

**Comments**
- 2D (uncharted drake's fortune) and 3D (uncharted drake's deception) groups are trained on '3D' games...

## [Houry, 2024 – Utiliser l'IA en cours de mandarin](https://docs.google.com/presentation/d/1oFVxhC0B4MyWi7DQhpWrc9j0scDqLvqaAPiBhbrdOoE/edit#slide=id.g273b7d628a0_0_148)
- PROSE :
	- Public cible "tu t'adresses à"
	- Rôle "tu es"
	- Objectif "produis-moi"
	- Sujet "sur le thème de"
	- Explicitation "en utilisant, comme"

## [Johnson & Senges, 2010 : Learning to be a programmer in a complex organization: A case study on practice‐based learning during the onboarding process at Google](https://www.emerald.com/insight/content/doi/10.1108/13665621011028620/full/html#idm46216430258464)

**THM** : 

**Authors** : Maggie Johnson, Max Senges

**Tags** : :onboarding: :agile:

**Abstract** : Purpose : This paper seeks to analyse the effectiveness and impact of how Google currently trains its new software engineers (“Nooglers”) to become productive in the software engineering community. The research focuses on the institutions and support for practice‐based learning and cognitive apprenticeship in the Google environment.  Design/methodology/approach : The study uses a series of semi‐structured interviews with 24 Google stakeholders. These interviews are complemented by observations, document analysis, and review of existing survey and statistical data.  Findings : It is found that Google offers a state‐of‐the‐art onboarding program and benchmark qualities that provide legitimate peripheral participation. The research reveals how Google empowers programmers to “feel at home” using company coding practices, as well as maximizing peer‐learning and collaborative practices. These practices reduce isolation, enhance collegiality, and increase employee morale and job satisfaction.  Research limitations/implications : The case study describes the practices in one company.  Practical implications : The research documented in the paper can be used as a benchmark for other onboarding and practice‐based learning set‐ups.  Originality/value : This is the first research that gives insights into the practice‐based learning and onboarding practices at Google. The practices are assessed to be state‐of‐the‐art and the insights therefore relevant for benchmarking exercises of other companies.

**Notes**
- semi structured interviews, 5 freshmen 6 employeers <1.5yo, 4 mentors, 3 learning program managers @Montain View California
- six dimensions of organizational socialization tactics [Van Maanen & Schein, 1977 : Toward a Theory of Organizational Socialization](#van-maanen--schein-1977--toward-a-theory-of-organizational-socialization)
- Google's onboarding process = 
	1. recruiting and pre‐start preparation = "black-box"
	2. two‐week face‐to‐face training and orientation = sets expectations, explains HR practices, core tech and prog practices + senior engineers live lectures on Google practices and culture + "life of an engeneer" = "things change quickly" a positive attitude toward new systems and ways of doing things is key to feeling comfortable, "we are all researchers", "learning to find out" learn who to ask and what inquiry methods wand paths lead them to answers, "find role models" copy most productive and influential sof ingeneers available on the intranet + career ladder of the company 
	3. online training = checklists + codelabs (tutorials also useful for experienced engineers to refresh knowledge) + codewalks
	4. mentoring program and Noogler support community = at the end of the intro lecture on the first day is the first meeting bw new and mentor, showing office and facilities - mentors are trained to be sensitized to the needs and expectations of new hires (2 purposes : identify potential points of improvements & new engeneers can raise practical questions and clarify misunderstandings which might otherwise have become permanent issues) + mailing list "Noogler Engineers Helping Engineering Nooglers (NEHEN)" = one com channel where they can team up with peers  BUT not necessarily cohesive
	5. on‐the‐job training = "starter project", ~2 weeks and good intro to a specialized field; "Tech Talks" voluntary, self-organized knowledge sharing events in which one or several googlers or guests present about a technical topic of interest, recorded and made available online "good way to keep a sens of Google as a whole (...) good way to get a sense of how different teams see their mission, waht their plans are, and who their primary clients are"
	6. practice‐based learning = glossary (=ETW), dev handbook
- Transparent perf management system : 1. 'weekly snippets' = bullet point report about what they did during the last 7 days; "Objectives and Key Results" = set measurables objectives; Company CV = on the intranet, asked to dvp, allow newcomers to lean about their colleagues and role models; "Design and Code Reviews" = peer prod practices ([Benkler, 2006](https://www.jstor.org/stable/j.ctt1njknw)), like prereg, code reviews are required after each change to prodduction code, reviewers are team members working in the same area or senior dev who initially designed the system.
- "You need to take responsibility (...) Google trusts you to make the decision that is best for you and best for Google"
- "You need to take care of your career development" (Shillingsburg, 2004)
- [Boud and Middleton (2003)](https://www.emerald.com/insight/content/doi/10.1108/13665620310483895/full/html) :
	- Mastery of organizational processes : mental representation of who is who
	- Dealing with the atypical : pair working allowing the new comers to absorb the common as well as the un-common practices of the experienced programmer.
	- Negociating the politics
- The Gestell for practice-based learning :
	- All activities maintain intranet pages - allow for exploration and prudent gradual engagement by novices
	- Provide weekly progress reports + quarterly objectives : accessible company-wide and allow to shadow role models unobtrusively
	- Knowledge guilds (aka. intergrouplets) responsible for maintaining various standards of the respective prog practices - noogler can enter, lurk on their mailing list and contribute to discourse gradually
	- Liberty of choosing a project of choice in their 20% of time - allowing different knowledge domain and strategic career self-organization
	- Small teams
- Supporting cog apprenticeship tools :
	- Online identity : build a reputation to allow for social and professional match finding
	- Trainee program : follow more senior peers to participate in their meetings and project activities – realistics expectation is an aspect to be most important for job satisfation ([Buckley et al., 1998)](https://psycnet.apa.org/record/1998-02893-008))
	- Expectation management
	- Practice-based learning : Role models; Communities of practice

## [Jolicoeur-Martineau, 2025 : Less is More: Recursive Reasoning with Tiny Networks](https://arxiv.org/pdf/2510.04871)

**THM** : 

**Authors** : Alexia Jolicoeur-Martineau

**Tags** : :HRM: :LLM:

**Abstract** : Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and ARC-AGI while trained with small models (27M parameters) on small data (∼ 1000 examples). HRM holds great promise for solving hard problems with small networks, but it is not yet well understood and may be suboptimal. We propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach that achieves significantly higher generalization than HRM, while using a single tiny network with only 2 layers. With only 7M parameters, TRM obtains 45% test-accuracy on ARC-AGI1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters.

**Notes**
- Chain of Thoughts (Wei et al., 2022) seeks to emulate human reasoning by having the LLM to sample step-by-step reasoning traces prior to giving their answer. It improves Acc but expensive, and can be brittle.
- Test-Time Compute (Snell et al., 2024)
- Since 2019, human-level accuracy still has not been reached, Gemini 2.5 Pro Acc = 4.9% on ARC-AGI-2 (Chollet et al., 2025)
- HRM is a supervised learning model with two main novelties: 
	- Recursive hierarchical reasoning:
		- Recursing multiple times through two small networks (fL, at high freq, and fH at low freq) to predict the answer
		- Each of them generate a different latent feature: fL outputs zH, fH outputs zL.
		- Both are used as input to the two networks.
	- Deep supervision:
		- Consists of improving the answer through multiple supervision steps while carrying the two latent features as initialization for the improvement steps.
		- Seems to be the primary driver of the perf gains.
- Reasoning across different supervision steps is worth it, but the recursion done in each supervision step is not particularly important.
- Tiny Recursive Model, an improved and simplified approach using a much smaller tiny network with only 2 layers that achieves s* higher generalization than HRM on a variety of problems.
- Sudoku-extreme: .55 -> .87; maze-hard: .75 -> .85, ARC-AGI-1: .40 -> .45; ARC-AGI-2: .05 -> .08
- https://github.com/arcprize/ARC-AGI-2?tab=readme-ov-file
- The focus of HRM is supervised learning. Both input and output are assumed to have shape [B, L] B is batch-size, L is context-length

## [Jonassen, 2000 : Toward a design theory of problem solving](https://link.springer.com/article/10.1007/BF02300500)

**THM** : PS varies along 3 dimensions (type, representation and individual diff) + see typology of PS

**Authors** : David H. Jonassen

**Tags** : :design-theory: :problem-solving: :education:

**Abstract** : Problem solving is generally regarded as the most important cognitive activity in everyday and professional contexts. Most people are required to and rewarded for solving problems. However, learning to solve problems is too seldom required in formal educational settings, in part, because our understanding of its processes is limited. Instructional-design research and theory has devoted too little attention to the study of problem-solving processes. In this article, I describe differences among problems in terms of their structuredness, domain specificity (abstractness), and complexity. Then, I briefly describe a variety of individual differences (factors internal to the problem solver) that affect problem solving. Finally, I articulate a typology of problems, each type of which engages different cognitive, affective, and conative processes and therefore necessitates different instructional support. The purpose of this paper is to propose a metatheory of problem solving in order to initiate dialogue and research rather than offering a definitive answer regarding its processes.

**Notes**
- Why are we so inept at engaging learners in PS? A major reason, I argue, is that we do not understand the breadth of PS activities well enough to engage and support learners in them.
- Problems are not the same and so cannot be supported in the same way as component skills.
- Instructional designs for well-structured problems are rooted in information-processing theory, but instructional designs for ill-structured problems necessarily share assumption with constructivism and situated cognition. Information-processing theories conceive of learning outcomes as generalizables skills that can be applied across content domains, while constructivism and situation cog, argue for the domain specificity of any performance and therefore recommend embeding instruction in some authentic context (Jonassen & Land, 2000)
- First a problem is an unknown entity in some situation (the difference bw a goal state and a current state). Second, finding or solving for the unknown must have some social, cultural or intellectul value.
- Problem solving is "any goal-directed sequence of cognitive operations" (Anderson, 1980)
	- First, it requires a construction of a mental representation/model (= multimodal representations consisting of structural, procedural, reflective knowledge, images and metaphors of the system and executive or strategic knowledge (Jonassen & Henning, 1999) of the problem, known as the problem space (Newel & Simon, 1972). It is the mental construction of the problem space that is the most critical for problem solving.
	- Second PS requires some activity-based manipulation of the problem space.
- Experts are good problem solvers because they recognize different provlem states that invoke certain solutions (Sweller, 1988). If the type of problem is recognized, then little searching through the problem space is required. Novices, who do not possess well-developed problem schemas, are not able to recognize problem types, so they must rely on general problem solving strateies, such as the information processing approaches, which provide weak strategies for problem solutions (Mayer, 1992)
- *Problem Type/Variations* :
	- **Structuredness** :
		- Well-structured problems (e.g. in schools and universities) require the applications of a finite number of concepts, rules and principles being studied to a constrained problem situation, so-called transformation problems (Greeno, 1978). Well-structured problems :
			- Present all elements of the problem to the learners.
			- Require the application of a limited number of regular and well-structured rules and principles that are organized in predictive and prescriptive ways.
			- Have knowable, comprehensible solution where the relationship bw decision choices and all the problem states is known or probabilistic (Wood, 1983)
		- Ill-structure problems do not have predictable or convergent solutions and may require the integration of several content domains. They appear ill-structure because :
			- Possess problem elements that are unknown or not known with any degree of confidence (Wood, 1983)
			- Possess multiple solution, solution paths, or no solutions at all (Kitchner, 1983)
			- Possess multiple criteria for evaluating solutions, so there is uncertainty about which concepts, rules, and principles are necessary for the solution and how they are organized.
			- Often require learners to make judgments and express personal opinions or beliefs about the problem, so ill-structured problems are uniquely human interpersonal activities (Meacham & Emont, 1989)
	- **Complexity** :
		- Problem complexity is defined by the number of issues, functions, or variables involved in the problem; the degree of connectivity among those properties; the type of functional relationships among those properties; and the stability among the properties of the prblem over time (Funke, 1991)
		- The most complex problems are dynamic, that is, those in which the task environment and its factors change over time
		- Complex problems involve more cognitive operation than the simpler one (Kluwe, 1995)
	- **Domain specificity** :
		- The cognitive operations are learned through the development of pragmatic reasoning schemas rather than exercises in formal logic. Graduates in different domains develop reasoning skills through solving situated, ill-structured problems that require forms of logic that are domain-specific.
		- Ill-structured problems tend to be more situated, but well-structured problems tend ot rely more on general problem-solving skills, such as means-ends analysis. However, well-structured problems, in the form of story problems, can be quite situated while ill-structured problems, in the form of dilemnas, can be fairly abstract.
- *Problem Representations* :
	- An important function of designing for problem solving is deciding how to represent the problem to novice learners (...) instructional designers decide which problem components to include and how to represent them to the learner -> they assume responsibility for constructing the problem space for the learners.
	- What about the fidelity of the representation ? Represented in its natural complexity and modality of it is filtered ? Social pressures and time constraints be represented faithfully ? Resolution in real time or solved in leisure time ? What levels of coop or comp in the problem ?
- *Individual Differences* :
	- **Familiarity** :
		- Experienced problem solvers have better developed problem schemas, which can be employed more automatically (Sweller, 1988)
		- Routine problems (familiar) appear more well-structured to the experienced solver while transfer of nonroutine problems (not familiar, Mayer & Wittrock, 1996) require far transfer which is more effortful and conscious (Salomon & Perkins, 1989)
	- **Domain and Structural Knowledge** :
		- How much someone knows about a domain is important to understanding the problem and generating solutions.
		- The latter must be well integrated in order to support PS. The integratedness of domain knowledge is best described as structural knowledge (Jonassen et al., 1993) : the knowledge of how concepts within a domain are interrelated, also known as cognitive structure, the organization of relationships among concepts in memory (Shavelson, 1972)
	- **Cognitive Controls**
	- **Metacognition**
	- **Epistemological Beliefs**
	- **Affective and Conative** (attitudes and beliefs + motivational and volitional)
	- **General Problem-Solving Skills**
- Well-structure problem focus on correct, efficient solutions, while the ill-structured problems focus more on the decision, articulation and argumentation. Problems vary from logic problems and algo with exact solution to dilemnas with no verifiably correct solution.
- The role of context becomes vitally important in defining ill-structured problems, while well-structured problems de-emphasize the role of conttext.
- **Logical Problems** :
	- No transfer of solutions of these problems to formally identical problems (Hayes & Simon, 1977; Reed et al., 1974)
	- Logical problems have been the focus of considerable lab-based psych reserch. However, the usefulness of that research to instructional design is limited by the lack of ecological validity
- **Algorithmic Problems** :
	- Cooking recipes can be generated for nonmath forms of algo problems (McCloskey et al., 1985)
	- Suffer from the lack or absence of conceptual understanding of the objects of the algo and the procedures engaged.
- **Story/Word Problems** :
	- E.g. maths values embedded in a brief story
- **Rule-Using Problems** :
	- Find the most relevant information in the least amount of time.
	- It requires selecting search terms, constructing effective search args, implementing the search strat, and evaluating the utility and credibility of information found.
	- E.g., Chess or Pokemon game
- **Decision-Making Problems** :
	- Selecting a single option from a set of alternatives – based on a set of criteria –, each of which has one or more consequences.
	- Varies in complexity : Simple decisions with single solution with limited number of choices are more likely to be solved through some form of rational analysis VS in multistage, dynamic task environments, where conditions are changinf require provlem solver to make multiple decisions underrisk and uncertainty (+ can be made real time, Kerstholt & Raaijmakers, 1997)
- **Troubleshooting Problems** :
	- "dépannage", e.g. computer programmers debugging an inoperative computer
	- Using symptoms to generate and test hypotheses about different fault states.
	- Requires system knowledge (how the system works), procedural knowledge (how to perform problem solving procedures and test activities), and strategic knowledge (strategies such as search-and-replace, serial elimination and space splitting) (Pokorny et al., 1996)
	- Precursor, Action, Result, Interpretation method is effective for analyzing troubleshooting problems
- **Strategic Performance** :
	- Involves real-time, complex and integrated activity structures, where the performers use a number of tactics to meet a more complex and ill-structured strategy while maintaining situational awareness.
- **Case-Analysis Problems** :
	- Emerged at Harvard Law School nearly 130 year ago (Williams, 1992)
	- In this ill-structured problems, goals are vaguely defined; no contraints may be stated, little is known about how to solve the problem; no consensual agreement on what constitutes a good solution; and information available to the problem solver is prodigious but incomplete, inaccurate, or ambiguous (Voss et al., 1991)
	- Engage a prcess that includes goal elaborating, information collecting, hypothesis forming, forecasting (predicting effects), planning and decision making, monitoring the effects of ones' actions, and self-reflecting (Dörner & Wearing, 1995).
	- Are the most contextually bound king of problm, so analyzing cases places a great importance on situation analysis.
- **Design Problems** :
	- They have ambiguous specification of goals, no determined solution path, and the need to integrate multiple knowledge domains. (e.g., a house, a musical composition, an essay, product or system)
	- Requires applying general and domain-soecific schemas as well as procedural knowledge.
	- The importance of an artifact as evidence of problem solving and the lack of clear standards for evaluating solutions are what make design problems so ill-structured.
	- Designers must structure the problem by defining the nature of the artifact that will satisfy the ill-defined requirements. Because the criteria for acceptable solutions are not always obvious, designers must construct personalized systems for evaluating their products.
	- Despite their ill-structuredness, design problems may be the most important type of problem to investigate because so many professionals get paid for designing things (products, systems, etc.)
- **Dilemmas** :
	- The most ill-structured and unpredictable type because often there is no solution that is satisfying or acceptable to most people, and there are compromises implicit in every solution.
	- Social dilemma (resource management and pollution dilemmas) are perceived by individuals in terms of their own personal self-interests or in terms of the common good ([Schroeder, 1995](https://scholar.google.com/scholar?hl=fr&as_sdt=0%2C5&q=Schroeder%2C+D.A.+%281995%29.+An+introduction+to+social+dilemmas.+In+D.A.+Schroeder+%28Ed.%29%2C+Social+dilemmas%3A+Perspectives+on+individuals+and+groups+%28pp.+1-14%29.+Westport%2C+CT%3A+Praeger.+&btnG=))
	- The larger the group that is involved in any dilemma, the more likely that individuals will pursue their self-interests, because the cost to any one person of providing for the common good exceeds the benefits that the individual would receive ([Olson, 1965](https://scholar.google.com/scholar?hl=fr&as_sdt=0%2C5&q=Olson%2C+M.+%281965%29.+The+logic+of+collective+action.+Cambridge%2C+MA%3A+Harvard+University+Press.+&btnG=))
	- That does not mean that there are not many solutions, which can be attempted with varying degrees of success, however, none  will ever meet the needs of the majority of people or escape the prospects of catastrophe.
- Problems encountered in everyday and professional contexts often consist of combinations of problems.
- Metaproblems (complex combination of well-structured and ill-structured problems) consist also of the regulatory and reflexive skills to monitor and solve combinations of problems. (...) The metacognitive skills required to regulate the solution of metaproblems need to be identified and elaborated (emotional intelligence ??)
- Articulating a set of strategies to support problem space constructiokn is the focus of future research
- If instructional technologists are able to articulate each problem type, its representation, and the kinds of cognitive and affective processes that are required to solve each king of problem, the we should be able to project the kinds of individuals differences that are likley to interact with those processes, as well as models and methods of instructional design, to support learning of each type of problem.
- [Jonassen & Hung (2015)](https://www.researchgate.net/publication/273459514_All_problems_are_not_equal_Implications_of_problem_type_complexity_and_structuredness)
- https://research.com/u/david-h-jonassen : see other pubs

## [(YT) Kapur, 2022 : Productive Failure](https://www.youtube.com/watch?v=kC8O-2y5pyE)

**THM** : Activation, awareness, affect, assembly

**Authors** : Manu Kapur

**Tags** : :productive-failure: :learning:

**Notes**
- Experts see deep structures, Novice superficial, but it's the deep structure you need to see so you can undertsand something and that's the paradox for a novice : if you teach something new, they need some knowledge in order to see something you want to show them 
- 1) Be able to see new things : The first job of teaching is not to teach, the first job is to prepare the novice to see, then when she sees, you can show them and teach
- 2) Be able to play with the ideas in a way that helps you encode it flexibly and adaptively
- Productive failure = Do (experiment, test, fail) -> Explain
- Three layers of the PF design (Kapur & Bielaczyc, 2012) : 
	- The activity engaged in by participants : certain features / principles, design the data, the contrasts, the variants in a way it activates students knowledge both intuitively and formally but in a way they will not be able to solve the problem
	- > Participant structures used to engage with the problem : ind/grp, how do you facilitate ?
	- >> Social surround used ot frame the problem-solving context : the idea is for you not the get the correct answer but for you to invent, to generate, to improve upon each other's ideas
- Underlying mechanisms (4A) :
	1. Activation : we build new knowledge based on prior knowledge, but if it's not sufficiently activated you will not be able to use it to make sense of new incoming information, activate formal, ifnormal, intuitive knowledge
	2. Awareness : student if PF become aware of the limits of their own knowledge and the gap between their knowledge and their limits
	3. Affect : creates stiuational interest, students want to know, why did my idea not work, motivation goes up, it's also about persisting and struggling because students go in PF is not easy, it's frustrating. Not always true that positive emotions leads to learning, also frustration, shame are positively associated with deep learning
	4. Assembly : Prior knowledge activated, aware of their limits of current knowledge, aware that there's a gap that need to be filled, and reach an affective state where they want to learn, they want to bridge the gap. If at that time a teacher comes and assemble it all and puts it all together that what's make productive failure powerful.

## [Ladyman et al., 2013 : What is a complex system?](https://link.springer.com/article/10.1007/s13194-012-0056-8)

**THM** : Nonlinear, Feedback, Spontaneous order, Robustness, Emeergence, Hierarchical organisation, numerosity

**Authors** : James Ladyman, James Lambert & Karoline Wiesner

**Tags** : :complexity: :statistical-complexity: :information: :complex-system:

**Abstract** : Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work.

**Notes**
- complex : having many parts connected in ways that are difficult to understand (Cambridge Dictionary)
- Is complexity for all the sciences or whether complexity is domain-specific?
- Complexity :
	- A structure with variations (Goldenfeld and Kadanoff, 1999, p.87) -> what is structure and variations ?
	- One 1) whose evolution is very sensitive to initial conditions or to small perturbations, 2) in which the number of independent interacting components is large, or 3) in which there are multiple pathways by which the system can evolve. (Whitesides & Ismagilov, 1999, p.89) -> chaos and nonlinear or lot of components or possible histories ?
	- Difficult to understand and verify (...) determined by such factors as the number of components and the intricacy of the interfaces between them, the number and intricacy of conditional branches, the degree of nesting and the types of data structures (Wen et al., 1999, p.92)
	- Complexity theory indicates that large populations of units can self-organize into aggregations that generate pattern, store information, and engage in collective decision-making (Parrish & Edelstein-Keshet, 1999, p.99) -> computational notions
	- In natural landform patterns is a manifestation of two key characteristics. Natural patterns form from processes that are nonlinear, those that modify the properties of the environment in which they operate or that are strongly coupled; and natural patterns form in systems that are open, driven from equilibrium by the exchange of energy, momentum, material or information across their boundaries (Werner, 1999, p.102) -> nonlinearity but neither a necessary nor a sufficient condition for complexity
	- One in which there are multiple interactions between many different components (Rind, 1999, p.105)
	- Muliple elements adapting or reacting to the pattern these elements create (Brian Arthur, 1999, p.107) -> cannot be complex unless there are many components interacting within it but not sufficient
	- Inherently complicated or intricate; rarely completely deterministic; math models involve non-linear, ill-posed, or chaotic behavior; unexpected outcomes (emeergent behavior) (Foote, 2007, p.410) -> introduces the idea of emergence but too confused
	- Complexity starts when causality breaks down (Editorial, 2009)
- Properties of a complex system :
	- **Nonlinearity** :
		- "Linear thinking and the belief that the whole is only the sum of its parts are evidently obsolete (Maizer, 1994, p.1) -> taken back in gestaltism 
		- It is *not a necessary nor sufficient* condition for a complex system : ex. a single chaotic pendulum can be subject to non-lineaar dynamics but it is not a complex system
		- Complexity as the study of systems with many interdependent components and excludes low-dimensional dynamical systems (MacKay, 2008)
	- **Feedback** :
		- A part of a system received feedback when the way its neighbourss interact with it at a later time depends on how it interacts with them at an earlier time
		- It is *not sufficient* because the individuals need to be part of a large enough group to exhibit complexity (ex. birds flock, or individual ants)
		- Theory of causal graphs : a chain of causal arrows indicates no feedbacks while a graph with loops of causal arrows shows feedback
		- Order, organisation and control is distributed and locally generated (e.g. ants) rather than centrally produced (e.g. steam regulator)
		- e.g. birds adjusting its course depending on the proximity and bearing of the birds around it
	- **Spontaneous order** :
		- Order in a system's behavior arises from the aggregate of a very large number of uncoordinated interactions bw elements
		- Notions that are related : symmetry, organisation, periodicity, determinism, pattern
		- This notion is central because pure randomness is sufficient for no complexity AND total order is also incompatible with complexity
		- It *is a necessary condition* for a complex system
	- **Robustness and lack of central control** :
		- Stabble under perturbations of the system
		- A system may maintain its order in part by utilizing an error-correction mechanism
		- Seems to be *necessary but not sufficient* for complexity
		- Computational language : ability of a system to correct errors in its structure
	- **Emergence** :
		- Emergent objects, properties or processes exhibit 'downwards causation', e.g., crystal formation, ant colonies organisation, and more generally the way that levels of organisation in nature emerge from fundamental physics and physical parts of more complex systems
		- In all epistemological senses emergence *is necessary but not sufficient*
	- **Hierarchical organisation** :
		- The ultimate result of all the features of complex system above is an entity that is organised into a variety of levels of structure and properties that interact with the level above and below and exhibit lawlike and causal regularities, and various kinds of symmetry, order and periodic behaviour
	- **Numerosity** :
		- Only exists if the system consists of a large number of parts, and usually, only if they are engaged in many interactions
- **Shannon entropy** :
	- SUM n, i=1 (Pri * log2(1 / Pri)
	- Measure of the probability distribution over the messages only and is not concerned with the content of the messages.
	- Entropy is max when all outcomes are equally likely, anytime move away from equally likely outcomes or introduce predictability entropy diminishes => means that we can ask fewer questions to guess the outcome (is it X?) : bits = qualtitative measure of information = measure of surprise ([Khan Academy, 2014](https://www.youtube.com/watch?v=2s3aJfRr9gE)), #bounces = SUM(p*bounces)
	- Shannon entropy is higher the more uniform the probabilty distribution = all messages have the same probability
	- "Surprise" is inversely proportional to its probability
	- Only express properties of a total set of sequences under some distribution
- **Kolmogorov complexity** :
	- C(x) = min{length(p):U(p)=x}
	- The length of the shortest possible description ~ how much pattern we can find in some sequence ~ Kolmogorov complexity >= length of x (e.g., 10101101110110000101000101100001100100011110), it is uncomputable ([Spanning Tree, 2020](https://www.youtube.com/watch?v=0cHHKDAelCo))
	- The algorithmic complexity of a random string is the length of the string itself. Any repetition of digits or symmetry in the string will allow the program that outputs the string to be shorter than the string itself
	- A random sequence is maximally incompressible VS string of 0s is maximally compressible.
	- This algorithmic complexity does not allow for the difficulty of running the program and generating its output (running time or "time complexity")
- **Lempel-Ziv complexity** :
	- As the previous is uncomputable, this algorithm compresses a sequence by taking advantage of repetitions of a specified max len in the sequence (Cover & Thomas, 2006)
	- Good estimator of the Kolmogorov complexity for many practical purposes : len(program) / len(original sequence)
- A deterministic measure of complexity treats a completely rdm sequence of 0s and 1s as having maximal complexity.
- A statistical measure of complexity treats a completely rdm sequence as having minimal complexity
- Conditions for a good measure of complexity ([Lloyd, 2001](#lloyd-2001--measures-of-complexity-a-nonexhaustive-list)) :
	1. How hard is it to describe ?
	2. How hard is it to create ?
	3. What is its degree of organization ?
- The complexity of the data (the product) corresponds to the difficulty of description
- The complexity of the system corresponds to the difficulty of creation
- Logical depth :
	1. Complex objects lie somewhere between complete order and complete disorder
	2. Cannot be produced quickly, any adequate complexity measure should obey a slow growth law
	3. Its history is not only long, but non-trivial = the most plausible explanation for the object's origin is one that entails a lengthy computation/causal process
- A proper definition of the "depth" of an object is going to be a compromise between the program size and computation time
- Bennett's (1988) key idea is that long causal processes are usually necessary in order to produce complex or 'deep' objects; deep objects are produced by short processes only with very low probability
- Logical Depth is a string d,b deep [...] if and only if d is the least time needed by a b-incompressible program to print x (Li & Vitnyi, 2009) : ???
- ...
- Statistical Complexity :
	- For a system to have high SC, it must have a large number of causal states and hence a large amount of regularities.
	- SC awards zero complexity to random strings and high complexity to strings with many statistical dependencies
	- [Complexity Explorer (2019) - Introduction to Complexity: Guest Spotlight, Jim Crutchfield](https://www.youtube.com/watch?v=SAU43m7qhR8) :
		- Shannon : How much information there is in the current of probabilistics events
- The measure that best captures the qualitative notion of the order produced by complex systems is tha tof the Statistical Complexity
- A survey of measures of complexity and their criticism can be found in Grassberger (1989). In it the author concludes that no unified measure can be found nor should there be one since complexity is intricately linked to meaning which is subjective and unmeasurable. 
- SC does not measure complexity as such but rather order. But because it measures order in the presence of noise it can be used to measure the order produced by complex systems since these systems necessarily exhibit noise.
- Tentative definition of complexity which we consider as a physical account of complexity : A complex system is an ensemble of many elements which are interacting in a disordered way, resulting in robust organisation and memory.
- **Ensemble of many elements** :
	- Not only many but also similar in nature – prerequisite for the condition of interaction
	- For systems to be able to interact or communicate (broadest sense) with each other they have to be able to exchange energy or matter or information
	- Ensembles of similar elements at one level form a higher-leve structure which then interacts with other similar higher-level structure.
- **Interactions** :
	- Interaction canbe either exchange of energy, matter or information. The mediating mechanisme can be forces, collision or communication.
- **Disorder** :
	- A necessary condition for complexity simply because complex systems are precisely those whose order emerges from disorder rathen than being built into them
- **Robust order** :
	- Although the elements continue to interact in a disordered way, the overall patterns and structures are preserved.
	- A macroscopic level arises out of microscopic interaction and it is stable (on that time or length scale)
- **Memory** :
	- "A system remembers through the persistence of internal structure" (Holland, 1992)
	- Memory is a straightforward corollary of robust order.
- Complex systems, unlike a coin toss, must possess some memory, some record of their past (~ prior knowledge??)
- Local organisation and robustness appear to be related by this idea of memory; both memory and robustness involve stability over time, and for this stability we need some form of back-up or redundancy with which to correct errors. This may be provided when we have a system whose order is not centrally controlled.
- A core problem in complex systems research is to understand how a omplex structure can have evolved out of a simple one.
- The hierarchical nature of complex systems is explained by the efficiency and stability of hierarchical building processes.

## [Lallemand et al., 2015 : Création et validation d’une version franc¸ aise du questionnaire AttrakDiff pour l’évaluation de l’expérience utilisateur des systèmes interactifs](https://doi.org/10.1016/j.erap.2015.08.002)

**THM** : French questionnaire to assess pragmatic, hedonic-identification, hedonic-stimulation and attractiveness aspects for user experience

**Authors** : Carine Lallemand, Vincent Koenig, Guillaume Gronier, Romain Martin

**Tags** : :UX: :HMI: :assessment: :user-experience:

**Abstract** : Introduction. – Alors que l’évaluation de l’expérience utilisateur (UX) est au cœur des préoccupations dans le domaine des interactions homme–machine, aucun outil d’évaluation auto-administré valide de l’UX n’existe actuellement en langue franc¸ aise. Le questionnaire AttrakDiff 2 (Hassenzahl, Burmester, & Koller, 2003) est un outil d’évaluation de l’UX qui repose sur un modèle théorique distinguant qualités pragmatiques et qualités hédoniques des systèmes interactifs. Objectif. – Cet article décritla traduction etla validation de la version franc¸ aise du questionnaire AttrakDiff 2 en vue de son utilisation sur des échantillons de population francophone. Méthode. – Suivant la méthodologie de validation transculturelle proposée par Vallerand (1989), le questionnaire a été traduit par des chercheurs trilingues puis a fait l’objet d’un processus de traduction renversée et d’une validation par un comité d’experts. Un prétest a été effectué sur 26 participants. Les caractéristiques de la version franc¸ aise de l’AttrakDiff 2 ont ensuite été évaluées par une étude quantitative en ligne sur un échantillon de 381 utilisateurs. Résultats. – Les résultats des analyses effectuées confirment la structure factorielle attendue en 3 facteurs et une bonne consistance interne des sous-échelles. Les liens entre les facteurs sont consistants avec le modèle théorique d’Hassenzahl (2003) où attributs pragmatiques et hédoniques perc¸ us se combinent pour former un jugement d’attractivité. Conclusion. – La présente version franc¸ aise de l’AttrakDiff 2 est globalement conforme à la version initiale allemande de l’outil et présente des niveaux de validité et de fidélité satisfaisants

**Notes**
- 4 échelles : aspects pragmatiques (utilité, utilisabilité, "do-goals"), hédonique identification (exprime leur "self" à travers les objets qu'ils possèdent et utilisent), hédonique stimulation (quête de connaissance et compétence), attractivité (appréciation globale découlant de la qualité perçue)
- La qualité pragmatique désigne principalement les aspects instrumentaux du système ou produit, càd son utilisté et son utilisabilité
- Critique : hédonique > pragmatique

## Lallemand & Gronier, 2016 : Méthodes de design UX. 30 méthodes fondamentales pour concevoir et évaluer les systèmes interactifs.

https://books.google.ch/books?hl=fr&lr=&id=6CJtDwAAQBAJ&oi=fnd&pg=PP1&dq=Lallemand,+C.+%26+Gronier,+G.[(2016).+M%C3%A9thodes+de+design+UX.+30+m%C3%A9thodes+fondamentales+pour+concevoir+et+%C3%A9valuer+les+syst%C3%A8mes+interactifs.+Paris+:+Eyrolles.&ots=EqAwKkWCtB&sig=rgJzXm9XPJj-ZUJBuh14Vm68jlI&redir_esc=y#v=onepage&q&f=false

**Authors** : Carine Lallmand, Guillaume Gronier

**Tags** : :UX: :design: :methods:

**Fiche 4 : Entretien**
- L'entretien permet d'explorer en *profondeur* et en *détail* les attitudes, opinions, préférences, représentations mentales
- Origines :
	- Blanchet et Gotman en 1992 démarche introspective
	- Rogers en 1942 avec entretien non directif : *empathie* (comprendre ce que l'autre ressent, éprouve), *acceptation inconditionnelle* (disponibilité de l'acceptation et intérêt), *non-directivité* (pas de modification, comportement ou valeur du pp)
- Techniques d'entretien :
	- **Laddering** (Reynolds & Gutman, 1988) : faire le lien entre les attributs, conséquences de ces derniers ou besoins associés d'un produit qui font sens pour l'utilisateur. Ladder -> "pourquoi", "pourquoi"... = évocation des attributs de plus en plus abstraits
	- **Incidents critiques** (Flanagan, 1954) : réfléchir à des expériences positives ou négatives lors d'un entretien structuré, pour identifier et corriger les problèmes et optimiser l'UX du système
	- **Entretien d'explication** (Vermersch, 1991) : faire "revivre mentalement" le contexte sensoriel, on se centre sur les actions précises réalisées.
	- **Auto-confrontation** (Mollo & Falzon, 2004) : commentaire et justification de ses actions visionnées sur une vidéo, une variante 'croisée' est réalisée par un pair
	- **Instruction au sosie** (Oddone et al., 1981) : donner à son sosie des instructions pour exécuter la tâche exactement comme il le ferait
	- **Repertory grid** (Kelly, 1955) : choix de 2 produits sur 3 présentés et préciser pourquoi ceux-ci sont semblables mais différent du troisième, puis doit nommer le contraste (beau/laid) jusqu'à avoir une liste d'attributs à noter de 1 à 5
- Pourquoi cette méthode ?
	- **Comprendre les users** : quoi, comment et dans quel contexte vont faire les users pour atteindre leurs objectifs ? + liberté d'expression de l'user & flexibilité aux concepteur·trices + environnement naturel
	- **Soutenir l'innovation par le recueil d'expériences** : on s'intéresse à la différence entre ce que font et disent les gens pour détecter les besoins cachés afin d'*inspirer* des conceptions *innovantes*.
- Avantages :
	- Pas de matériel, flexible, validation écologique; données recueillies source d'inspiration et d'innovation; entretien exploratoire peut amener à questionnaire à plus large échelle
- Désavantages :
	- Pas possible sur grand échantillon; chronophage; maîtriser technique pour avoir qualité; si pas sur terrain décontextualisation et basé sur souvenirs
- Mise en pratique :
	- Entretien **non-directif** (ou exploratoire) = explorer une thématique, parler librement, relancer l'interviewé·e pour aller en profondeur 
	- Entretien **semi-directif** = aborder des points choisis et relances
	- Entretien **directif** = structuré ~= questionnaire, réponses brèves sans relances
- Plannification :
	- Définition des objectifs :
		- Pourquoi ? Aspects à explorer ? Qui ? Quelles données ? Où et quand ?
		- "Et si je n'avais que 5 minutes, que lui demanderais-je ?" > réfléchir en 2 minutes, écrire thématique, refaire avec d'autres membres de l'équipe et comparer les résultats
	- Créer le guide d'entretien :
		- Questions, timing, ordonnées logiquement
		- "J'aimerais que vous me parliez de...", "Raccontez-moi votre expérience de...", "Que pensez-vous de..." puis déclinaisons sur le thème
	- Types de questions :
		- Questions de comportements : Que font-ils ?
		- Questions d'opinions (attitudes, croyances ou motivations) : Que pensent-ils ?
		- Questions de connaissances : Que savent-ils ?
		- Questions démographiques et factuelles : Qui sont-ils ?
		- Séquence : Journée/activités typiques ? en premier ? et ensuite ?
		- Expemple spécifique : Parlez-moi de... qu'aimez-vous au sujet de...
		- Quantité : Combien utilisent ... dans votre entreprise ?
		- Comparaison sociale : Est-ce que les autres ...
		- Projection dans le futur : Dans 10 ans, à quoi pensez-vous que cela va ressembler ?
		- Rétrospective du passé : Qu'est-ce qui a changé par rapport à avant ?
		- Vision idéale : Pourriez-vous décrire le système idéal pour vous ?
		- Canaux d'information
		- Opinion sur un sujet
		- Association de mots : Si je vous dis... à quoi pensez-vous ?
		- Complétion de phrases : Complétez spontanément la phrase suivante : ...
		- Création de liste : Pouvez-vous me faire une liste de ...
		- Jeu de rôle : Je vais jouer... et vous allez être... Montrez-moi comment il doit réagir
		- Tâches : Pouvez-vous me dessiner ...
		- Participation : Pouvez-vous me montrer comment je devrais faire pour ...
		- Démonstration : Pouvez-vous me montrer comment vous ...
	- Formuler ses questions de manière ouverte, respectueuse, accessibles et humaines, inciter à racconter histoires et anectodes
	- Activités supports : proposer des photos (photo-éliciation, par l'interviewer ou pp), de dessiner des cartes ou des courbes
	- Sélectionner et recruter les participants : users cibles, diversifier (non-users, extrêmes ou experts, anciens, loyaux aux concurrents ou qui détestent), une 10aine (Bellino et Colombi, 2004)
	- Organisation et logistique : préférable dans contexte naturel, à boire et à manger, language corporel
- Passation :
	- Structure de l'entretien (Goodman et al., 2012; Baxter et al., 2015) :
		- **Introduction** (5min) : se mettre à l'aise, consentement, permission d'enregistrer ou filmer
		- **Echauffement** (5-10min) : dire qu'on va parler de lui ex. question démographique centrée sur le participant
		- **Corps de l'entretien** (80%) : génériques > spécifiques
		- **Rétrospective** (5-10min) : retour sur génériques et sythèse de l'entretien avec le pp
		- **Débriefing et clôture** (3min) : qqch à ajouter ? Merci et clôture.
	- Attitudes de l'intervier·e : attitude neutre, empathie, écouter et ne pas modifier la pensée/cmportement/valeurs du pp. A EVITER : conseils, évaluation, aide ou réconfort, investigation, interprétation
	- Consignes : pour répondre aux objectifs, 
	- Relances : montrer l'intérêt et approfondir en rebondissant sur réponses
		- Silence, Clarification, Question sur le contenu, Question sur l'attitude, Echo, Reflet (donc selon vous...), Interprétation (en fait, vous craignez...), Complémentation, Résumé, Grommellement; Non-dit (Et ça vous attriste?)
		- Signaler quand change de thématique, recadrer quand dérive
	- Notes : heure avec idée ou thématique
- Analyse et interprétation des résultats
	- Retranscription des entretiens : retranscription intégrale, aussi ton, silences, soupirs, rires.
	- Analyse de contenu : regrouper les *patterns* verbaux, codé sur une base de catégories, compter les occurences
- Exploitation des résultats :
	- conception de personas, création d'*experience maps*, construction de scénarios d'usages ou de storyboards : synthétisation d'users types, leurs activités et besoins

**Fiche 7 : Questionnaire exploratoire**
- Fondements théoriques :
	- 1930-1960 : ère de l'invention, métier de statisticien apprait, études en entreprise ou aurprès citoyens
	- 1960-1990 : ère de l'expansion, par téléphone, grandes entreprises
	- 1990-now : ère des big data, gestion et ttt de grande qtt d'info
- Pourquoi cette méthode ?
	- Large audience / coût faible + Confirmer hypothèses + Communication des résultats statistiques de façon graphique
- Plannification :
	- Taille : pas plus de 25 questions, plus il est long moins le/la pp passera du temps / item
	- Code Dictionnary : Question - Référence - Type de question - Codage réponse

**Fiche 6 : Observation**
- Pourquoi cette méthode ?
	- **Observer activité réelle** (tâche, ce qui est à faire vs. activité, ce qui est fait *réellement*, pas influencé par user's interprétation) + **Analyser l'environnement** (connaître le contexte, un des 3 grands facteurs de l'UX) + Tester des hypothèses (*informed empathy* est au coeurs du processus de design UX)
- Techniques d'observation :
	- **Systèmes de tracking** : mini-caméras, observation indirecte
	- **Fly on the wall** : se fondre dans le contexte de l'user, non intrusive et interfère peu, observateur évite ainsi de l'influencer (Eyitope & Kamper, 2001)
	- **Shadowing** : toutefois observateur peut poser des questions au participant afin de mieux comprendre les raisons de certains comportements
	- **Contextual inquiry** : entre entretien et observation, démarche de conception participative. créer un dialogue entre observateur et observé (Holtzblatt & Jones, 1993)
	- **Undercover agent** : observateur déguisé et inteagit avec user, permet immersion pour entrer en contact avec le sujet sans se sentir observé
- Format :
	- Typologie des rôles de l'observateur (Gold, 1958) :
		- *obs. non-inférente* << Non participant (caché) -> Obs. complet (invisible) -> Obs.-participant (interaction limitée) -> Participant-obs (interagit avec situation, rôle connu) -> Participant complet (obs. est acteur, mais rôle inconnu) >> *obs. inférente*
- Planification :
	- Définir objet d'étude > Sélection pp > Définir lieu d'obs. > Définir durée d'obs. > Créer grille d'obs. (lieu, date, heure, durée, situation spécifique, éléments, actions, réactions, verbalisations, comportements)
- Passation :
	- Fiche ou grille d'obs; enregistrement AV / de l'entretien; logs; analyses des artéfacts
	- Eviter biais > rémède : grille d'observation ou plusieurs observateurs permet d'augmenter la fidélité des données :
		- Effet Hawthorne : présence de l'obs est perçue comme valorisante donc pp plus productifs > remède : voir sans être vu mais éthique et déontologique !
		- Effet de halo : impression dominante de l'observateur est appliquée à l'ensemble des observations
		- Effet de congruence ou de confirmation : observateur ne retient que ce qui va dans le sens des hypothèses ou attentes
		- Effet de la 1è impression : tendance à évaluer le cpt de l'ind en fonction de la 1è impression
		- Effet de générosité : tendance à n'attribuer à la personne observée que des caractéristiques positives
		- Effet de contraste : tendans à ne remaarque que ce qui est saillant par rapport à ses expériences antérieures
- Analyse et interpétation des résultats :
	- Rapport d'activité : Activité - Description - Fréquence - Durée cumulée
	- Graph temporel d'acitivté
- Observer plusieurs users avec profils opposés
- Se familiariser avec activité concernée (terminologie, outils, techniques) avant d'aller sur le terrain

**Fiche 13 : Personas**
- Processus de conception pour présenter et décrire les buts, besoins et caractéristiques de difféentes groupes d'users.
- Utile pour générer des idées et guider les décisions de conception
- First time seen dans conception de systèmes interactifs par Cooper 1999
- *Empathie et la théorie de l'esprit* : les personas facilitent la compréhension des attentes du futur user (Brangier et al., 2012) et l'anticipation de ses comportements (Pruitt & Grudin, 2003)
- *Théorie des stéréotypes* : simplification pragmatique et instanciée des users réel·le·s (Turner & Turner, 2011)
- *Emotion et créativité* : engendre chez concepteurs un état émotionnel positif, procpice à l'élargissement de l'esprit, à la curiosité et à la créativité (Norman, 2012)
- Facilite l'engagement des concepteur·trice·s dans le projet et favorisent une synergie positive au sein de l'équipe (Pruitt & Grudin, 2003)
- Offrent représentation imagée, concrète, tangible des users cibles, qui par définition restent théoriques.
- Peuvent être perçus comme trop abstraits, impersonnels ou trops axés sur les détails qui n'aident par le concepteur (Matthews & Judge, 2012)
- Garder le focus sur les users : méthode de synthpse sur users *réels* afin que les produits correspondent *réellement* à leurs besoins.
- Soutenir la génération d'idées en favorisant (Brangier et al., 2012) : nb d'idées plus important (fluidité), specte d'idées plus large (flexibilité), aide concepteurs à préciser idées et mieux élaborer (élaboration), lus de 50% des idées produites avec personna sont nouvelles (originalité)
- Convaincre :
	- Cette méthode permet à l'équipe de changer de perspective et de rester focalisée sur les users cibles,afin de mieux répondre à leurs besoins et de créer une UX positive et engageante
	- Personas permettent de communiquer avec l'équipe et de construire une vision partagée des users. Ils sont basés sur une synthèse des données recueillies lors de la phase d'exploration des besoins
	- Les personas permettent de générer des idées plus nombreses et cératives pour des produits plus innovants
	- Utilité sur le long terme
	- Excellents médiateurs qui facilitent la com entre les membres d'une équipe
	- Servent de base à d'autres méthodes de conceptions dont les scénarios ou storyboards

**Fiche 20 : Tri de cartes**
- Conception participative pour concevoir l'architecture de l'information d'un système centrée sur l'user qui corresponde au modèle mental des users
- Un animateur définit les cartes et animent, regroupement des cartes par les pp selon leur modèle emntale et nomment les catégories créées
- Pendant génération prototype, ex. création menus
- Contenu du système sous forme de cartes, pp les trient, catégoriesent et nomment ces dernières
- Pourquoi cette méthode :
	- Extraire modèle mental des users : eval subjectives, catégorisation, les implique dans conception, bonne crédibilité car données objectives
	- Organiser les éléments d'un système : méthode de référence pour adaptation aux représentations des users finaux
	- Co-conception ludique : favoriser implication au sein du projet de conception
- Convriancre :
	- Méthode incontournable pour cocnevoir architecture de l'info adaptée aux users finaux, en se basant sur données précises et chiffrées
	- Activité ludique, et pp se sentiront adaptés dans co-conception
	- Aussi en ligne ou ordi qui facilite ttt des résultats
- + : modèles mentaux sur organisation et contenu, quantitative, arborescence facile à présentée, ludique
- - : traitement et analyse pas immédiat dans structure consensuelle, biais de reproduction sans exprimer LEUR logique, bcp de temps pour animer
- Tri de cartes ouvert : 1. regroupement selon catégories, 2. labellisent. Utile quand new
- Tri de cartes fermé : 1. présentation catégories, 2. répartition cartes dans cat. Utile quand système existant ou quand imposé par charte ou guide (ex. MacOS, Fichier et Edition)
- Tri de cartes inversé : 1. présentation d'architecture, 2. retrouver contenu classé dans un catégorie
- Préconisations [30:100] (Spencer, 2009), [60:80] (Barrère & Mazzone, 2012)
- Critères pré-passations :
	- Structure hiérarchique déjà présent ? Oui = fermé, Non = ouvert
	- Recrutement des pp facile ? Oui = papier, Non = informatique
	- Du temps pour passation et analyse ? Oui = individuel/papier, Non = groupe/informatique
- Bussolon et al. (2006) trouvent que performance identique quand papier vs informatique
- Cartes : pages de navigation ou produits; fonctionnalités

**Fiche 24 : Echelles UX**
- Outils standardisés pour recueil ressenti et xp user - questionnaires auto-reportés
- ! Scores a montrer aux managers ou clients
- ! Comparer plusieurs systèmes, version, ou différentes pop d'users grace a questionnaires std
- ! Economique temps et money
- Valides et fidèles
- [AttrakDiff](https://link.springer.com/chapter/10.1007/978-3-322-80058-9_19); [meCue](https://doi.org/10.1524/9783486781229); [UserExperienceQuestionnaire](https://link.springer.com/chapter/10.1007/978-3-540-89350-9_6)
- AttrakDiff : qualité pragmatique, qualité hédonique-stimulation, qualité hédonique-identité, attractivité globale
- Résultats : diagramme ou histogramme des valeurs moyennes de chaque dimension; diagramme des paires de mots; QP/QH

**Fiche 30 : Tests utilisateur·trice·s**
- Tester une amélioration continue : excellent outil diagnostique et d'aide à la formulation de recommandation - itératif
- Mise en situation réelle
- Combiner plusieurs méthodes
- ! Observer les users dans leurs interactions avec le système pour recommandations d'amélioration l'UX du système
- ! Inviter client a TUX pour confronter perceptions et éveiller intérêt.
- Tests en situation contrôlée :
	- *Usability lab* - isoler pp, salle de test séparée glace sans tain, avec expert·e observant.
	- Outils : Morae (Techsmith), Noldus, Ovo Logger, Silverback
- Tests en contexte naturel : sur le terrain, chez eux, travail, rue.
- Tests à distance : bcp users, synchrone, asynchrone
- Tests en mode guérilla (MacDonald & Atwood, 2013) : solliciter spontanément les users cibles dans un lieu public pour l'évaluation d'un système
- Trunk test : 1) imprime n'importe quelle page web, 2) tenir feuille à bout de bras en plissant les yeux pour ne pas voir détails, 3) trouve et entoure le plus rapidement possible 1. l'identité du site, 2. le nom de la page, 3. les sections et sous-sections, 4. les éléments de navigation locale, 5. les éléments qui indiquent où on se trouve sur le site, 6. une fonction de recherche
- Maquette = exploratoire & comparaison; Prototype fonctionnel = évaluation, comparaison; Prototype final = Validation & Comparaison; Produits sur le marché = Amélioration continue
- Définir les scénarios d'usage basés sur les buts principaux : 5-8 scénarios, présente par ordre de diff up : phrase courte, vous.
- Etablir une grille d'observation : activités et tâches réalisées, temps, succession, réussite et éléments de satisfation, échecs/erreurs/pannes, commentaires "think aloud", commentaires
- 5 pp (Nielsen, 2000) ou plus pour couvrir toutes les questions UX du sstème ou pour évaluer un système complexe
- Durée 90 min (accueil et instructions - 10'; réalisation scénarios d'usages - 30'; passations de questionnaires - 15'; entretien de débrief - 15'; remerciement dédommagement - 5')
- Results : N success/fail pour chaque scénario, N erreurs / scénar, N et type repeated errors, N actions vs minimal, temps moyen, contenu vbalisations, contenu debrief, scores questionnaires
- Rapid Iterative Testing and Evaluation : corrige dès 1ere passation les erreurs commises par 1er pp and so on. Mais capacité à formuler rapidement des recommandations pour améliorer systèmes. Until 5 pp OK

## [Lang et al. 2022 – Handbook of Learning Analytics (2nd ed.)](https://www.solaresearch.org/publications/hla-22/)

### [CHAPTER 1 : Lang et al. 2022 – What is learning analytics](https://doi.org/ 10.18608/hla22.001)

**THM** : A concern, opportunity, field of inquiry, a community

**Authors** : Charles Lang, Alyssa Friend Wise, Agathe Merceron, Dragan Gaševic, George Siemens

**Tags** : :learning-analytics: :data: :technology: :education: :field: :domain:

**Abstract** : Over the last ten years learning analytics (LA) has grown from a hypothetical future into a concrete field of inquiry and a global community of researchers and practitioners. Although the LA space may appear sprawling and complex, there are some clear through-lines that the new student or interested practitioner can use as entry points. Four of these are presented in this chapter, 1. LA as a concern or problem to be solved, 2. LA as an opportunity, 3. LA as field of inquiry and 4. the researchers and practitioners that make up the LA community. These four ways of understanding LA and its associated constructs, technologies, domains and history can hopefully provide a launch pad not only for the other chapters in this handbook but the world of LA in general. A world that, although large, is open to all who hold an interest in data and learning and the complexities that follow from the combination of the two

**Notes**
- LA Across 4 dimensions :
- A concern (préoccupation) :
	- Proliferation of data, they now also include the contexts and purposes for which data is collected (...) concerns : privavy, ethics, data ownership, equity, usability
- An opportunity :
	- La could remake edu systems and ameliorate ills such as inequality and access
- A field of inquiry (champ de recherche)
- A community

### [CHAPTER 2 : Gray & Berner, 2022 – A  Practitioner's Guide to Measurement in Learning Analytics - Decisions, Opportunities, and Challenges](https://www.solaresearch.org/publications/hla-22/hla22-chapter2/)

**THM** : identify learning construct of interest -> select measurement model -> implement in a data collection instrument

**Authors** : Geraldine Gray, Yoav Bergner

**Tags** : :learning-analytics: :measurement: :educational-data-sources: :latent.constructs: :sources-of-error: :explanatory-models-of-learning:

**Abstract** : What is our data measuring, why are we measuring it, and what can we infer from our measurements? These are key questions for models of learning, and the focus of this chapter. This chapter discusses the role of measurement in transitioning from predictive models of learning to models from which meaningful explanations about learning can be inferred. We consider how to associate latent constructs of learning with observable data from a variety of data sources relevant to learning contexts, illustrated with examples from recent LAK proceedings. We also review common sources of errors that arise with a variety of data collection instruments, and highlight the challenges and opportunities for progressing valid and reliable measurement of both learning itself and factors related to the learning process.

**Notes**
- Predictive or explanatory ?
- To determine whether data collected in a learning context is a reasonable Measure of a contruct of learning :
	1. Identify the learning construct of interest
	2. Select an appropritate measurement model for the construct
	3. Implementing the measurement model as a data collection instrument (validity, reliability)

### [CHAPTER 3 : Predictive Modelling in Teaching and Learning](https://doi.org/10.18608/hla22.003)

**THM** : List of methods (see below)

**Authors** : Christopher Brooks, Craig Thompson

**Tags** : :predictive-modelling: :machine-learning: :educational-data-mining: :feature-selection: :model-evaluation:

**Abstract** : This chapter describes the process, practice, and challenges of using predictive modelling in teaching and learning. In both the fields of Educational Data Mining (EDM) and Learning Analytics (LAK) predictive modelling has become a core practice of researchers, largely with a focus on predicting student success as operationalized by academic achievement. In this paper we aim to provide a general overview of considerations when performing and applying predictive modelling, the steps which an educational data scientist must consider when engaging in the process, and a brief overview of the most popular techniques in the field.

**Notes**
- To make inferences about uncertian future events
- Learning content management system :
	- http://www.d2l.com/
	- http://www.starfishsolutions.com/
	- http://www.ellucian.com/
	- http://www.blackboard.com/
- Specialized companies :
	- http://bluecanarydata.com/
	- http://www.civitaslearning.com/
- Explanatory modelling is a *post-hoc* and reflective activity aimed at testing an understanding of a phenomena.
- The predictive modelling workflow :
	- Problem identification :
		- Recurring need, large set of data
		- Sparse and noisy data present challenges when trying to create accurate predictive models
	- Data Collection :
		- [Open University Learning Analytics Dataset](https://analyse.kmi.open.ac.uk/open_dataset)
		- [MITx and HarvardX Dataverse](https://dataverse.harvard.edu/dataverse/mxhx)
	- Classification and Regression :
		- Classification algo used to redict categorical labels
		- Regression algo used to predict numeric labels
	- Feature Engeneering / Feature Selection :
		- Depending on the algo used to build a predictive model, it can be beneficial to examine the correlation bw features, and either remove highly correlated attributes (the multicollinearity problem in regression analyses), or apply a transformation to the features to eliminate the correlation
		- By determining an informative subset of features, one can reduce the computational complexity of the predictive model, reduce data storage and collection requirements, and aid in simplifiying predictive models for explanation
		- If NA, remove row or col OR replace missing val with a 'default' value, such as the mean of the known values OR fill missing values by finding similar records in dataset and copying the missing values from their records
		- Naïve Bayes classifier can make predictions even when some attributes are unknown
		- Nearest neighbour classifier relies on computing the distance bw two datapoints, and in some implementations the assumption is makde that the distance bw a known value and a missing value is the largest possible distance for that value
	- Methods for Building Predictive Models :
		- Linear Reg : predict a numeric label from a linear combination of features
		- Log Reg : predict the odds of two or more labels, allowing for categorical predictions
		- Nearest Neighbours Classifiers : use only the most similar data points in the training data set to determine the appropriate predicted labels for new data
		- Decision Trees (e.g., C4.5 algo) : repeate partitions of the data based on a series of single attribute 'tests'. Each test is algorithmically chosen to maximize the purity of the classifications in each partition
		- Naïve Bayes Classifiers : assume statistical independence of each of the features fiven in the classification, and provide probabilistic interpretations of classifications
		- Bayesian Networks : graphical models are often manually constructed an dprovide probabilistic interpretation of classifications
		- Support Vector Machines : make use of a high dimensional data projection in order to find a hyperplane of greated separation bw the various classes
		- Neural Networks : biologically inspired algorithms that propagate data input through a series of sparsely interconnected layers of computational nodes (neurons) to produce a label. While neural networks have been the subject of research for more than 70 years, the area has received renewed interest (and commercial success) due to the advances of Deep Learning
		- Ensemble Methods :use a voting pool of either homogeneous or heterogeneous classifiers. Two prominent techniques are bootstrap aggregating, in which several predictive models are built from random sub-samples of the data set, and boosting, in which successive predictive models are designed to account for the misclassifications of the prior models.
		- Weka package, freely available
		- Time is better spent reflecting on the fundamental assumptions of classifiers, or in tuning parameters of particular methods being employed.
	- Evaluating a model :
		- It is important to asses the model in the same manner as will be used in situ : to build the predictive model using data available from one year, and then construct a testing set consisting of data from the following year, instead of dividing dara from a single year into training and testing sets
- Preictive Analytics in Practice :
	- The intentions of an applied predictive model should be to enable better education outcomes for learners, not simply to measure existing education outcomes.
	- Andrew Ho : the predictive model is only one half of the prediction-and-response system, and it is importand for researchers and practitioners to keep this in mind :
		- In short, we want educational predictions to be wrong. If our predictive model can tell that a student is going to drop out, we want that to be true in the absence of intervention, but if the student does in fact drop out, then that should be seen as a failure of the system. A predictive model should be part of a predictionand-response system that a) makes predictions that would be accurate in the absence of a response and b) enables a response that renders the prediction incorrect. In a good predictionand-response system, all predictions would ultimately be negatively biased. [18, p. 36]

### [CHAPTER 4 : Cacophony of Networks in Learning Analytics](doi.org/10.18608/hla22.004)

**THM** : See notes, everthing is to TH

**Authors** : Oleksandra Poquet, Srecko Joksimovic

**Tags** : :online-networks: :digital-traces: :digital-learning: :network-modelling:

**Abstract** : Network analysis, a suite of techniques to quantify relations, is among core methods in learning analytics (LA). However, insights derived from the application of network analysis in LA have been disjointed and difficult to synthesize. We suggest that such is due to the naïve adoption of network analysis method into the methodologies of measuring and modelling interpersonal activity in digital learning. This chapter describes the diversity of empirical research using network analysis as a cacophony of network approaches. Focusing on LA studies that evaluate social behavior of individuals or model networks, the chapter exemplifies aspects of the analytical process that require rigor, justification, and alignment to overcome the cacophony of empirical findings. The chapter argues that the clarity of network definitions, hypotheses about network formation, and examination of the validity of individual-level measures are essential for coherent empirical insights and indicators. Future work should also make effort to model the temporal nature, multiplex ties, and dynamic interaction between the levels where interpersonal interactions unfold.

**Notes**
- SNA : To quantify relations
- Analsis of interpersonal activity in digital learning (learner-to-learner)
- Foundations of Network Studies in LA :
	- SNA quantifies patterns within the sets of interdependent relations. Research on social networks, where network ties represent self-reported relationships bw people, is widely used in LA, drawing on SNA insights about social structures, theories on how they evolve, and SNA techniques ([Wasserman & Faust, 1994](https://psycnet.apa.org/record/1995-97740-000), or for a review : [Winship, 1996](https://www.jstor.org/stable/2291756))
	- Centrality =? Enhanced access to resources and information
	- Haythornthwaite & De Laat ([2010](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bb57668cc00bf5be6fece58dca973ecd1f3ec0c9), [2012](https://www.researchgate.net/publication/316790667_Social_Network_Informed_Design_for_Learning_with_Educational_Technology)) proposed analytical questions that SNA can explore in learning setting, such as 'who learns from whom', 'what learners learn from each other, ' what kings of interaction happen between people who learn together', 'which directions do resources flow', 'how frequently do learning interactions happen' , ' how important are they for people involved' (p.354)
	- Complementing observed digital data with types of media used for interaction, self-reported instruments, and content of what was exchanged online with interaction trace data
	- Network of individual clickstream (maybe to see : [Amo Filva et al., 2019](https://www.sciencedirect.com/science/article/pii/S0167739X18313499), [Mubarak et al., 2020](https://onlinelibrary.wiley.com/doi/full/10.1002/cae.22328))
- Network studies in LA :
	- NA can derive node-level metrics, such as describing the position and a relative **importance** of a node (a person, a word, a web page in the cours or other) in a network.
	- NA ca reveal closely interconnected groups of nodes, or provide network-level metrics that describe the entirety of the network structure.
	- RQ :
		- What is the relationship between node characteristics, node positioning, and the outcomes of such a position
		- Why ties form, i.e. what mechanisms generate observed network structure,
		- How node attributes influcenc enetwork formation, as well as how network structure impacts node attributes.
	- How individual positioning captured through net centralities relates to performance and learning-related outcomes in a coàenrolment network ([Gardner & Brooks, 2018](https://doi.org/10.1145/3170358.3170373))
- Cacophony of Networks in LA :
	- The problem may begin when the metrics from NA are used to interpret indicators, constructs, or processes related to learning. In these instances, NA is no longer just a tool, but becomes a methodology with its own theoretical assumptions. Such assumptions include an understanding of what net represent, but these assumptions are often implicit within the research choices. Insufficient attention to the assumptions underlying research design can result in the naïva adoption of NA
	- To highlight areas of misalignment, we distinguish between :
		1. Using NA as a method to reduce high-dimensional data
		2. Using network analytical methodologies to undrstand socially shared communication and interpersonal activity in learning settings.
	- When NA is a methodology, network construction, metrics and ways of modelling, as well as metric interpretations are at risk of misalignment.
- NA as a method :
	- NA as a method summarizes relational data, without particulat theoretical meaning assigned to the metrics. The method quantifies relational patterns and identifies clusters based on the relaitons between the observations of interest. These relations are, at least in part, interdependent, and node-level metrics quantifying them are often non-normally distributed. In LA, nodes linked by relationships can be people, words, learning resources, types of learning behavior captured through clickstream data, topics in the cours, and similar. Applying network analysis techniques to these data can reduce its dimensionality and classify nodes. E.g., topics discussed in the course ([Joksimovic et al., 2015](https://dl.acm.org/doi/10.1145/2723576.2723609)), learning strategies can be detected from networks of learner-level clickstream data, where ties bw events represent co-occurrence  of learning actions ([Matcha et al.,](https://link.springer.com/chapter/10.1007/978-3-030-29736-7_39))
- NA as a methodology :
	- Network Construction Issues :
		- Decisions about network constructions should be theory-based and systematic and "... a network model should be viewed explicitely as yielding a network representation of something ([Brandes et al., 2013](https://scholar.archive.org/work/hr3tdk2t3bcendt2arabfkyzxi), p.2)
		- To align part of the network analysis methodology, analyzed phenomenon eeds to be theorized through literature, abstracted to the network concept, and represented in the netwoek data through theorized and systematic definition of ties and non-ties,nodes and network boundaties (for guidelines, see [Howison et al., 2011](https://aisel.aisnet.org/jais/vol12/iss12/2/))
	- Choosing and Interpreting Centrality Measures :
		- Centralities measures such as betweenness and closeness are distance-based, i.e. the formula takes into account the entire network structure. Betweenness centrality is derived from the number of shortest paths that go through each node. In SNA nodes with high betweenness can be interpreted as having privileged access to resources.
	- Comparing, Interpreting, and Modelling Networks :
		- A network structure can serve as group-level indicator caused by a specific pedagogical and technological setting (Chen et al. 2018, Ouyang & Scharber, 2017) or as a signal of desired outcomes, such as team's performance (Pilny et al., 2016). In such network-level studies, methodological flaws can easily occur :
			1. When researcher directly compare descriptive networks metrics from different settings
			2. When they use hypothesis from SNA theory to model how socio-technical netowrks in learning environments form
		- Comparing descriptive indicators across networks requires stat analyses that rely on the so-called null models that explian how socio-technical network form. Null models are random networks simulated using hypothesized generative rules, such as 'learners are likely to respond to those who interacted with them rearlier' or 'learners interact on a given day when the assignment was posted'. (...) Many different approaches exist to hoe null models can be generated, such as tie permutation, exponential random graph modelilng (ERGM), stochastic actor-oriented modelling, among other approaches to network reconstruction.
		- LA largely lacks validated null models that explain how networks form in digital learning environments.
		- First, given the diversity of social contexts examined in LA, it is likely that the processes generating ties between individuals in them are also different, and theories as to how the form are yet to be put forward. Second, stat modelling in LA has only recently statred to explicitly include temporal aspects of learner activity in socio-technical networks and overall participation levels at the node level ([Chen & Poquet, 2020](https://doi.org/10.1145/3375462.3375535))
- Future Research :
	- At minimum, a clear description of network models is needed, to enable further synthesis of insights andprevent naive transfer of interpretation from self-reported networks research into the network measures of online learner networks
	- A new generation of network studies is needed to use the potential of complex network modelling to integrate dynamic, relational, spatial, muti-level, and mutiplex nature of models of social learning with technology. For NA methodologies to deliver on the promise for rich insights and indicators to inform about learning, explicit modelling of socio-technical learning processes and better alignment of theory with the methodologies is needed.

### [CHAPTER 7 : Temporal Aspects of Learning Analytics - Grounding Analyses in Concepts of Time](doi.org/10.18608/hla22.007)

**THM** : Passage of time; Order of time

**Authors** : Inge Molenaar, Alyssa Friend Wise

**Tags** : :temporal-analysis: :sequential-analysis: :concepts-of-time: :metrics:

**Abstract** : This chapter represents an effort to lay out a common framework for the concepts of time to (a) support diverse researchers working on temporal aspects of learning analytics to communicate better, (b) facilitate an understanding of how different approaches to studying time in learning articulate and (c) map out the space of temporal analysis to reduce redundancy of efforts. We distinguish two concepts of time, namely the passage of time and order in time. Passage of time considers time as a continuous flow of events and order in time focuses on the organization among events. Within the passage of time we distinguish four metrics: position, duration, frequency and rate. Within order in time we discriminate between consistency, recurrent and non-recurrent change and irregular change. Metrics extracted to index passage of time can be used in many different statistical methods, whereas analysis of order in time commonly requires the usage of advanced analysis methods. For either, decisions about the level of granularity at which time is considered and segmentation of time into “windows” have important effects on analysis results. We argue that understanding the value of temporal concepts and implications for the related analysis, is foundational for closing the loop and advancing learning analytics design with temporal insights.

**Notes**
- Why Time Matters in LA :
	- The primary goal of learning analytics is to understand and optimize learning, a process that occurs over time
	- One of the main arguments made in LA research is that learning does not happen in an instant
	- Within LAR an important focus is on how learning evolvs over time ([Knight et al., 2017](https://doi.org/10.18608%2Fjla.2017.43.2))
	- The field has adapted a wide range of analytic techniques for this purpose from other fields : time series analysis ([Schmitz & Wiese, 2006](https://doi.org/10.1016%2Fj.cedpsych.2005.02.002)), lag-sequential analysis ([Jeong, 2003](https://doi.org/10.1207%2Fs15389286ajde1701_3)), Markov Modelling ([Soller & Steen, 2007]()https://www.semanticscholar.org/paper/Applications-of-Stochastic-Analyses-for-Learning-Soller-Stevens/318c1f89eee223a111ef519b41f198d82209de5e), statistical discourse analysis ([Chiu & Khoo, 2002](https://doi.org/10.1177%2F1046496405279309)), epistemic network analysis ([Schaffer et al., 2016](https://doi.org/10.18608%2Fjla.2016.33.3))
- A conceptual Framework for Considering Temporality :
	- `time-on-task` : considers the amount of time students spend working on a particular task ([Kovanovic et al., 2018](https://doi.org/10.1145%2F3170358.3170374))
	- Productive failure indicates that when students have a chance to wrestle with a problem, explanations given after tend to become more meaningful for understanding new concepts compared to receiving the explanation immediately ([Kapur, 2016]()https://doi.org/10.1080%2F00461520.2016.1155457)
	- When students spend enough time with others' discussion posts to read (rather than just scan) them, they are more likely to contribute high quality posts themselves ([Wise et al., 2014](https://doi.org/10.1007%2Fs11412-014-9192-9))
- Passage of time: Considering time as a continuous flow of events :
	- Position : [Kapur and Bielaczyc (2012)](https://doi.org/10.1080%2F10508406.2011.591717) showed that scaffolding interventions too early in the learning process are detrimental to the groups own exploration process, yet scaffolds too late in the larning process do not affect the group learning.
	- Duration, Frequency, Rate
- Order in time: Considering time as a relative arrangement of multple events
	- Consistency (1111), Recurrent regular change (1212), Non-Recurrent regular change (1122), Irregular change (1328)
- Temporal Analysis, Segmentation and Granularity :
	- Methods such as statistical discourse analysis, sequential lag analysis, t-pattern analysis, process mining, Markob modeling, latent transition analysis
	- Choices made about segmentation can have dramatic impacts on results and there for for clear justification the method used to determine time windows is important
- Closing the loop: temporal consequences for design :
	- [Chiu et al. (2014)](https://www.igi-global.com/gateway/chapter/89013) found that when students were asked to summarize an online discussion in the middle, tather than at the end of the conversation, it often led them to reach more advances phrases of knowledge construction.

### [CHAPTER 15: Game Learning Analytics](https://doi.org/10.18608/hla22.015)

**THM** : Agency, engagement, growth, social connection + see the references !

**Authors** : Emily Reardon, Vishesh Kumar, Glenda Revelle

**Tags** : :digital-games: :learning: :analytics: :agency: :engagement: :assessment: :collaborative-learning: :social-learning:

**Abstract** : Games are a pervasive cultural phenomenon with intriguing connections to learning, and the use of learning analytics can inform our understanding of learning in the context of games. In this chapter we identify four principles that are fundamental to both compelling gameplay and meaningful learning – agency, engagement, growth, and social connection. Agency in learners helps them grow and feel safe to fail, persist, and feel ownership of their learning. Engagement, both as great interest and active involvement, is essential to learning, and digital games can be very engaging. Growth involves increases in ability that are developed through effort, perseverance, trying alternative strategies, and seeking help from others. Social connection with other players both within and outside of games facilitates learning. We propose that these four principles serve as an entry point for understanding and conducting game learning analytics work. For each principle we provide examples of evidence-based approaches to the design, measurement, and analysis of learning in game-based contexts to guide thinking and work in the nascent field of game learning analytics. This chapter is intended to be useful not only to game learning analytics practitioners but also to people working in LA-adjacent domains, such as game design, classroom learning, data security, and educational policy. We suggest that designers, practitioners, educators, and learners could all benefit from the translation of academic GLA work into a form that is useful to this broader constituency

**Notes**
- Game Learning Analytics (GLA) is the application of LA methods to gain insight about learning in the context of digital gameplay.
- Game ([Salen & Zimmerman, 2003](https://pdfroom.com/books/rules-of-play-game-design-fundamentals/jb5qOn6egxQ)) : A system in which players engage in an artificial conglict, defined by rules, that results in a quantifiable outcome.
- Four Principles of Game-Based Learning :
	- These correspond roughly to the four "pillars of learning that [Hirsh-Pasek et al. (2015)](https://pubmed.ncbi.nlm.nih.gov/25985468/) derived from learning sciences research, but adapted them specifically to address game-based learning.
	- Agency :
		- Or "magic circle", supports agency by providing players a "psychosocial moratorium" a developmental concept introduced by Erikson (1956) and applied to VG contexts by Gee. Within the magic circle, players craft a perosnalized narrative in dialogue with the game's design, assume temporary power, experiment with identities or roles, and explore their sense of ethics and morals with reduced risk vs real-worls exploration os such ideas.
		- Help them grow, feel safe to fail, persist, and feel ownership of their learning.
		- Design for Agency :
			- Agency is closely tied to concepts of self-efficacy (Bandura, 1977; believe you can succeed, likely to engage and more effort invest; linked with staircase), active learning (monitor their undersanding, seeking out opportunities to experiment and explore, and applying whtat they discorver to shape their own knowledge) and meaningful learning (connecting new information with whtat you already know and with your relevant real-life personal experience ([Brown, 2014](https://psycnet.apa.org/record/2013-42812-000))
			- Human Centered Design (HCD) is based on undersanding the needs of users, involving users in iterative design, and adapting technology based on user feedback ([Teasley, 2019](https://www.emerald.com/insight/content/doi/10.1108/ILS-06-2018-0045/full/html))
			- Universal Design for Learning (UDL) provides recommendations and methods for including diverse learning needs, goals, and abilities to better support all learners ([Rose & Meyer, 2006]()https://eric.ed.gov/?id=ED515447) and improves learning processes for a diverse population of students ([Capp, 2017](https://www.tandfonline.com/doi/full/10.1080/13603116.2017.1325074))
		- Measurement and Analysis of Agency :
			- Player categorization / models : assessing player agency by recognizing learners' play styles and identifying foals of plans that players are pursuing, then eentually supporting their agency by adapting the game to suit a particular player's gameplay style.
			- Play styles ([Bartle, 1996](https://www.researchgate.net/publication/247190693_Hearts_clubs_diamonds_spades_Players_who_suit_MUDs)) : achievers, explorers, socialisers, killers then critiqued by [Hamari & Tuunanen (2014)](https://www.semanticscholar.org/paper/Player-Types%3A-A-Meta-synthesis-Hamari-Tuunanen/a4fd78a8713f353ac3994f420b45c91ac6c87612)
			- Identifying players who play by rapid guesswork versu those using slower strategic moves, which provides insight on players's prior domain knowledge ([Liu et al., 2013](https://www.semanticscholar.org/paper/Predicting-Player-Moves-in-an-Educational-Game%3A-A-Liu-Mandel/04b57aa064e14f824563c94ed11214f33627800a))
			- Procedural Content Generation is a productive way to practice data-driven adaptive game design : suit a player's experience and inferred preferences (e.g., scale enemy difficulty, puzzle complexity, etc.
			- Futre work should similarly adress isues of equity and justice, and bette raddress the raneg of players' physical and learning abilities.
	- Engagement :
		- Design for engagement : 
			- Intrinsic motivation is the most well-research construct and ties together many of these features
			- Challenge, curiosity, control and fantasy and interpersonal motivations like cooperation, competition, and recognition ([Malone & Lepper, 2005](https://www.researchgate.net/publication/238687478_Making_learning_fun_A_taxonomy_of_intrinsic_motivations_for_learning))
			- Praise for effort or progress is more likely to encourage a growth mindset than praise foe task perofrmance or ability, which has important implications for the workding of instructional and reward messaging in learning games.
		- Measurement and Analysis of Engagement :
			- Amount of gameplay, means and variance in gampley time, play time per session, n of sessions. Also flow.
			- Game progress by using heatmaps to identify where players proceed or get stuck
			- Clustering and state networkdiagrams can be used to measure engagement ([Owen, 2015](https://scholar.google.com/scholar?hl=fr&as_sdt=0%2C5&q=V.+Elizabeth+Owen.+%E2%80%9CUsing+learning+analystics+to+support+educational+game+development%3A+A+datadriven+design+apporach%E2%80%9D.+In%3A+American+Educational+Research+Association+%28AERA%29+annual+meeting%2C+Chicago%2C+IL.+2015&btnG=))
	- Growth :
		- Design for growth :
			- Games effectively promote learning when they intefrate cog engagement with playfulness. and when content engagement is linked to game action
			- DBR, Scaffolding, guided play.
			- With regard to failure a wrong answer is a learning moment. IF you don't get anything wrong, you aren't learning – your performanc eis evidence that you were already competent in the content before playing the game
		- Measurement and Analysis of Growth :
			- Bayesian networks can be used to identify learner's progress by analyzing gameplay data signifying player actions, successes and failures ([Shute et al., 2016](https://www.sciencedirect.com/science/article/pii/S0747563216303740?casa_token=NkYyDqnTDOYAAAAA:uDR0f3WSAEv0W-OZSDXW9PATw37SSXtLNLeAie-DKkBSkvXhSstq_C_hg9Uyy2bSqYELfBmnoA)). This work leverages the Stealth Assessment Framework ([Shute, 2011](https://myweb.fsu.edu/vshute/pdf/shute%20pres_h.pdf)), which involves developping learner models that describe what learners know, competence models that articulate the learning domain, and evidence models that map player game actions to learning. The SAF proposes a desing and analytic framework for embedding assessment activities in engaging game tasks, with the goal of blurring the distinction bw assessment and learning in gameplay
			- Cluster analysis of gameplay data can be used to identiy different learnning phases such as EXploration, TInkering, and REfinement (EXTIRE framework, [Berland et al., 2013](https://www.tandfonline.com/doi/abs/10.1080/10508406.2013.836655)) 
			- Hidden Markov Models (HMMs) can be used to identify productive and unproductive progress in gameplay ([Tissenbaum et al., 2016](https://eric.ed.gov/?id=ED592698)). To identify productive player actions (for instance, remembering successful approaches or trying out novel approaches) as well as unproductive player actions(like repeating the same successful approach with no change). Since productive actions indeicate learner growth and unproductive actions often correlate with visitors leaving the exhibit, identifying these patterns helps in understanding and supoprting growth of skills and performance
	- Social connection :
		- Measurement and Analsis of SC :
			- Involves recognizing dynamics such as role enactment, collaboration and cometition, collective and individual guidance, social sharing and parasocial connection.
- Opportunities :
	- Standardizing the assessment of efficacy in learning games is a prime opportunity in GLA (...) This work can build on existing standardization frameworks in VG industry ([El-Nasr et al., 2013](https://link.springer.com/book/10.1007/978-1-4471-4769-5)) and could also enable the evalution of learning across multiple games and contexts.
	- Inform far transfer to differnt activities when integrated with other school and activity data.
	- Evolve to be transparent, digestible, controlled by, and empowering for all involved participants – teachers, parents and in particular learners themselves.

## [Lankoski & Björk, 2015: Game Research Methods, an overview](https://books.google.ch/books?hl=fr&lr=&id=Pya9CQAAQBAJ&oi=fnd&pg=PR1&dq=game+research+methods&ots=K7oifipp_u&sig=pWEjyV4r5DdMaPESmZw08fQXpGw&redir_esc=y#v=onepage&q=game%20research%20methods&f=false)

### [CHAPTER 10. Landers & Bauer, 2015 : Quantitative methods and analyses for the study of players and their Behaviour, ](https://dl.acm.org/doi/abs/10.5555/2812774.2812788)

**THM** : Very good intro to quanti methods

**Authors** : RICHARD N. LANDERS AND KRISTINA N. BAUER

**Tags** : :quantitative: :player-analysis: :behavior-analysis: :game-analytics:

**Abstract** : Often, when studying games, researchers need to describe, explain, or predict human behaviour. The behaviour we're interested in may occur outside of a game--for example, does violent videogame play cause aggression in children? But the behaviour we're interested in may instead occur inside of a game--for example, does tweaking a particular game element increase player satisfaction? In both of these cases, we want to know the answer to what appears on its surface to be a straightforward question: given a particular situation of interest, what do people do? Unfortunately, people's behaviour is difficult to predict well because people vary to a high degree both interpersonally and across contexts. They have different hopes, dreams, ideas, experiences, skills, attitudes, abilities, and so on, all of which come together to influence their reactions to any particular situation. So how do we develop general rules for how people behave?

**Notes**
- Construct of interest : any unmeasurable char of a player or a game
- Since constructs are un measurable, we must operationalize constructs, and the goal of operationalization is to ensure the way that the construct is measured is as accurate a representation of the construct as possible. Operational definitions are precise, objective ways that we try to measure constructs.
- Relationshiops bw constructs are generally reffered to as theory, whereas relationships between operational definitiosn are referred to as hypotheses.
- When an independent-samples t-test is statistically significan, it means that if we were to assume there was no true difference between the experimental grops, it is highly improbable that the difference we found in our study would have occured.

### [CHAPTER 14. Wallner & Kriglstein, 2015 : An Introduction to Gameplay Data Vizualization](https://dl.acm.org/doi/abs/10.5555/2812774.2812792)

**THM** : Shows all kind of data visualization

**Authors** : Günter Wallner, Simone Kriglstein 

**Tags** : :game-analytics: :gameplay-data: :visualization:

**Abstract** : The prevalence of internet-enabled gaming devices enables game developers nowadays to remotely and unobtrusively monitor every aspect of a game, allowing them to accumulate large amounts of data of the player-game interaction over extended time periods. This data has become a viable source for developers to guide decision-making throughout the game design process and even after the game has been released, for example, to identify balancing issues (Kim, et al., 2008; Zoeller, 2010), to understand player movement (Moura, El-Nasr and Shaw, 2011), to reduce production costs (Hullett, et al., 2011), or to uncover bugs (Zoeller, 2010), to name but a few. At the same time, the increasing popularity of online multiplayer gaming has attached great importance to in-game statistics to allow players to recap their performance and to compare it with others. This growing interest in game telemetry data is reflected by the emergence of the new field of game analytics--concerned with the discovery and communication of meaningful patterns in data as applied in the context of game development and game research (cf. El-Nasr, Drachen and Canossa, 2013). Game analytics heavily relies on visualization techniques to assist developers and players alike to understand, analyze, and explore the data (cf. Wallner and Kriglstein, 2013). Visualizations in game research can be helpful to discover unexpected paths which have been taken by the players, to identify possible design and balancing problems, or to find common patterns in the behavior of the players. Moreover, since the rich interaction possibilities provided by a game can give rise to emergent behavior which is hard to anticipate, visualizations can assist in exploratory data analysis helping the analyst to discover potentially interesting structures, trends, and anomalies not thought of beforehand.

**Notes**
- Analytics, in a broad sense, is the process of identifying and communicating meaningful patterns to inform decision making.
- Game analytics can thus be understood as the application of analytics to game dev and research (El-Nasr et al., 2013)
- For a visualization to be effective it has to be appropriate for the specific tasks and goals of the user.
- Visualisation in important because : organization of large amounts of info in a quickly accessible form, builds value knowledge, quality control of the data
- Ben Medler – Data Cracker
- Charts / Heatmaps
- Movement visualizations :
	- Track position at regular time interals + orientation or health
- Node-link representation :
	- Used to visualize abstract or high-dimentional data

### [CHAPTER 19. Mohseni et al. 2015 : Extensive modding for experimental game research](https://dl.acm.org/doi/abs/10.5555/2812774.2812799)

**THM** : Best practices for extensive modding

**Authors** : M. ROHANGIS MOHSENI, BENNY LIEBOLD AND DANIEL PIETSCHMANN

**Tags** : :extensive-modding: :game-research:

**Abstract** : This chapter covers the topic of extensive modding in the area of experimental game research. In simplified terms, modding a computer game means changing specific aspects of how the game looks and feels to the user. This also includes applying minor changes to the game, such as changing the appearance of just a few objects, which does not require a high degree of expertise. Solely relying on making minor changes, however, severely limits the type of research questions that can be addressed. Therefore, we advocate extensive modding, which refers to modifications of a large extent that allow researchers to examine almost any research question imaginable. Extensive modifications may range from major content extensions or replacements, like new tasks or quests, game environments, characters or game mechanics, to total conversions, like changing the game scenario or theme, or the game genre. To apply these changes, researchers have to know which modding tools are available for a given game and how to use them properly. Advanced modifications, such as changing the behaviour of the game world and its inhabitants or even the fundamental mechanics of the game, often require deep knowledge of the game, game design, the modding community and--depending on the game--a certain level of programming skills.

**Notes**
- Modding (Scacchi, 2010) :
	1. User interface customizations*
	2. Game conversion mods*
	3. Machinima and art mods
	4. Custom gaming PCs
	5. Game console hacking
- Best practices :
	- Choose the game :
		- It should be up-to-date
		- Should have an active modding community
		- Should provide a setting that is suitable for the research question
	- Learn the game
	- Setup the plateform
	- Maximize internal validity
	- Create comparable stimuli
	- Equalize player sample
	- Dynamically adapt difficulty
	- Reduce Game complexity
	- Control cofounder variation
	- Conduct pre-test
	- Collect data

### [CHAPTER 20. Waern & Back, 2015. Experimental Design.](https://dl.acm.org/doi/abs/10.5555/2812774.2812800)

**THM** : NA

**Authors** : Annika Waern, Jon Back

**Tags** : :game-design:

**Abstract** : One way to understand games better is to experiment with their design. While experimental game design is part of most game design, this chapter focuses on ways in which it can become amethod to perform academic enquiry, eliciting deeper principles for game design.

**Notes**
- NA

## [Le Guennec, 2016 : Le métadesign, ou comment l’expérience doit échapper au designer](doi.org/10.3917/sdd.004.0124)

**THM** : The designer's role in one team during co-conception is to let other's creativity emerge, to do that, he needs to take a step back, not thinking about the product's expected qualities, but aiming at a 'user-designed' product.

**Authors** : Yann Le Guennec

**Tags** : :meta-design: :user-centered-design:

**Notes**
- Design d'expérience : conception de systèmes d'interactions entre les acteurs et les processus.
- Tâche du designer : qualifier la durée de l'expérience et tenter de lui donner un sens, un pouvoir de modification des états des acteurs
- La conception des systèmes d'interaction cherche à mettre en évidence un certain nombre de bénéfices pour ses acteurs.
- Utilisateurs sont acteurs au même titre que Concepteurs + Design centré utilisateur
    - = **meta-design** : "Le métadesign caractérise les objectifs, les techniques et les processus pour créer de nouveaux moyens d’expression et des environnements qui permettent aux possesseurs d’un problème d’agir en designers." ([Fischer, 2003](http://l3d.cs.colorado.edu/~gerhard/papers/hci2003-meta-design.pdf))
- Le rôle du designer réside dans le fait de permettre et favoriser l'émergence du dispositif de méta-conception - s'effacer pour laisser apparaître la créativité et les intentions de l'ensemble des acteurs du système = nécessité d'effacement = initialisation d'un processus de métadesign.
- Objectif personnel de participer à l'élaboration d'une sollution qui soit la plus satisfaisante possible pour les acteurs auxquels cette solution est destinée.
- Processus global commence par la formulation des bénéfices attendus et la fluidification des rôles des acteurs + fondé sur l'acceptation du désir humain d'une évolution qui passe par la transformation de son environnement,
- Questions :
    - désir de quoi? -> bénéfices attendus et donc les objectifs
    - responsables comment? -> manière d'y accéder, pertinence de ces objectifs et les outils méthodologiques qu'ils impliquent.

## [Lemercier et al., 2001 : Quels apprentissages sont-ils possibles avec des exerciseurs multimédia en classe ? Réflexions théoriques et compte rendu d’une expérience](http://andre.tricot.pagesperso-orange.fr/LemercierTricot_PNER.pdf)

**THM** : "La qualité d'une application est liée à son utilité, à son acceptabilité, et à son utilisabilité"

**Authors** : Céline Lemercier, André Tricot, Isabelle Chênerie, Didier Marty Dessus, Frédéric Morancho et Jérôme Sokoloff

**Tags** : :EIAH: :exerciseur:

## [Lloyd, 2001 : Measures of Complexity: A nonexhaustive List](https://doi.org/10.1109/MCS.2001.939938)

**THM** : 

**Authors** : Seth Lloyd

**Tags** : :complexity: :measure:

**Notes**
- Difficulty of Description :
	- Ex : Information, entropy, algorythmic complexity, minimum description length, fisher information, renyi entropy, code length, Chernoff information, dimension, fractal dimension, Lempel-Ziv complexity
- Difficulty of Creation :
	- Involving constructing or duplicating a system, e.g., time, energy, money
	- Ex : Computational compl, Time and Space computational compl, Information-based compl, logical depth, thermodynamic depht, cost, crypticity
- Degree of organization :
	- Difficulty of describing organizational structure, whether corporate, chemical, cellular, etc.
	- Ex : Effective complexity, metric entropy, fractal dimension, excess entropy, stochastic compl, sophistication, (...) grammatical complexity
	- Amount of organisation shared between the parts of a system as the result of organizational structure
	- Ex : Mutual information, Algorithmic mutual information, channel capacity, correlation, stored information, organization

## [Lövdén et al., 2020 - Human skill learning: expansion, exploration, slection, and refinement](https://doi.org/10.1016/j.cobeha.2020.11.002)

**THM** : Evidence in behavior, brain activity and brain structure during motor learning of this theory

**Authors** : Martin Lövdén, Benjamín Garzón & Ulman Lindenberger

**Tags** : :learning: :skill: :motor-learning:

**Abstract** : Learning, or the process of acquiring knowledge and skill, allows humans to shape and adapt to their environments during development. Researchers have long theorized that the principal brain processes behind learning resemble a recruitment process. The brain initially explores an expanded pool of candidate neural circuits. Based on outcomes, the most promising candidate circuit is selected for refinement. Partly fuelled by new methods, the last decade of research on learning-related functional and structural changes in rodents has supported this theory, and, more recently, related evidence has started to emerge from human studies. We emphasize the need for formal theories and neurocomputational modelling of cortical plasticity to guide work on open issues, such as the link between functional and structural changes.

**Notes**
- understand and enhance learning for needs of society, lifelong well-being and health, transformative force on environment
- "Performance on complex motor tasks typically shows rapid improvements and high variability early in practice, followed by a proacted period of slower developing refinements towards task execution with little variability

## [Lugan, 2009 : La modélisation des systèmes complexe chez E-Morin et J.-L. Le Moigne](https://www.cairn.info/la-systemique-sociale--9782130575528-page-99.htm)

**THM** : Système complexe est identifiable, actif, régulé, s'auto-régule, décide de son compmortement, a une mémoire, coordonne ses décisions d'action, imagine et choisit entre différentes décisions.

**Authors** : Jean-Claude Lugan

**Tags** : :complexity: :systems: :modelisation:

**Notes**
- Positivisme : Philosophie envisagée comme une véritable science : 'Pourquoi' -> 'Comment' ([REF](https://www.youtube.com/watch?v=n1brw_hmcV8))
- Pour E. Morin, est complexe ce qui ne peut se ramener à une loi, ce qui ne peut se réduire à une idée simple.
- Modéliser ? C'est l'action intentionnelle de construire, par composition de concepts et de symboles, des modèles susceptibles de rendre plus intelligible un objet ou un phénomène perçu complexe et d'amplifier (...) le raisonnement de l'acteur projetant une intervention délibérée au sein de cet objet ou de ce phénomène et à anticiper les conséquences des projets d'actions possibles.
- Modélisation analytique : isole les éléments d'un ensemble, (...) conduit à une action programée dans son détail. "De quoi c'est fait?"
- Modélisation systémique : relie les éléments d'un ensemble, insiste sur leurs relations (...) "Qu'est-ce que ça fait?"
- Modéliser, c'est à la fois identifier et formuler quelques problèmes sous la forme d'énoncés et chercher à résoudre ces problèmes en raisonnant par simulations. ERn faisant fonctionner le modèle problème, one tente de produire des modèles-solutions.
- Système général entre : Fonctions, Projet, Transformations, Environnement actif
- Modéliser un système complexe, c'est modéliser un système d'actions.
- Un processus est donc a priori un complexe d'actions multiples et enchevêtrées que l'on perçoit par l'action résultante et caractérisée par l'articulation ou la composition de trois fonctions fondamentales :
	- La fonction de transfert temporel : mémorisation, stockage;
	- La fonction de transformation morphologique : traitement, computation;
	- La fonction de transfert spatial : transport, transmission
- Pour J.-W. Lapierre, de manière plus concise, c'est tout changement dans le temps de matière, d'énergie ou d'information qui se produit dayns le système, traitant ces variables d'entrée et le menant aux variables de sortie.
- 9 niveaux de complexité (K.Boulding) :
	1. **Identifiable** : dispose d'une perception minimale; le phénomène qu'il modélise se différencie de son environnement
	2. **Actif** : il est perçu car actif
	3. **Régulé** : le phénomène doit être perçu à travers des formes manifestant régularité sinon stabilité
	4. **S'informe sur ses propres comportements** (rétro-feedback? Auto-régulation?) : comportements résultant à des effets sur l'environement
	5. **Décide de son comportement** (Autonomie?) : élaboration de ses propres décisions de comportement, suppose un sous-système de décisions autonome
	6. **Mémorise les informations** (prior knowledge?) : afin d'élaborer ses décisions, le système considère non seulement les informations instantanées mais aussi les informations qu'il a mémorisées.
	7. **Coordonne ses décisions d'action** : que le système doit prendre à chaque instant
	8. **Imagine et conçoit de nouvelles décisions possibles** : solutions alternatives
	9. **Se finalise** : décide des finalités
- Organisation selon le triple paradigme d'E. Morin :
	1. Ré  -organisation qui exprime la transformation diachronique;
	2. Auto-organisation qui exprime l'autonomie;
	3. Eco -organisation qui exprime le fonctionnement synchronique ouvert dans l'environnement
- Se distingue de la structure comprise comme charpente, squelette relativement stable.

## [Luo et al., 2024 : Large language models surpass human experts in predicting neuroscience results](https://arxiv.org/abs/2403.03230)

**THM** : 90% accuracy LLM vs 66% accuracy human experts

**Authors** : Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N Apurva Ratan Murty, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love

**Tags** : :llm: :brainGPT: :prediction:

**Abstract** : Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.

**Notes**
- Can LLMs outperform human experts on this forward-looking benchmark? In particular, BrainBench evaluates how well the test-taker can predict neuroscience results from methods by presenting two versions of an abstract from a recent journal article. The test-taker’s task is to predict the study’s outcome, choosing between the original and an altered version. The altered abstract significantly changes the study’s outcome (i.e., results) while maintaining overall coherence.

## [Mayer, 2010 : Apprentissage et technologie](https://books.google.fr/books?hl=fr&lr=&id=306PApBeLTwC&oi=fnd&pg=PA179&dq=mayer+2010+learning+with+technology&ots=-KDkEGRGoN&sig=1tzXmzcDjMbGJ8R0GV1Nh8DciSk&redir_esc=y#v=onepage&q=mayer%202010%20learning%20with%20technology&f=false)

**THM** : Principes de design pédagogique pour favoriser l'ap. par la tech

**Authors** : Richard E. Mayer

**Tags** : :learning: :technology: :cognitive-load:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- 2 questions :
	1. Comment apprend-on avec la technologie du point de vue de l'apprenant ?
	2. Comment exploiter la technologie pour facilité l'apprentissage du point de vue de l'enseignant·e ?
- Fomation assistée par ordinateur, Multimédia, Simulations interactives, Hypertextes et hypermédias, Tuteurs intelligents, Recherche documentaire par requêtes, Agents pédagogiques animés, Environnements virtuels avec agents pédagogiques, Jeux sérieux, Apprentissage collaboratif assisté par ordinateur
- Approche d'apprentissage par la technologie, centré technologie (comment utiliser technolgie pour enseigner) :
	- Son plus grand défaut : ne tient pas compte de l'apprenant et postule que les apprenants et enseignants s'adapteront aux exigences des nouvelles technologies et non l'inverse (Norman, 1993)
- Approche d'apprentissage par la technologie, centré sur apprenants (comment adapter la technolgie pour favoriser l'apprentissage)
- Mécanismes d'apprentissage par la technologie :
	- *Double canal* : le cerveau humain dispose de deux canaux distincts pour traiter les informations verbales et visuelles (Paivio, 1986, 2007)
	- *Capacité limitée* : le cerveau humain ne peut traiter qu'une petite quantité d'informations à la fois dan chaque canal (Baddeley, 1999; Sweller, 1999)
	- *Traitement actif* : l'apprentissage est signifiant lorsque l'apprenant met en oeuvre un processus cognitif approprié, par exemple, se concentrer sur les informations utiles, le sorganiser en une représentation cohérente et les intégrer aux connaissances pertinentes déjà acquises (Mayer, 2008; Wittrock, 1989)
- L'enseignement par la technologie recouvre à la fois les supports, ou médias, pédagogiques, et les méthodes pédagogiques
- Principes de design pédagogique susceptibles de favoriser l'ap. par la tech. :
	1. Distinction entre traitements inutile (extraneous), essentiel (intrinsic) et génératif (germane) (Sweller, 1999)
	2. Réduire le traitement inutile :
		- Cohérence - les informations non pertinentes doivent être limitées
		- Signalisation - les informations essentielles sont mises en évidence
		- Redondance - aucun texte ne doit être ajouté à l'écran sur une animation narrative
		- Contiguïté spatiale - le texte est placé à proximité des graphiques correspondants
		- Contiguïté temporelle - la narration et l'animation correspondante sont présentées simultanément
	3. Maîtriser le traitement essentiel :
		- Segmentation - présentation d'animation en segments adaptés au rythme de l'apprenant
		- Préformation - formation préalable à la désignation, à la locailsation et aux caractéristiques de sprincipales composantes
		- Modalité - texte présenté sous forme verbale plutôt qu'écrite
	4. Techniques pour favoriser le traitement génératif
		- Multimédia - apprentissage plus en profondeur lorsqu'on est encouragé à construire des liens entre les mots et les images
		- Personnalisation - adressage à l'apprenant à la première ou la deuxième personne

## [Meri et al., 2022 : Higher access to screens is related to decreased functional connectivity between neural networks associated with basic attention skills and cognitive control in children](https://doi.org/10.1080/09297049.2022.2110577?link_credit=KennethRioja)

**THM** : Importance of child-parent interaction during screen exposure, even at 8-12 yo, associated with better cognitive abilities - may allow a better error monitoring, leading to better learning patterns.

**Authors** : Raya Meri, John Hutton, Rola Farah, Mark DiFrancesco, Leonid Gozman & Tzipi Horowitz-Kraus

**Tags** : :children: :neurobiology: :screen: :cognitive: :child-parent-interaction:

**Abstract** : Screen-based media has become a prevailing part of children’s lives. Different technologies provide limitless access to a wide range of content. This accessibility has immensely increased screen exposure among children, showing that this exposure is associated with decreased cognitive abilities. This study was designed to evaluate how the neurobiological correlates for different sub-components of screen exposure, such as level of access, content, and frequency, are related to different cognitive abilities. Resting-state functional MRI data were collected in 29 native English-speaking children (8–12 years old), in addition to cognitive-behavioral measures. Functional connectivity measures within and between several networks related to cognitive control and attention were calculated [fronto-parietal (FP), cingulo-opercular (CO), dorsal attention (DAN), ventral attention (VAN), salience, default mode (DMN), cerebellar networks]. Sub-components of screen exposure were measured using the Screen-Q questionnaire. Higher access to screens was related to lower functional connectivity between neural networks associated with basic attention skills and cognitive control (i.e., DAN and salience). In addition, higher levels of parent-child interaction during screen exposure were related to increased functional connectivity between networks related to cognitive control and learning (i.e., CO and cerebellar). These findings suggest that screen exposure may reduce the engagement of basic attention and modulation of cognitive control networks and that higher levels of parent-child interaction engage cognitive control networks. An enhanced understanding of these processes can provide an important scientific basis for future educational and medical approaches regarding screen exposure.

**Notes**
- ? exposure associated with decreased cognitive abilities in children
- ? this study was designed to evaluate how the neurobiological correlates for diff sub-components of screen exposure (level of access, content and freq)
- American Association of Pediatrics (AAP) recommendations, children > 2yo can consume 1-2 hours/day of screen
- Screen exposure *associated* with obesity, language delays, attention problems and deterioration of cog abilities
- if too much screen time, reduced parent-child interaction
> RQ: explore the relations between the type of screen exposure (level of access, content, frequency, and parent-child interaction)
- n=29, 8-12 yo, no diff av. household income or maternal lvl of ed.
- screen content (non-violent, slow pace, from ScreenQ, answered by parents) x WM (digit span, WISC) : **r = .4**
- dialogue (parent-child interaction and co-viewing) x Speed of Processing (subtest of symbol seatch WISC) : **r = -.41**
> Overall, higher screen access measure was related to decreased functional connectivity between networks related to attention and modulation of cognitive control, and increased functional connectivity between networks related to cognitive control and learning was associated with lower screen dialogue measure.

**Comments**
- the screen content items are indicated as difficult for parents to answer for their children probably due to the young age of children () and/or social desirability bias ([Hutton et al., 2020](https://doi.org/10.1038/s41390-020-0765-1?link_credit=KennethRioja))
- the screen content subscale ranges from 0-5, 0 is no violent media + child never dl apps + talk/sing media VS. 5 is often violent media + often child dl apps + fast more action

## [Molinari et al., 2017 : Les émotions dans les situations de collaboration et d'apprentissage collaboratif médiatisées par ordinateur](https://doi.org/10.3917/raised.021.0175)

**THM** : 

**Authors** : Gaëlle Molinari, Sunny Avry, Guillaume Chanel

**Tags** : :computer-mediated-collaborative-learning: :emotion:

**Notes** :
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Emotion comme une réaction brève à des événements de l'environnement qui comporte des composantes cognitive, physiologiques, motivationnelle, expressive et affective (Scherer, 2001)
- Théorie Contrôle-Valeur de [Pekrun (2006)](https://link.springer.com/content/pdf/10.1007/s10648-006-9029-9.pdf) :
	- Rend compte des antécédents et de effets des émotions en contexte d'apprentissage
	- Cible les émotions d'accomplissement liées à l'activité et son résultat
	- Lien émotion et niveau d'activation pendant activité ou à propos du résultat
	- Les émotions d'accomplissement sont le résulat d'une évaluation de la situation sur la base de deux critères subjectifs :
		- Le contrôle comme perception de notre capacité à produire les actions nécessaires à la réussite de la tâche (link with SDT)
		- La valeur attribuée à l'activité et à son résultat qui peut être intrinsèque lorsqu'elle renvoie à l'intérêt ou extrinsèque lorsqu'elle renvoie au bénéfice perçu au regard des buts (link with intrinsic/extrinsic motivation)
	- Prédiction : plus les apprenant·e·s se sentent en contrôle et plus ils sont susceptibles de ressentir des émotions positives
	- Pekrun et al. (2011) :
		- Lien entre caractéristiques de la situation <-> évaluation de cette dernière (appraisal) <-> émotions <-> processus apprentissage et résultat
		- Effet (+) des émotions activantes (joie, espoir, fierté) et (-) des émotions désactivantes (désespoir, ennui) sur l'engagement et l'apprentissage.
		- Colère, anxiété, honte : - corrélées à motivation intrinsèque, effort perçu pour apprendre, auto-régulation et performances académiques
		- Anxiété : + corrélée à motivation extrinsèque, utilisation de stratégies de surface, besoin de régulation externe par enseignant.
- Le partage social des émotions participe à la construction des liens sociaux (Rimé, 2005, cité par Gauducheau, 2008) et facilite la coordination interpersonnelle
- Emotions dans l'apprentissage collaboratif :
	- Transactivité (Berkowitz & Gibbs, 1983) : dans l'espace cognitif, les apprenant·e·s s'engagent à produire du sens à partir du contenu d'apprentissage à partir du raisonnement et des idées et leurs partenaires
	- Selon Järvenoja et Järvelä (2013), six stratégies de régulation pour équilibre socio-émotionnel : 1) renforcement social, 2) discussion orienté vers les buts, 3) augmentation de l'intérêt, 4) structuration de la tâche, 5) auto-handicap, 6) gestion du sentiment d'efficacité
- Results :
	- Effet d'outil d'Awareness Emotionnel :
		- Majoritairement positive : satisfaction et confiance
		- EAT permet de plus comparer own emotions à other
		- Femmes x EAT ont rapporté avoir passé plus de tmeps à construire sur les idées de leur partnaire vs. control condition. Inverse chez les hommes et transactivité plus important dans contrôle que EAT.
		- Pp qui rapportent ressentir plus intensément des émotions pos ont aussi plus rapporté transaction et comparé émotionsn à celles de leur partenaire pdt interaction
		- Share de manière spontané (hors des rappels) et plus positives que neg.
		- Regard porté 4* plus sur zones d'émotions positives que neg (contrôlé par le nb d'émotion partagé)
		- Regard plus porté sur own que other
		- H/F différence pas dans le nb d'émotions communiquées mais H plus regardé visualisation other's emotion que F
	- Effet des émotions d'accomplissement :
		- PP rapportent - d'ennui et + de satisf quand reçoit feedback de valeur élevée plutôt que faible
		- PP rapportent - frustration et - désespoir quand reçoit feedback de contrôle élevé plutôt que faible
		- Honte quand contrôle faible x valeur faible
		- Joie quand contrôle élevé x valeur faible
		- Transactivité et performance de groupe augmentent quand satisfaction augmente et ennui diminue
		- Joie positivement corrélée à mise en commun des informations et à la performance de groupe
		- Frustration et désespoir négativement corrélés à performance de groupe
		- Gratitude la plus fortement corrélée à la performance de groupe et transactivité, argumentation et recherche de consensus

## [(YT) Morard, 2023 – AL2049, a PLayful Museum's Visit to Grasp the Issues of Complexity](https://www.youtube.com/watch?v=fl3l9EYA1tM)

**Authors** : Simon Morard

**Tags** : :play: :al2049: :museum: :video-game:

**Notes**
- Food production & complexity : Ericksen, 2009; Rastein & Ghersi, 2010; NRC 2015
- Collective game
- There is not a perfect and unique solution yet.
- Resource management game can be metaphorical
- https://drive.google.com/file/d/1p6E-H902UaFC9lcFz9gCjuLPsw5W-N7R/view :
	- Mixed method, gameplay analysis
	- Culture, society, tourism

## [Morin, 1999 – Les sept savoirs nécessaires à l'éducation du futur](http://unesdoc.unesco.org/images/0011/001177/117740Fo.pdf)

**THM** : Penser et éduquer de manière planétaire

**Authors** : Edgar Morin

**Tags** : :education: :savoir:

**Notes**
- "L'un des défis les plus difficiles à relever sera de modifier nos modes de pensée de façon à faire face à la complexité grandissance, à la rapidité des changements et à l0imprévisible, qui caractérisent notre monde. (p.7)
- Exprimer ses idées sur l'essence même de l'édcation du futur dans le contexte de sa vision de "la pensée complexe"
1. Les cécités de la connaissance : l'erreur et l'illusion :
	- Le développement de l'intelligence est inséparable de celui de l'affectivité
	- A l'erreur de perception s'ajoute l'erreur intellectuelle. La connaissance, sous forme de mot, d'idée de théorie, est le fruit d'une traduction/reconstitution par les omyens du langage et de la panesée et elle connaît le risque d'erreur. Cette connaissance, à la fois en tant que traduction et en tant que reconstruction, comporte de l'interprétation, ce qui introduit le risque d'erreur à l'intérieur de la subjectivité du connaissance, de sa vision du monde, de ses principes des connaissance.
	- Un rationalisme qui ignore les êtres, la subjectivité, l'affectivité, la vie est irrationnelle (#quotes)
	- Nous commençons à devenir vraiment rationnels quand nous reconnaissons la rationalisation incluse dans notre rationalité et reconnaissons nos propres mythes, dont le mythe de la toute-puissance de notre raison et celui du progrès garanti.
	- Reconnaître dans l'éducation du future un **principe d'incertitude rationnel**
	- Le mythe et l'idéologie détruisent et dévorent les faits (...) **nous devons mener une lutte cruciale contre les idées, mais nous ne pouvons le faire qu'avec le secours des idées**.
	- Le nouveau jaillit sans cesse (...) une fois l'inattendu survenu, il faudrait être capable de réviser nos théories et idées, plutôt que de faire entrer au forceps le fait nouveau dans la théorie incapable de vraiment l'accueillir.
	- 20/71
2. 

From : https://nospensees.fr/les-sept-savoirs-necessaires-a-leducation-du-futur-selon-edgar-morin/ :
- Lutte contre les erreurs du système éducatif actuel
- Aveuglement du savoir : erreur et illusion :
	- Emotions et limitation de la perception énère des biais et limitation des connaissances : critique de la connaissance elle-même
- Les principes d'une connaissance pertinente :
	- Générer des méthodes qui facilitent la détection des interactions complexes qui existent dans la réalité – en intégrant les connaissances au lieu de les désagréger
- Enseigner la condition humaine :
	- Identité vs. membre d'un groupe; cerveau-esprit-culture, raison-affect-impulsion, individu-société-espèce
- Enseigner l'identité terrienne :
	- Education doit avoir une perspective planétaire (environnement immédiat), rend les connaissances plus réelles et inclusives
- Affronter les incertitudes :
	- La réalité est dynamique, changeante, incertitude continuelle dans les décisions. Il n'est pas convenable de continuer à éduquer les individus comme si ce qui existait était un système de certitudes
- Enseigner la compréhension :
	- Communication =/= compréhension; ouverture empathique envers les autres + tolérance des idées et des sentiments des autres, tant qu'ils ne violent pas la dignité humaine
- L'éthique du genre humain :
	- Promotion des pratiques démocratiques et le respect de la diversité (sinon dictature des majorités) – les projets, interactions et engagements doivent être conçus et vécus à l'échelle de l'espèce.

## [Mosconi, 2004 : Effets et limites de la mixité scolaire](https://www.cairn.info/revue-travail-genre-et-societes-2004-1-page-165.htm)

**THM** : 

**Authors** : Nicole Mosconi

**Tags** : :genre:

**Notes**
- Socialisation scolaire :
	- "La dynamique du milieu scolaire contibue à faire vivre aux garçons et aux filles des expériences très différentes liées aux rapports sociaux de sexe (Duru-Bellat, 1994, 1995)"
	- Stéréotypes de sexe :
		- "les femmes, dans une situation d'interaction compétitive, diminuent leur auto-attribution de compétence quand le groupe est mixte, par rapport à la compétence qu'elles s'attribuent dans un groupe non-mixte (Lorenzi-Cioldi, 1988)"
		- "A l'école, on observe que les filles, en présence de garçons, ont tendant à se sous-estimer, particulièrement dans les domaines connotés masculins où les garçons, eux, ont tendance à se sur-estimer"

## [Mulet et al., 2017 : Quels tuteurs informatisés pour réduire les comportements d’évitement de la recherche d’aides des apprenants.](http://journals.openedition.org/dms/1954)

**THM** : 

**Authors** : Julie Mulet, Jean-Christophe Sakdavong et Nathalie Huet

**Tags** :

**Notes** 
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Formation à distance
- Aides proposées aux apprenants et la façon dont ces derniers les utilisent
- Comportements de recherche d'aide 

## [Nelson-Le Gall, 1985 : Help-Seeking Behavior in Learning](https://doi.org/10.3102/0091732X012001055 )

**THM** : 

**Authors** : Sharon Nelson-Le Gall

**Tags** : :help-seeking: :learning:

**Notes** :
- Social-normative models:
	- Cultural values and social roles emphasize norms related to dependence or independence as the basis for predicting and explaning help-seeking
	- Cultural norms emphasizing self-reliance and individual achievement may influence attitudes toward HS - Individuals may differ in the way they have internalized them
	- Diff bw US and Dutch (Graf et al., 1979)
	- Also sex differences (McMullen & Gross, 1983) - on the sex-role-consistent behavior (f+, m-)
	- Limitations : evaluations of the perceived normativeness of help-seeking for occupants of the student role in learning setting are virtually nonexistent
	- Research indicates that help-seekers tend to prefer helpers who are perceived as being older, competent, and obligated to comply with their requests for help (e.g., Barnett, Darcie, Holland, & Kobasigawa, 1982; Druian & De Paulo, 1977; Nelson-Le Gall & Gumerman, 1984)
	- If, however, classroom norms support and encourage informal helping exchanges among peers, the perceived costs of help-seeking may be lessened.
	- Limits : a) variability may exist within a given group so that generalizations are not possible, b) measures of specific aspects of individuals' personality and motiv orient. of the environment appear to have greater predictive and explanatory power than do normative variables (Ames, 1983; Fisher, Nadler, & Whitcher-Alagna, 1982)
- Self-esteem models :
	- Focuses on the role of norms and beliefs in moderating the decision to seek or not to seek help. The beliefs are deeply instilled personal beliefs and conceptions about the self as an individual.
	- Prediction based on notion of vulnerability :
		- Low self-esteem ind seek less than high self-esteem ind - less positive self-cognition > more vulnerable to self-threatening info > avoid the self-threatening situation of seeking help
	- Prediction based on notion of consistency :
		- High self-esteem is expected to be associated with less help-seeking - it is the inconsitency of incoming self-related information with existing self-cognitions that is threatening to the self
- TO READ - instrumentality of Help-Seeking

## [Nguyen & Bavelier, 2023 : Play in Video Games](https://doi.org/10.1016/j.neubiorev.2023.105386)

**THM** : 

**Authors** : Nguyen Arthur, Bavelier Daphné

**Tags** : :video-games: :locomotor-rotational: :object: :social-play: :role-play: :rule-play: :play-pretend: :cognition: :future-learning: :action-video-games:

**Abstract** : Video game play is remarkably ubiquitous in today's society given its recent emergence only in the late 1950s. While this fast evolution could exemplify the power of play, video games exploit but also extend other types of play. Here, we review a classification of the ecosystem of video games useful in the emerging field of the cognitive neuroscience of video games. We then discuss how video games may leverage different play types, considering first locomotor-rotational, object, and social play before highlighting the importance of role, rule, and pretend play in video games. With an eye toward comparative studies of the neural bases of play across species, we discuss whether video games may fulfil the five criteria from Burghardt (2005) to identify play. Finally, in line with play’s possible preparatory role for adulthood, we review the positive impact on cognition and future learning of action-like video games. Highlighting that not all video games have this impact, we note more granular hypotheses about the biological functions of play are to be encouraged.

**Notes**
- Avergae age of a US and European gamer in 2021 was 32 years old, with 7680% of gamers being 18 or older ([esa, 2022](https://www.theesa.com/resource/2022-essential-facts-about-the-video-game-industry/), [ISFE and EGDF, 2021](https://www.videogameseurope.eu/wp-content/uploads/2022/08/FINAL-ISFE-EGDFKey-Facts-from-2021-about-Europe-video-games-sector-web.pdf))
- In France, the aerage age was 39 yo, with 88% of adults having played vg ([SELL, 2022](https://www.sell.fr/sites/default/files/essentiel-jeu-video/lessentiel_du_jeu_video_eng_nov_22.pdf))
- AVG derive their positive impact on :
	- Putting a load on divided attention
	- Switch attentional mode on demande, visual search mode to highly focused, aiming mode
	- Alows to train cognitive flexibility
	- Load on pacing or the need to execute actions or take decisions under tight time constraints : feel under time pressure

## [Oliveira et al., 2022 : Game Design for a Museum Visit: Insights into the Co-design of AL2049, a Game About Food Systems](https://doi.org/10.1007/978-3-031-22124-8_3)

**THM** : 

**Authors** :Gil Oliveira, Nicolas Godinot, Eric Sanchez, Catherine Bonnat, Simon Morard, and Sandro Dall’ Aglio

**Tags** : :game: :co-design: :learning: :museum:

**Abstract** : This paper presents the results of the co-design of a learning game in a museum context. This game deals with the topic of the food system and considers how this concept is integrated with the Swiss school curriculum which constituted the starting point of the game design. Based on the ludicization theoretical framework, we question how learning objectives were articulated with the game elements and the choices made to design a playful learning situation. Our work is grounded on a design-based research methodology. In this context, we describe the initial phase of co-design, which involved co-design activities, research of educational content, and iterative prototyping. This empirical work led to the game AL2049, which aims to bring secondary school students (12–15 years old) to understand interrelationships in food systems and, in so doing, to face complex problem solving. Results comprise the game and the choices made in regard to the ludicization model.

**Notes**
- "Designing a reflexive space in which a student can interact and of changing the meaning of the learning situation so that it becomes playful (Sanchez et al., 2016)"
- Popularize the concept of food system to a large audience
- Integration of knowledge about food system complexity from PER
- Engage in reflexive thinking through : elaborating personal opinions, identify facts, verify their accuracy, put them in perspective, explore different options and pov, abandon preconceived ideas, compare his/her pov with those of others, face doubt and ambiguity
- Prerequisite for understanding complex systems = critial thinking
- 2 pedago objectives :
	- Systemic approach = understanding complex relationships bw components of the food system
	- Critical thinking = focused on reflecting and evaluating the information provided
- Gamification : ex. adding game elements
- Ludicization : 
	- Focus on the play - the situatione xperienced by the learner, rather than on the game itself
	- Change the meaning of the learning situation so that it becomes playful
	- Integration of a learning content into a game, providing an epistemic dimension to the gaming experience and enabling students to dvp their knowledge from it
- Model of Ludicization (Bonnat et al., 2023) :
	- Conversion of a "target domain" (domain to be learned, food system), which includes complex and multidisciplinary knowledge, into a "source domain" (learning situation, AL2049) that takes the form of a playful learning experience
	- Usage of a metaphor : transfer from transparent source domain to an opaque target domain
	- Thus describing abstract concepts in a more comprehensible and concrete way by providing analogies from familiar domains (Lakoff & Johnson, 1980)
	- Consider games as hermeneutic spaces (Caracciolo, 2009) : theory of interpretation include the art of understanding and communication
	- "The use of a metaphor implies that there is a hidden meaning behind the fame, and that this second level of meaning gives the game its power"
	- The metaphor remains implicit within the game and is rendered explicit during the debriefing
- Phase 1 : scenario through 2 game jams
- Phase 2 : 11*1/2 days workshops between research team, museum curator, game designer
- Phase 3 : Prototype testing and adjustment
- Combines tangible and digital elements
- 12-15 yo, 90 min, goal : feed the population
- Core mechanic = assigning specific functions to different spaces
- "The simulation that took place in the museum, understood as a closed system, represents the planet and the global food system. Players can reflect on their experience – they learn by reflecting on the game xp rather than on the xp itself (source, AL2049)
- The debriefing helps to transform the subjective knowledge developed during the game (the need to assess the quality of information) into knowledge that can be used in another context)
- How a game can be an analogous and metaphorical situation of a domain that needs to be learned
- "Traces will be collected to see how it is used by the players and how their food conceptions evolve"

## [Ortiz-Rojas et al., 2017 : Gamification and learning performance. A systematic review of the literature](https://www.researchgate.net/profile/Margarita-Ortiz/publication/320372825_Gamification_and_learning_performance_A_systematic_review_of_the_literature/links/59e488b9aca2724cbfe90b51/Gamification-and-learning-performance-A-systematic-review-of-the-literature.pdf)

**THM** : On 23 studies, mostly positive to no s* effects of gamification for high-edu students

**Authors** : Margarita Ortiz-Rojas, Katherine Chiluiza, Martin Valcke

**Tags** : :gamification: :learning-outcomes:

**Abstract** : Over the last 10 years, research on gamification, the use of game elements in non-game contexts, has increased in the field of education, due to its potential to enhance learning performance. Yet, the majority of available research rather focuses on the evaluation of motivation and engagement as key dependent variables. Hence, the purpose of this study is to review available studies on gamification, with an exclusive focus on learning performance as the key dependent variable. Through a systematic search and selection process, building on “Web of Science” articles and by considering studies between 2000 and 2016 related to gamification and learning, 582 articles were identified. Further inclusion and exclusion criteria, regarding setting (education), study focus (empirical), journal access (full access) and dependent variables (learning performance), resulted in a review of 23 articles meeting the criteria. The analysis of these articles showed how gamification could be linked to a direct increase in learning performance of students. Nevertheless, some studies also reflect weaker statistical differences between being involved or not in a gamified environment. The review analysis results are especially helpful to define a future agenda for gamification research, addressing the following gaps in the literature. First, include mediating and moderating variables to find more empirical research that can prove an indirect effect of gamification on learning performance. Second, carry out additional research that empirically underpins the direct linkage between gamification and learning performance. Third, include specific individual gamification elements to be able to determine explicit differential effects of these elements on learning performance. Fourth, conduct research in a broader range of knowledge fields to develop empirical evidence in the context of other knowledge domains next to computer sciences. Finally, consider involving larger sample and setting up longer experimental interventions, to avoid novelty effects and risks of lack of generalization. 

**Notes**
- 23 final studies based on gamification, education, empirical work in a class setting, research articles, full access, learning/academic perf/achievement
- mostly high edu, combination of gamification elements, STEM
- results : 9 +, 12 no s*, 2 -

## [Oviedo-García, 2021 : Journal citation reports and the difinition of a predatory journal: The case of the Multidisciplinary Digital Publishing Institute (MDPI)](https://doi.org/10.1093/reseval/rvab020)

**Authors** : M. Angeles Oviedo-García

**Tags** : :predatory-journal: :publication: :journal:

**Abstract** : The extent to which predatory journals can harm scientific practice increases as the numbers of such journals expand, in so far as they undermine scientific integrity, quality, and credibility, es- pecially if those journals leak into prestigious databases. Journal Citation Reports (JCRs), a refer- ence for the assessment of researchers and for grant-making decisions, is used as a standard whitelist, in so far as the selectivity of a JCR-indexed journal adds a legitimacy of sorts to the articles that the journal publishes. The Multidisciplinary Digital Publishing Institute (MDPI) once included on Beall’s list of potential, possible or probable predatory scholarly open-access publish- ers, had 53 journals ranked in the 2018 JCRs annual report. These journals are analysed, not only to contrast the formal criteria for the identification of predatory journals, but taking a step further, their background is also analysed with regard to self-citations and the source of those self-cita- tions in 2018 and 2019. The results showed that the self-citation rates increased and was very much higher than those of the leading journals in the JCR category. Besides, an increasingly high rate of citations from other MDPI-journals was observed. The formal criteria together with the analysis of the citation patterns of the 53 journals under analysis all singled them out as predatory journals. Hence, specific recommendations are given to researchers, educational institutions and prestigious databases advising them to review their working relations with those sorts of journals.

**Notes**
- Nature definition of pred journal : "Predatory journals and publishers are entities that prioritize self-interest at the expense of scolarship and are characterized by false or misleading information, deviation from best editorial and publication practices, a lack of transparency, and/or the use of aggressive and indiscriminate solicitation practices" ([Grundniewicz et al., 2019](http://dx.doi.org/10.1038/d41586-019-03759-y))
- Pred journal has no or pratically inexistent peer-review process

## [Parmentier, 2023 : Jeux expressifs :iLe récit suffit-il à générer du sens ? Étude du lien entre la richesse du gameplay d’un jeu expressif et la perception de sens chez le « joueur eudémonique ».](https://tecfa.unige.ch/tecfa/maltt/memoire/Parmentier2023.pdf)

**THM** : 

**Authors** : Hélène Parmentier

**Tags** : :jeu-expressif: :meaning: :jeu-sérieux: :eudémonisme:

**Abstract** :Appelés meaningful, purposeful, voire eudaimonic games en anglais, les jeux expressifs proposent un gain de sens ou de perspective, parfois à travers leur structure narrative, parfois en utilisant le gameplay pour permettre au joueur d’interpréter le sens du jeu en interagissant avec lui. Cettte étude explore le lien entre la richesse du gameplay et la perception de sens chez les joueurs, en distinguant le sens du contenu (signifiance) et le sens du jeu en tant que support (pertinence). Par ailleurs, les jeux expressifs sont un médium de choix pour le jeune domaine de recherche que représente l’expérience vidéoludique eudémonique. L’éudémonisme, chez les grecs, était une conception du bonheur dans laquelle les individus s’investissent dans des activités signifiantes dans un objectif de croissance personnelle. En utilisant deux jeux expressifs traitant du deuil, nous étudions le lien entre l’eudémonisme des joueurs et leur perception de sens des jeux expressifs. Cinquante participants ont rempli un questionnaire visant à mesurer un score d’eudémonisme avant d’être aléatoirement répartis en deux groupes, l’un proposant un jeu dont le récit est entièrement intégré au gameplay, l’autre dont le récit est plutôt transmis directement. Ils ont ensuite renseigné un questionnaire portant sur l’expérience de jeu et le sens perçu. Bien que les résultats indiquent effectivement que le niveau de gameplay détermine la pertinence du support, qui à son tour détermine l’appréciation du jeu, aucune différence n’a été trouvée entre les deux conditions. Des perspectives sont proposées pour la recherche en expériences vidéoludiques eudémoniques. 

**Notes**
- Expressive/meaningful games, more than entertainment or leisure (Susi et al., 2015) VS persuasive games (to convince) -> they invite player to requestion the world and want to leave player find their own answer and interpret the play experience.
- No pedagogical goals (VS educational games)
- Recherche de sens est associée au concept d'eudémonisme -> vise à la réalisation de son plein potentiel, à une vie accomplie -> s'implique dans des activités ou buts qui favorisent la croissance et en perpétuelle quête de sens.
- L'expérience de jeu étant délibérément suggestive et reposant sur l'interprétation du sens du jeu par le joueur lui-même
- "En jouant à ces jeux qui permettent de questionner le monde, d’interroger ses propres représentations, de changer de perspective, le « joueur eudémonique » trouverait dans ces expériences de jeu le sens qu’il recherche au sein des activités dans lesquelles il s’investit.
- JVP défi d'intégration dans la mécanique principale du jeu (Szilas & Widmer, 2009)
- Expressif vs pédagogique: on n'y enseigne/apprend pas une connaissance ou une compétence précise, mais plutôt une forme de "leçon de vie", qu'on connaissait sans doute déjà d'une certaine manière, en offrant la possibilité de la vivre différemment pour obtenir l'"effet Eureka".
- Daneels et al. (2020), dimensions qui font d'un jeu une "expérience eudémonique" :
	- Emouvant (+/-) -> mixed-affect, plus appréciation que plaisir, càd l'exposition volontaire à des expériences difficiles offrant paradoxalement un bénéfice positif (Cole & Gillies, 2022)
- Why does this story need to be told as a video game? (Bogost, 2017) -> Quelle est la valeur ajoutée du jeu dans cette expérience -> discriminer sens du support du sens de l'expérience
- Pb: interroger l'existence d'un lien entre l'eudémonisme des joueureuses et leur expérience de jeu expressif en fonction de son niveau de gameplay
- 

## [Plass et al., 2015 : Foundations of Game-Based Learning](https://doi.org/10.1080/00461520.2015.1122533)

**THM** : 

**Authors** : Jan L. Plass, Bruce D. Homer & Charles K. Kinzer

**Tags** : :game-based-learning:

**Abstract** : In this article we argue that to study or apply games as learning environments, multiple perspectives have to be taken into account. We first define game-based learning and gamification, and then discuss theoretical models that describe learning with games, arguing that playfulness is orthogonal to learning theory. We then review design elements of games that facilitate learning by fostering learners' cognitive, behavioral, affective, and sociocultural engagement with the subject matter. Finally, we discuss the basis of these design elements in cognitive, motivational, affective, and sociocultural foundations by reviewing key theories from education and psychology that are the most pertinent to game-based learning and by describing empirical research on learning with games that has been or should be conducted. We conclude that a combination of cognitive, motivational, affective, and sociocultural perspectives is necessary for both game design and game research to fully capture what games have to offer for learning.

**Notes** :
- no s* gender differences in learning or motivationnal outcomes in ed games ([Annetta et al., 2009](https://doi.org/10.1080/09500690801968656); [Papastergiou, 2009](https://doi.org/10.1016/j.compedu.2008.06.004))
- Game-based learning = type of game play with defined learning outcomes ([Shaffer et al., 2005](https://files.eric.ed.gov/fulltext/ED497016.pdf)) + balance the need to cover the subject matter with the desire to prioritize game play ([Plass et al., 2010](https://www.gdcvault.com/play/1012682/The-Games-for-Learning-Institute))
- Gamification = involves the use of game elements, such as incentive systems, to motivate players to engage in a task they otherwise would not find attractive
- Game = "a system in which players engage in an artificial conflict, defined by rules, that results in a quantifiable outcome" ([Salen & Zimmerman, 2004](https://gamifique.files.wordpress.com/2011/11/1-rules-of-play-game-design-fundamentals.pdf))
- >> play and cog dev...

**Comments**

## [Rétaux, 2023 : Analyse d'ouvrage par Xavier Rétaux](https://doi.org/10.4000/activites.8450)

**THM** : play model by Sanchez E.

**Authors** : Xavier Rétaux

**Tags** : :jeu: :apprentissage:

**Notes**
- "Comment un apprentissage peut-il se développer alors que le joueur ne sait jamais vraiment ce qui vient de lui (le marionnettiste) et ce qui vient du jeu (la marionnette) ?"
- "Le modèle compte 6 niveaux :
	- culture organisationnelle de l’institution, 
	- conception, 
		- Penser le debriefied dès la conception du JV pour founrinr à l'ens les outils pour revenir à la'ctivité réelle de jeu pour la traduire en savoir
	- débriefing, 
		- "Ce débriefing peut interroger : s’il faut débriefer avec l’apprenant, ne revient-on pas à la situation initiale de formation ? Si le débriefing est refusé par l’apprenant comme il aurait pu refuser de suivre un cours classique, qu’apporte vraiment la situation de jeu ? C’est l’objet des chapitres initiaux de l’ouvrage."
	- orchestration, 
		- Ens = maître·esse du jeu, 2 buts :
			- Maintenir le "contrat ludique" (Duflo, 1997) - accepter un système de règles en échange des libertés que le système permet
			- Créer le "cercle magique" (Huizinga, 1951) - acceptation des règles arbitraire et coopérer pour permettre le jeu
			- => libre d'expérimenter et de perdre, et si possible avec les autres
	- jeu-play 2
		- Collab & Coop entre joueur·euse·s pour conflit socio-cognitif (REF)
	- jeu-play 1."
		- jeu = situation qui se développe dans un cadre particulier
		- jeu-artefact = game
		- jeu-activité = play
		- jeu épistémique = "jeu sérieux", utilisé "à des fins utilitaires ou de formation"
		- but = transfert de responsabilité de l'élève é l'enseignant : la dévolution (Brousseau)
- "L'activité des joueur·euse·s est une "économie du risque" (Duflo, 1997) (...) Quand il n'y a plus de risque, il n'y a plus de jeu."
- Le jeu est non-suffisant pour transmettre des savoirs
- Mais le jeu assure l'engagement de l'apprenant·e
- Livre pour concevoir des situations d'apprentissage par le jeu
 
## [(YT) Rigby, 2017 : The Freedom Fallacy: Understanding "Player Autonomy" in Game Design](https://www.youtube.com/watch?v=3vct13OhIio)

**THM** : 

**Authors** : Scott Rigby

**Tags** : :autonomy: :sdt: :gdc:

**Notes**
- Autonomy means that when we act, we do so volitionally
- Volition isn't independence or freedom to do whatever you want, it is wanting to do the things that you are doing
- Freedom Fallacy : Autonomy =/= freedom BUT = volition
- What are you doing to build volition and not just provide freedom?
- Structure beats out freedom in satisfying autonomy
- The key is not to just offer more, it is to offer a density of things that matter!
- Procedural Volition Quest :
	- Players verbs in the equations, as players make choices and act, those actions alter the game world procedurally and at scale
	- Focus on meaningful impact, focus on dynamicaly changing content, goal, and activity structures in response to players actions in ways that satisfy that basic needs
	- Latent variables, the players individual patterns might become important in weighting other variables – surfacing content and opportunity that is most likely to resonate with the player
- Engeneering deep volitional player experiences that lead to satisfaction

## [Roth, 2023 : Learning analytics ou comment lire les traces numériques pour aider les étudiant·e·s en difficulté](https://ciel.unige.ch/2023/04/learning-analytics-ou-comment-lire-les-traces-numeriques-pour-aider-les-etudiant-e-s-en-difficulte/#.ZEe7qgNuH1w.linkedin)

**THM** : 

**Authors** : Patrick Roth

**Tags** : :learning-analytics: :traces: :edtech: :moodle: :learning:

**Notes**
- Accompagner la mise en place d'enseignement ind. en adaptant le parcours d'apprentissage
- Contribuer à l'amélioration continue d'une formation en analysant l'évolution du niveau de compérences des étudiant·e·s
- Compétences visées d'une formation vs compétences demandées sur le marché du travail.
- Contrôle qualité d'une formation en monitorant le taux de rétention, l'évolution des notes ou la comparaison des résultats inter volées

## [Sanchez, 2022 : Le paradoxe du marionnettiste, jeu et apprentissage]()

**THM** : L'institutionalisation des savoirs se fait par le debriefing entre enseignante/formatrice et apprenante.

**Authors** : Eric Sanchez

**Tags** : :game: :play: :learning:

**Notes**
- Dans la peau de John Malkovich : Le joueur est un marionnettiste, ce qu'il advient dans le jeu dépend de ses décisions. Mais le devenir du joueur dépend également d'autres éléments contingents qui sont liés au jeu lui-même. C'est en effet ce jeu qui informe le joueur des cosnéquences de ses décisions. C'est ce jeu qui détermin les degrés de liberté offert au joueur qui est donc lui-même manipulé par sa marionnette. En s'engageant dans le jeu , troc liberté contre liberté encadrée par règles arbitraires du jeu. Dans le jeu, le marionnettiste peut apprendre de ses découvertes qu'il a lui-même suscitées, provoquées.
- Le paradoxe du marionnettiste vient souligner que l'idée que l'on peut apprendre en jouant contient une contradiction. Dès lors que les événemtns qui surviennent dans le jeu résultent de ses propres dévisions, si le jeu n'est qu'illusion, comment le joeuur peutéil penser que ces événements l'informent sur la réalité du monde?
- Vérifier au préalable qu'il y a jeu avant de montrer son "impact" (p.12)
- "le jeu n'est pas dans la chose mais dans l'usage qu'on en fait" (Henriot, 1969, p.24) (p.17)
- Catégorisation de joueureuses sur ratio succès/actions ? (p.17)
- Game (jeu structuré avec règles) vs. Play (libre) (Winnicott, 1971, 2002) (p.25)
- Selon Piaget, jeu apparaît comme un "secteur d'activités dont la motivation ne doit pas l'adaptation au réel mais au contraire l'assimilation du réel au moi, sans contraintes ni sanctions" (Piaget & Inhelder, 1966, 2008, p.59) (p.29)
- Jeu "sérieux" est détourné s'il a un fin ludique = faillite de l'apprentissage OU si'il y a disparition de la frivolité = ce n'est plus un jeu (p.30)
- Plus que de comprendre que le jeu (artefact) puisse donner lieu a de l'apprentissage, il faut plutôt mettre l'accent sur les interactions / la situation pour tenter de comprendre comment ces derniers peuvent devenir épistémiques (p.31)
- Jeu "épistémique" plus précis que jeu "sérieux" : souligne la situation qu'il permet d'élaborer, et prend en compte la subjectivité du jeu et vers les interactions qu'il permet.
- [Ludus](https://www.lepetitjournaldesprofs.com/reseauludus/), [Escape n'Game](https://www.cquesne-escapegame.com/), [S'Cape](https://scape.enepe.fr/), [Semper Ludens](https://www.semperludens.fr/)
- "Chocolate-covered broccoli approach" (Bruckman, 1999) (p.49)
- Link w/ AL2049 : September 12th game is a persuasive game, based on the idea that retroactions leads to developping an experience to the player to make her think about modalities of a situation
- "Les rétroactions produites par le jeu-game permettent une évaluation des conséquences des actions réalisées jusqu'à conduire à leur remise en cause, càd le développement de nouvelles connaissances, pour élaborer de nouvelles façons d'agir." (p.57) Lien avec Tuning effect (Lindgren & Schwartz, 2009). Lien avec Tuning, herding, conditionnement (Zuboff, 2021)
- Link w/ mem : "Ces retours sont rendus possibles par le fait que les jeux recueillent en permanence, de manière automatique, de sinformations sur ce que le joueur et capable de faire et donc sur son avancée dans le processus d'apprentissage" (p.58)
- Le jeu n'est qu'un modèle simplifié du monde, l'apprentissage par le jeu n'est odnc que selon ce modèle-ci. (p.64)
- Le caractère performatif du jeu résulte de différentes formes de coopérations : coopérer avec le concepteur du jeu (accept to play), avec adversaires (accept to compete) et avec coéquipiers (accept to teamplay) (p.69)
- Coopétition (Nalebuff & Brandenburger, 1997) : interactions se mettant en palce au sein du jeu (Sanchez, 2017) (p.70)
	- Jeu-play 1 relève d'un conflit/compétition et jeu-play 2 relève de la collaboration entre coéquipiers -> coopétition est l'alternance entre ces deux types de situations et donc la mise en place d'interaction épistémiques de deux types :
		- **Jeu-play 1** (joueureuse <-action/rétroaction-> jeu-game)  = nature actionnelle, permet un apprentissage qui relève d'un processus adaptatif
		- **Jeu-play 2** (joueureuse <-formulation/validation-> joueureuse  = nature interactionnelle entre co-équipières, apprentissage selon la formulatione t la validation des connaissances en jeu (p.76)
- Conflit socio-constructiviste pas seulement conflit mais que ce dernier suscite des interactions (p.77)
- Stealth assessment (Shute et al., 2916) (p.90)
- Infos sur les processus d'apprentissage sont plus complexes à afficher sur tableau de bord d'apprentissage (learning dashboard) (p.93)
- L'enseignant dispose de peu de temps pour analyser les erreurs et réussites de ses élèves (p.94)
- Privacy by design (Cavoukian, 2009) & Ethical by design (Leidner, 2017) (p.95)
- **Orchestration** (maîtresse du jeu <-> apprennante) = manière dont l'enseignant gère, en temps réel, des activités dans un contexte soumis à des contradictions multiples (Dillenbourg, 2013) (p.98)
- **Débriefing** (enseignante <-institutionnalisation-> apprenante) :
	- Apprentissage effectif que si le jeu est suivi d'un temps dédié à l'évocation par le joueur des découvertes qu'il a effectuées en jouant + accompagnée d'une réflexion sur ces découvertes (Garris et al., 2002) -> quitte le cercle magique pour le debriefing
	- Favorise réflexion et métacognition et transforme les connaissances implicites qui ont été dvpées dans le jeu deviennent explicites
	- Brousseau (1986) nomme ce processus institutionnalisation, permet un changement de statut de la connaissance : de subjective et située à objectif et indépendant. 
- Lederman debriefing model "voir/ressentir/savoir" :
	- Description des événements, prise de données sur l'expérience vécue
	- "Personnalisation", quelles émotions ressenties lors du jeu
	- Généralisation et application imaginé de l'usage futur
- Lederman (1992) on tools for debriefing :
	1. What were the learning **goals** ?
	2. How the **elements of the situations** did change the play experience ?
	3. What **strategy** to adopt to attain the learning goal ?
	4. What **material**, what questions ?
	5. How was it, what did I **feel** ?
- Play-design (instead of game-design) :
	- Conception d'une expérience de eu ou design d'expérience ludique + collaborative work
	- Genvo (2008) travaux s'inscrivent dans une vision qui n'essentialise pas le jeu et met la question de l'expérience vécue par le joueur au coeur du processus de conception
- Precepts (actions possibles/permises dans le jeu) – percepts (infos disponibles dans le jeu) – affects (émotions ressenties au cours du jeu) – concepts (concepts que le jeu conduit à mobiliser)
- Clim@ction et Insectophagia (Sanchez et al., 2010, Sanchez et al., 2017) résolution passe par la collaboration, la confrontation et la négociation -> ill-structured problems (Jonassen, 2000; King & Kitchener, 1994) : résolution sans haut-degré de certitude et que plusieurs solutions peuvent être proposées, aucune n'étant ni certaine, ni vérifiable, y compris par des experts. (p.129)
- Résoudre de tels. problèmes est lié à son rapport au savoir ? épistémologie personnelle (Jonassen, 2000) : considère la connaissance et l'acte de connaître.
- Hofer & Pintrich (1997), 4 dimension pour l'épistémologie personnelle d'un individu :
	- Certitude : continuum de croyances de l'idée que la vérité absolue existe avec certitude à des positions où le savoir est considéré comme provisoire et changeant
	- Simplicité : caractère isolé ou intégré des connaissances = idée que les connaissances sont des faits que l'on peut connaître de manière discrète, sans liens avec d'autres, à celles où le savoir est relatif, contingeant et contextuel. => Link with complexity telle que définie par Morin (p.130)
	- Source de la connaissance : individu est acteur de la construction du savoir et prise de conscience progressive dans cette constr.
	- Jusstification : science justifie ou justification des savoirsou idées auxquelles on adhère.
- Visée : développement épistémique = de leur capacité à appréhender la complexité d'un problème, de s'identifier comme capable d'élaborer des solutions à ce problème et de développer une argumentation qui justifie les choix opérés
- Certains élèves satisfaits de contribuer à la production de solutions mettant en avant l'authenticité de la situation vécue pour justifier leur adhésion au jeu VS. rejet où la question d'une évolution des savoirs ne se pose pas et qu'on ne retire pas de savoirs
- Jouer le jeu, c'est adhérer à une épistémologie (p.131)
- Hofstadter & Sander (2013) : apprentissage résulte d'un processus qui permet à l'app d'identifier des relations analogiques entre différentes situations plutôt que d'une pure logique interprétative.
- Conception de jeux récréatifs (Plass et al., 2015; Salen & Zimmerman, 2004) et jeux éducatifs (Kalmpourtzis, 2018)
- **Conception** (chercheureuse <-transposition métadidactique-> praticienne en game jam :
	- Caractérisation du public cible – focus groupes => définition des objectifs pédagogiques du jeu
	- Présentation de ces objectifs pédagogiques + personas (p.135)
	- Etablissement d'une fiche descriptive d'un usager fictif du dispositif à concevoir : identification des contraintes et leviers de conception liés à l'autilisateur
	- Idéation : univers du jeu, narration ,énigmes, par la collaboration des participant·e·s non-expert·e·s du jeu
- Méthode de conception : Figure 13 - Processus de conception à voir (p.138)
- **Culture organisationnelle de l'institution** : à prendre en compte (p.155)
- Les jeux numériques consistuent des systèmes contraints par des règles (du jeu, normativité du jeu-game) mais qui, généralement, offrent néanmoins une grande liberté à la joueuse (liberté du jeu-play)
- Ce modèle décrit deux jeux épistémiques emboîtés : 1. apprenante qui fait l'expérience d'une situation ludique et apprendre de cette expérience. 2. Celui des conceptrices qui de manière collaborative, vivent une expérience qui peut éalement prendre une forme ludique. Ces deux cas se traduisent par une expérience qui, si accompangées d'une travail métacognitif, peut avoir une valeur épistémique
- La conversion des connaissances constitutives de l'expérience que le joueur aura vécue à travers le jeu en savoirs institutionnalisés et mobilisables dans des situations analogues nécessite une accompagnement qui doit justementêtre apporté par une représentante de l'institution, formateurice ou enseignante. Faire le pari de l'égalité de l'intelligence et de l'émancipation intellectuelle en utilisant l'apprentissage par le jeu, ne signifie pas apprendre de soi-même, sans maître qui enseigne.
- On n'apprend pas en jouant, on apprend en portant un regad réflexif sur une expérience ludique, une expérience métaphorique de la vie, une expérience de soi.

## [Scheffel et al., 2014 : Quality indicators for Learning Analytics](https://www.jstor.org/stable/pdf/jeductechsoci.17.4.117.pdf?acceptTC=true&coverpage=false&casa_token=cFakWMF1tDoAAAAA:3KKH7ZKX7S-VJ8ouwNmwJpn_qAG622yqnPTsZ05LNB_PJujTmNJ-ZOG9cY9k48L161f5HS_h3CY9Q_kIMrpUZLk3B57-qNkHAUwcfPa16sMjBYlMEes)

**THM** : Quality of LA tool depends on objectives, learning support, learning measures and output, data aspects and organisational aspects

**Authors** : Maren Scheffel, Hendrik Drachsler, Slavi Stoyanov and Marcus Specht

**Tags** : :learning-analytics: :quality-indicator: :framework:

**Abstract** :This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts from the field of learning analytics. The outcomes of this study are further extended with findings from a focused literature review.

**Notes**
- Group Concept Mapping (GCM) :
	1. Generation of ideas
	2. Sorting the collected ideas into clusters
	3. Rating the ideas according to several values (Importance and feasibility)
- "One specific quality indicator to evaluate the effects of learning analytics is...", 8-cluster solution :
	1. Data: open access
	2. Data: privacy
	3. Acceptance & uptake
	4. Learning outcome
	5. Teacher awareness
	6. Learning performance
	7. Learning support
	8. Student awareness
- Quality indicators for LA to help standardise the evaluation of LA tools :
	1. Objectives : Awareness, reflection, motivation, behavioral change
	2. Learning Support : perceived usefulness, recommendation, activity classification, detection of students at risk
	3. Learning measures and output : Comparability, Effectiveness, efficiency, helpfulness
	4. Data aspects : transparency, data standards, data ownership, privacy
	5. Organisational aspects : availability, implementation, training of educational stakeholders, organisational change
- !!! : More research- than practice-oriented

## [Stepping Outside the Ivory Tower: How Can We Implement Multimodal Learning Analytics in Ecological Settings, and Turn Complex Temporal Data Sources into Actionable Insights?](https://ris.utwente.nl/ws/portalfiles/portal/422493657/ISLS2024.pdf#page=340)

**THM** : 

**Authors** : Bertrand Schneider, Richard Davis, Roberto Martinez-Maldonado, Gautam Biswas, Marcelo Worsley, N Rummel

**Tags** : :multimodal-learning-analytics: :learning-analytics:

**Abstract** : The field of Multimodal Learning Analytics (MMLA) is expanding, allowing researchers to capture rich, fine-grained data on learning processes in a variety of learning environments. High-quality process data can open the door for new insights on how people learn, creative interventions to support them, and build the foundation for personalized learning platforms. There is, however, a growing recognition that there is a lack of ecological implementations in MMLA (Cukurova, Giannakos & Martinez-Maldonado, 2020): the vast majority of projects are lab-based, which limit the generalizability and impact of multimodal sensing in education. This symposium brings together researchers who have used MMLA methods in the wild. The panel will discuss obstacles to the use of multimodal data in real-world settings, share lessons learned from current projects, and propose productive next steps for the field to become more ecologically relevant.

**Notes**
- MMLA seeks to understand how different modalities contribute to learning, how they intersect and influence each other; and helps in designing effective Lg env.
- [3D reconstruction algorithm](github.com/WildflowerSchools/poseconnect)
- Teachers' concerns about how introducing LA tools into exercise sessions might degrade env of trust and safety that they had worked hard to cultivate
- 
- Lessons learned:
	- If possible find partners who are working on the same problem
	- Work with students to undertsand their level of comfort with data collection tools and which measures need to be taken to create a safe and trusted learning env
	- Make data collection process transparent so that students can opt-out
	- Avoid collecting data that impacts privacy (e.g., speech)
	- Adopt participatory design methodologies to create useful platforms for learners and teachers
	- Do not underestimate the technical know-how and infrastructure required to collect and process MM data
	- Focus on formative (and not summative) assessment

## [Seif El-Nasr, 2013 : Game Analytics: Maximizing the Value of Player Data](https://link.springer.com/book/10.1007/978-1-4471-4769-5)

### [CHAPTER 12 : Drachen et al., 2013 – Game Data Mining](https://doi.org/10.1007/978-1-4471-4769-5_12)

**THM** : Re-read if interested in techniques of game data mining

**Authors** : Drachen, A., Thurau, C., Togelius, J., Yannakakis, G. N., & Bauckhage, C

**Tags** : :game-analytics: :data-mining:

**Abstract** : During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.

**Notes**
- Develeop better AI-controlled opponents or make games that adapt to the player ([Charles and Black, 2004](https://www.cp.eng.chula.ac.th/~vishnu/gameResearch/AI_november_2005/DynamicPlayerModelling.pdf), [Thurau et al., 2007](https://doi.org/10.1504/IJISTA.2007.012489), [Missura & Gärtner, 2009](https://link.springer.com/chapter/10.1007/978-3-642-04747-3_17), [Pedersen et al., 2010](https://doi.org/10.1109/TCIAIG.2010.2043950), [Yannakakis and Hallam, 2009](https://doi.org/10.1109/TCIAIG.2009.2024533)
- Cross Industry Standard Process for Data Mining (CRISP-DM)
- https://gameanalytics.com/blog/
- [Using AI for Game UX](https://www.youtube.com/watch?v=Lc4b7fqyOGQ) :
	- Why people do things?
	- How do we report GA in such a way, that all the different stakeholders can utilize and drive action from it ? How to communicate and how do we drive action X ?

### [CHAPTER 19 : Seif El-Nasr – Visual Analytics Tools – A Lens into Player’s Temporal Progression and Behavior](https://link.springer.com/chapter/10.1007/978-1-4471-4769-5_19)

**THM** : Map with circles and paths to show time, interaction, items, paths

**Authors** : Magy Seif El-Nasr Ph.D., André Gagné, Dinara Moura & Bardia Aghabeigi Ph.D.

**Tags** : :visual-analytics: :temporal:

**Abstract** : As argued in previous chapters, developing engaging interactive new media experiences, including virtual worlds, multi-player or single-player games, involves understanding the target market. Telemetry and metrics can provide a powerful method to enable designers and other industry professionals to gain insight about their users. Previous chapters have discussed telemetry analysis extensively (see Chaps. 4, 5, 7, 9, 11, 12, 13, 14, 15, 16, and 17). Chapter 18 introduced the Information visualization field and game visual analytics, specifically. In this chapter, we extend the arguments made in previous chapters, specifically investigating the use of visual analytics systems that reveal player behaviors over time, where we emphasize the temporal dimension as key to revealing information that indicate causes for specific behavior, and they may also give developers more insight about popular patterns of behaviors.

**Notes**
- Heatmaps but cannot take temporal activity
- Visualizing Time Spent in Each Area : Knowing how much time players spent playing in a particular area is crucial to hypothesize whether players had problems in an area or not.
- Visualizing Interaction with Characters : KNowing what players do in each area of the game is also important for understtading player behavior
- Visualizing where maps are activated
- Visualizing items collected
- Visualizing players path

## [Sloane, 2017 : ‘Where no man has gone before!’ – Exploring new knowledge in design-based research projects: A treatise on phenomenology in design studies](http://dx.doi.org/10.15460/eder.1.1.1026)

**THM** : As researchers working in ecological conditions (e.g., schools), there are 'learning arrangements', and Rrs need to accept the rules of a different life-world = adapting to the situation

**Authors** : Peter F. E. Sloane

**Tags** : :first-person-perspective: :life-world-approach: :design-based-research: :ecological-conditions-experimentation:

**Abstract** : Design-based research (DBR) is a programme where researchers co-operate with practitioners to work out new solutions. In DBR researchers interfere in daily life and participate in practitioners’ working processes. One open question is: What kind of knowledge can be generated in these projects? My starting point here is a DBR project in vocational education and training in Germany which is used for an investigation of the epistemological background of this kind of research enterprise. The characteristics of DBR are reflected on the basis of phenomenological and hermeneutical approaches. The basic assumptions of these concepts are introduced and applied to the DBR approach to show how DBR generally works and how, specially, features of DBR like participation in daily life, co-operation with practitioners, gathering knowledge in the field a. s. o. can be handled. The line of argumentation in this contribution is a radical switch between practical questions in daily work in DBR on one hand and theoretical re-assurance on the other hand. For researchers, DBR is an enterprise in a new world. The analytical paradigm does not prepare the voyagers for this journey. Therefore the non-analytic continental tradition of philosophy has to be re-discovered.

**Notes**
- What kind of knowledge can be generated with DBR ?
- Two results to be distinguished :
	- A design/prototype : solution to a problem, a proven tool
	- Knowledge is generated : declarative or insight into organisational processes or knowledge about how things work in school and how they should be done
	- => Researchers and practitioners learn how to design = 'Design Principles' (Euler, 2014)
- Researchers are confronted with political, organisational and instructional requirements
- The working contexts bring researcher with teachers, administrators, politicians, etc., and they have to look for agreements with these actors
- Researchers are not only explainers but consultants
- In society, specification os sciences and studies (academic disciplines) are 1) knowledge archives of society : researchers collect knowledge + 2) transfer this knowledge to society : e.g., teaching is the mechanism of turning knowledge into practice.
- Hermeneutics means to find out what happens to yourself when you are engaged : mvmt from the perspective on an object (Kant) to concernment by an object (Dilthey). In the rational case, a Rr explains what he observes and in the second case he describes what affects him
- Life-World approach :
	- Researchers and practitionners live in different organisations with varying rules and obligation - researchers who go into the field to observe schoolwork change into a different environment
	- Life-world = a counter-project to the natural eworld in (natural) science (Husserl, 1996, 1962)
	> "The world as immediately or directly experiences in the subjectivity of everyday life, as sharply distinguished from the objective "worlds" of the sciences, which employ the methods of the mathematical sciences of nature; although these sciences originate in the life-world, they are not those of everyday life" (Life-world, 2016)
	- Meaning for DBR ? School work does not exist as an 'unchangeable' object. It emerges for the researcher through his interactions with actors in school. Interaction bw researchers and practitioners is the basis to construct knowledge about this 'school world'
	- Learning Arranements = researchers informed this practitionrs and moderated the focus groups who had to develop this prototype.
- Inter-subjectivity, or how the others and their perceptions are conceptualized in a phenomenological concept :
	- On Empathy : Researchers as counsellors re-construct the problems, work out the background of the problem in coop with the teachers and dvp strategies together to solve the problem => research journal and discussion groups = texts are produced
	- On Textual Reality : We share situations, thus interpretations and backgrounds. Societal reality is mediated textually (Soeffner, 1983, 1986). A textual form or it does not exist (Derrida, 1986).
	- On the Second-Person Perspective = intention to establish connections between the insight view (FP) and the outside explanation (TP) : communication where the researcher's third perspective has to be match with the practitioners' first perspective
- Researchers need to accept the rules of a different life-world, and understand the culture in this 'school' world : by locating the acting persons and implicit hierarchy, and the sometimes informal obligations in this world
- Rrs had to learn how the participating teachers define their roles in the process.
- DBR projects' iterative cycle : 1) identifying and clarifying the problem, 2) theoretical foundation, 3) development of the design, 4) testing and formative evaluation of the design, 5) creation design pricniples, 6) itervention and summative evaluation
- Working in the field means changing the life situation and makes it necessary to cope with the rules that exist in his field, allso it includes learning about the relevant knowledge and undertanding the cultural settings
- Participating in practice gives researchers the possiblity to experience social interaction and find out, maybe in one of these eureka moments why things work the way they do because all of a sudden, an action pattern may become visible. In a more pragmatic sense, the researchers have to discover the logic of the narratives
- Hermeneutical field 1: com and coop in the arena bw researchers and practictioners -> refers to commitments in the arena, step of gathering information, lot of doc texts produced and interpreted
- Hermeneutical field 2 : com and coop in the scientific community bw Rr - link back the expeirences won in the arena
- Joint time limited, both people from different life-worlds. 

## [Sokolov et al., 2020 : Serious video games and virtual reality for prevention and neurorehabilitation of cognitive decline because of aging and neurodegeneration. Current opinion in neurology, 33(2), 239-248.](https://journals.lww.com/co-neurology/Fulltext/2020/04000/Serious_video_games_and_virtual_reality_for.14.aspx)

**THM** : State of the art in Healthy Older Adults (HOA)/MCI for Computerized Cog Training, SVG, Exergames, VR

**Authors** : Sokolov A. A., Collignon A, Bieler-Aeschlimann M

**Tags** : :aging: :cognitive-neurorehabilitation: :computerized-cognitive-training: :training: :exergames: :neurotechnology: :serious-game:

**Abstract** :Cognitive decline because of aging and neurodegeneration has become increasingly prevalent. This calls for the implementation of efficacious, motivating, standardized and widely available cognitive interventions for the elderly. In this context, serious video games and virtual reality may represent promising approaches. Here, we review recent research on their potential for cognitive prevention and neurorehabilitation of age-related cognitive decline and mild cognitive impairment (MCI). The majority of currently available data in this evolving domain lacks the methodological quality to draw reliable conclusions on the potential of novel technology for cognitive training in older people. However, single well designed randomized controlled trials have reported promising effects of cognitive interventions involving serious video games and virtual reality. The cognitive benefits of exergames promoting physical exercise with and without combined cognitive training remain unclear. The immersion into stimulating and motivating environments along with training content based on neuroscientific and neuropsychological models may represent a significant advance as compared with conventional computerized cognitive training. Additional research with sound methodology including sufficient sample sizes, active control groups and meaningful outcome measures of everyday function is needed to elucidate the potential of serious video games and virtual reality in multifactorial neurorehabilitation of cognitive decline in aging and neurodegeneration.

## [Stuckler, 2021 : 5 steps to write a research paper in a weekend](https://www.youtube.com/watch?v=yt6zuQbETMI)

Comment made by @ruampimentel883
1. Get everything in place
    1. Analysis Ready
    2. Table Ready
    3. Figure
    4. Writing is the last 10% of the project
2. Write the paper from inside out
    1. Don’t do it from start to finish (intro, method, results, discussion)
    2. Start with the method (easy to write)
    3. Then results
        1. Describe tables, figures and key findings to tell a clear and coherent story
3. Write the conclusion
        1. Recapitulate the findings (key findings) 1st paragraph
        2. List Limitation (2nd paragraph)
        3. What are the implication for future research? 
        4. What are implication for other actions on the field? (if any)
4. Now, Introduction
        1. Why is it important? Why should anyone care?
        2. What is missing? What is the limitations?
        3. What is the value of this study?
5. Not be a perfectionist
    1. Just write… then edit

## [Szilas, 2022 : Serious game design in practice: lessons learned from a corpus of games developed in an academic context](https://doi.org/10.1145/3555858.3563283)

**Authors** : Nicolas Szilas

**Tags** : :learning-games: :serious-games: :maltt: :intrinsic-integration:

**Abstract** : This article consists of a post-analysis of twenty-four learning games, developed by master students within a dedicated course, over a period of twelve years. From this longitudinal qualitative analysis of serious game development, we are able to identify critical factors that make these games successful or not. We have grouped these factors into three axes: project organization, game mechanics and graphics. The variety of games produced in this academic context opens the way toward a more systematic analysis of this corpus, aiming at producing useful guidelines to design better serious games in an efficient manner.

**Notes**
- More than a quizz, *contextualization* allow the exercise to be more meaningful at the level of the fictional world, and at the level of the narrative
- *Enriched mechanics* = the main mechanic is turned into more than a simple quizz
- *Systemic mechanics* = a global quest influenced by actions taken during the game
- *Graphics* :
	- Use ready-made assets
	- Use an authoring tool that embeds the graphics
	- Use graphical rendering effects
	- Use specialized graphic design tools

## [Szilas, 2024 : Intelligence Artificielle dans l’éducation : le point de vue de la Terre](https://cdn.discordapp.com/attachments/1182770506047443025/1228290972576780380/Szilas_IA_Terre_LudoviaCH2024.pdf?ex=662b821c&is=66190d1c&hm=f15129449d707b9e3317d3b4972265ea0f602cf1a250b232e3155cbe4a538100&)

**THM** : Quels usages de la technologie?

**Authors** : Nicolas Szilas

**Tags** : :intelligence-artificielle: :écologie: :pollution-numérique: :sobriété-numérique: :futur:

**Abstract** : L’arrivée de l’Intelligence Artificielle (IA) est perçue comme une révolution. Nous estimons que l’IA est avant tout une nième technologie qui s’offre au monde de l’éducation, dont les impacts environnementaux ne font qu’aggraver une situation déjà alarmante : une pollution numérique considérable à tous les niveaux, et qui à terme conduit à une fin du numérique, par épuisement des ressources ! Loin des discours des industriels, c’est donc avec parcimonie et sobriété qu’il faut envisager l’utilisation de l’IA dans l’éducation. Pour ce faire, le monde académique doit jouer un rôle de premier plan, et résister à cette tentation de la numérisation à tout va, dont l’IA fait partie

**Notes**
- L'IA dans l’éducation constitue une nième technologie, au même titre que l’audiovisuel, l’EAO1 , les hypertextes, le multimédia, les tuteurs intelligents, les simulations et micromondes, la communication médiatisée via Internet, les jeux vidéo pédagogiques, les tableaux blancs interactifs, la réalité virtuelle, la réalité augmentée. On objectera que cette liste est hétérogène et revêt des réalités différentes, mais elle illustre le fait qu’à chaque époque une technologie surgit et accapare l’attention (au moins des chercheurs en technologie éducative), qu’elle pénètre ou non l’école elle-même.
- Avantages "potentiels"
- C’est précisément l’enjeu du numérique dans l’éducation d’une part d’identifier des usages pertinents et d’autre part, à travers ces usages, de donner des exemples positifs d’utilisation du numérique, en contraste avec les usages hors classe, contribuant ainsi à l’éducation au numérique.
- On trouvera l’argument que des LLMs moins énergivores que ChatGPT existent ou sont à l’étude et que de grands progrès d’optimisation vont voir le jour très prochainement. Cependant, l’histoire montre que de telles optimisations, aussi souhaitables soient-elles, seront largement compensées par une augmentation importante d’utilisation justement permise par ces optimisations ; c’est ce qu’on appelle l’effet rebond
- Il est nécessaire, sans être réfractaire à cette technologie, d’identifier les usages qui sont parcimonieux et ceux qui ne le sont pas, et de s’abstenir de promouvoir ces derniers.
- Pourquoi l’université suit-elle ainsi la tendance et n’est pas ce qu’on pourrait attendre d’elle, à savoir un lieu (protégé car non soumis à des contraintes économiques) de réflexion à long terme et de dépassement de la simple tendance ? Car bercé par l'équation : innovation = numérique = futur.

## [Taylor et al., 2020 : The Paradox of Help Seeking in the Entropy Mastermind Game](https://doi.org/10.3389/feduc.2020.533998)

**THM** : availability of help allow less queries (bw 0 and 28%) vs. normal ; more help when more difficult

**Authors** : Nichola Taylor, Matthias Hofer and Jonathan D. Nelson

**Tags** : :help-seeking: :bayesian:

**Abstract** : Research on Bayesian reasoning suggests that humans make good use of available information. Similarly, research on human information acquisition suggests that Optimal Experimental Design models predict human queries well. This perspective contrasts starkly with educational research on help seeking, which suggests that many students wait excessively long to ask for help, or even decline help when it is offered. We bring these lines of work together, exploring when people seek help as a function of problem state in the Entropy Mastermind code breaking game. The Entropy Mastermind game is a probabilistic version of the classic code breaking game, involving inductive, deductive and scientific reasoning. Whether help in the form of a hint was available was manipulated within subjects. Results showed that participants tended to ask for help late in the game play, often when they already had all the necessary information needed to crack the code. These results pose a challenge for some versions of Bayesian and Optimal Experimental Design frameworks. Possible theoretical frameworks to understand the results, including from computer science approaches to the Mastermind game, are considered.

**Notes** : 
- people often do not utilize opportunities for help or ignore them (Aleven et al., 2003)
- pattern : students would attempt a task, they would be provided with feedback and th offer of help, and then they would decline the help (du Boulay et al., 1999)
- people are motivated to find the best explanation to explain the available data (Chater et al., 2006; Kharratzadeh and Shultz, 2016)
- Bayesian models suggest that humans are rational about learning and inference and will use information to ask questions that will maximize their knowledge (Eberhardt and Danks, 2011) BUT criticism !!!
- help condition = ring the bell when you feel stuck.
- n = 20
- 95% bootstrap CI for the diff of queries : The availability of help reduce the number of queries between 0.12 and 2.68 (mean= 10.66 normal, 9.28 help) per game
- mastermind game : random better than systematic (0.66 to 1.83 Bootstrap)

## [Tchounikine & Tricot, 2011 : Environnements informatiques et apprentissages humains](https://hal.archives-ouvertes.fr/hal-00742999)

**THM** : Overview of EIAH

**Authors** : Pierre Tchounikine et André Tricot

**Tags** : :EIAH: :ITS:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Environnement Informatique pour l'Apprentissage Humain (EIAH) = Intelligent Tutoring System (ITS)
- Définition :
	- En tant que système informatique : programme destiné à être utilisé par les apprenants impliqués dans une situations d'eneignement et à accompagner ou susciter leur apprentissage
	- En tant que champs scientifique : ensemble des travaux visant à comprendre les processus de construction des EIAH et les phénomènes d'apprentissage liés à ces environnements informatiques
- La vision actuelle (constructiviste) est donée sur l'idée que l'AP est une modification de la capacité à réaliser une tâche sous l'effet d'une interaction avec l'environnement.
- Rôles que la machine peut tenir :
	- Présentation de l'information, Traitement de l'information, Communication
- Types de logiciels qui entrent dans EIAH :
	- Tuteurs intelligents : doit disposer d'une modélisation des connaissances du domaine d'apprentissage.
	- Hypermédias : rôle de représentation imagée.
	- Micromondes : simulation, immersion dans un monde virtuel.
	- Technologies de l'information et de la communication (TIC)
	- Environnement informatique pour l'apprentissage collaboratif (EIAC)
- Un EIAH est la conjonction d'une intention didactique ou pédagogique et d'un environnement informatique. COncevoir un EIAH, c'est concevoir, organiser et gérer un dispositif impliquant des acteurs humains et un ou plusieurs artefacts informatiques.
- Le problème est la mise en oeuvre de situations d'apprentissage nécessitant la conception d'un artefact informatique spécifique, qui doit présenter des propriétés ou des caractéristiques explicitement liées à l'intention didactique ou au contexte pédagogique.
- Il n'existe pas actuellement de processus (ou ensemble de propositions de processus) de conception des EIAH plus ou moins normalisé faisant l'objet d'un consensu comme cela peut l'être pour les systèmes d'information.
- Description du processus de conception d'un EIAH :
	- Points d'entrée :
		- Technologie particulière - en lien avec de "nouveaux possibles" qui permet de créer des situations d'apprentissages "intéressantes"
		- Processus d'ingénierie informatique, normes et standards - comment ces langages, avec leur syntaxe et sémantique, permettent de modéliser et/ou de mettre en oeuvre la situation pédagogique visée
		- Théorie issue des sciences humaines et sociales - considéré comme fondement pour concevoir des situations d'apprentissages et artefacts informatiques associés
		- Analyse didactique des connaissances disciplinaires - afin de déterminer les propriétés et caractéristiques de ces connaissances et de leur apprentissage, et sous quelles conditions les situations d'ap peuvent être transposées dans un environnement informatique tout en conservant leurs caractéristiques favorables à la construction du sens
		- Acteur enseignant ou tuteur - qui veut faciliter la tâche, co-construction des objets et notions qui doivent être partagés par le système et ces enseignants
		- Résultats empiriques dans le domaine de l'apprentissage - pour dégager des guides de conception
	- Etapes :
		- Inventer l'EIAH - imaginer, penser l'env. info, les users, contexte d'utilisation, utilisations attendues
		- Spécifier - propriétés et caractéristiques
		- Réaliser - build models and code
		- Expérimenter - étuder sa correspondance aux attentes
		- Raffiner & Faire évoluer - modifications en fonctions des expérimentations en milieu écologique
- Enseigner est une ingénierie, càd, une utlisation raisonnée, à des fins de conception, de connaissances scientifiques. L'ingénieur·e a pour tâche de concevoir un dispositif qui permette d'atteindre un objectif donné, avec un certain nombre de ressources et de contraintes. Il conçoit une solution optimale.
- Théorie de la charge cognitive :
	- Prend en compte trois grands types de facteurs (niveau de connaissance des apprenants; complexité du contenu; type de contenu).
	- Si les prior knowledge sont faibles, la conception de la situation d'enseignement doit s'attacher à réduire la charge cognitive inutile, en favorisant la réussite de la tâche, utilisant les illustrations pertinentes, expliquant les illustrations à l'oral ou écrit (pas les deux), ne donnent pas d'explications inutiles, concevant les leçon directives et utilisant des problèmes résolus en faisant disparaître progressivement les explications et les solutions
- Elaborer de nouvelles connaissances sur l'apprentissage humain :
	- Effet de modalité (ou de réduction du partage attentionnel) : correspond aux document pédagogiques multimodaux au sein desquels les diverses sources d'information qui requièrent d'être mies en relation pour être comprises sont présentées selon els modalités sensorielles différentes (auditive et visuelle)
		- En intégrant physiquement ces informations et en les présentant selon des modalités sensorielles différentes, on facilite l'apprentissage
		- ex. lire un texte explicatif tout en regardant des illustrations liées à ce texte risque de surcharger le canal visuel, entraînant une diminution des performances de compréhension VS. écouter un discours + regarder illustration n'entraînera pas de surcharge de traitement au niveau perceptif
		- => connaissances nouvelles pour la conception de supports-documents d'apprentissage
	- Effet de redondance :
		- A l'inverse, quand une seule source suffit, l'ajout d'info redondantes détériore l'apprentissage (Cerpa, Chandler et Sweller 1996, Chandler et Sweller 1991, 1996, Kalyuga, Chandler et Sweller 1999)
		- => [Le Bohec & Jamet (2005)](https://www.cairn.info/revue-le-travail-humain-2005-2-page-97.htm)
	- L'analyse des erreurs
	- Conception pour les activités instrumentées :
		- L'environnement information est à la fois une ressource pour 'activité de l'apprenant et un facteur d'impact sur l'activité, puisque l'EIAH embarque une intention didactique et que ses propriétés ne sont pas contingentes.
		- Un EIAH rend certaines choses possibles et d'autres pas, et ces éléments ont été étudiés en relation avec des objectifs pédagogiques précis.
		- Il y a donc un enjeu important à la compréhension des mécanismes d'instrumentation liés aux EIAH et à l'intégration de cette dimension dans leur processus de conception, à la fois d'un point de vue théorique et pratique (conception itérative, continuée dans l'usage, etc)
	- Evaluation des systèmes :
		- Difficultés :
			- 1) Déterminer si l'objectif a été atteint et d'identifier les facteurs d'influence.
			- 2) Disposer de méthodlogies d'évaluation scientifique : méthodes d'évaluation labo vs ecologique.
			- 3) Mener une évaluation qui puisse être interprétée pour rpocéder à une réingéniérie du système produit : il faut établir des relations de cause à effet entre les propriétés des objets construits et d'analyse
		- Questions :
			- 1) Différence entre pédagogie informatisée vs. non informatisée ou situation pédagogique en tant que telle ?
			- 2) Légitimité de "faire évaluer" un EIAH lorsque cette dimension de l'évaluation n'a èas été un élément pris en compte dès la conception par les constructeurs ?

## [Tondello et al., 2016 : The Gamification User Types Hexad Scale](https://dl.acm.org/doi/abs/10.1145/2967934.2968082)

**THM** : Gamification scale that correlates with BF

**Authors** :Gustavo F. Tondello, Rina R. Wehbe, Lisa Diamond, Marc Busch, Andrzej Marczewski, Lennart E. Nacke 

**Tags** : :gamification: :assessment:

**Abstract** : Several studies have indicated the need for personalizing gamified systems to users' personalities. However, mapping user personality onto design elements is difficult. Hexad is a gamification user types model that attempts this mapping but lacks a standard procedure to assess user preferences. Therefore, we created a 24-items survey response scale to score users' preferences towards the six different motivations in the Hexad framework. We used internal and test-retest reliability analysis, as well as factor analysis, to validate this new scale. Further analysis revealed significant associations of the Hexad user types with the Big Five personality traits. In addition, a correlation analysis confirmed the framework's validity as a measure of user preference towards different game design elements. This scale instrument contributes to games user research because it enables accurate measures of user preference in gamification.

**Notes**
https://sydologie.com/2022/12/hexadeau-de-noel/

## [Tricot, 2017 : Quels apports de la théorie de la charge cognitive à la différenciation pédagogique ? Queles pistes conctrètes pour adapter des situations d'apprentissage.](https://www.cnesco.fr/wp-content/uploads/2017/03/170313_16_Tricot_def.pdf)

**THM** : Paradoxe de la demande d'aide - plus loin du but d'apprentissage, plus besoin de guidage et inversement - check table !

**Authors** : André Tricot

**Tags** : :cognitive-load: :différenciation-pédagogique: :learning:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Pour apprendre à l'école : moyens (tâches sur supports) =/= but (élaboration des connaissances)
	- Pas la même chose que les apprentissages adaptatifs où moyen = but.
- Trois sources d'exigence cognitive de chaque situation d'apprentissage :
	- charge intrinsèque (intrinsic cognitive load) - liée aux informations à traiter pour réaliser la tâche / au cours de l'apprentissage ;
	- charge extrinsèque (extraneous cognitive load) - liée aux informations inutiles (ex. mauvaise présentation ou info irrelevantes) ;
	- charge essentielle (germane cognitive load) - liée à l'apprentissage lui-même, càd l'organisation mentale des informations et leur intégration aux autres connaissances utiles
- "Effets" pour réduire charge extrinsèque, voire intrinsèque :
	1. *Effet de non spécification du but* - il est généralement plus efficace de ne pas trop spécifier le but d'un problème -> enlever la charge intrinsèque de l'exigence de la tâche
	2. *Effet du problème résolu* - généralement plus efficace d'étudier un problème résolu que de résoudre un problème pour apprendre -> enlever la charge intrinsèque de résolution de problème
	3. *Effet du problème à compléter* - alterner entre problème résolus, à résoudre et à compléter
	4. *Effet d'attention partagée* - présenter la solution du problème de manière intégrée dans la figure, réduisant la charge extrinsèque
	5. *Effet de modalité* - quand intégration multiples sources d'information, plus efficace de présenter ces sources dans des modalités différentes que de présenter le matérielle d'une façon physiquement intégrée
	6. *Effet de redondance* - intégration physique ou effet de modalité efficace si les différentes sources d'information sont complémentaires
	7. *Effet de l'interactivité entre éléments* - les techniques précédentes sont efficaces si la charge cognitive intrinsèque est élevée (mise en relation de nombreux éléments pour comprendre la situation)
	8. *Effet de variété des exemples* - avec parcimonie en début d'apprentissage, et non très différents dès le début
	9. *Effet de disparition progressive du guidage* - séquence d'enseignement efficace : problèmes initialements résolus > problèmes à compléter > problèmes proposés sans aucune aide
	10. *Effet d'immagination* - immaginer qu'on réaliser ou comment va réaliser ~= mise en oeuvre effective
	11. *Effet d'auto-explication* - s'expliquer à soi-même >= essayer de faire ou comprendre (Chi et al., 1989)
	12. *Effet de l'information transitoire* - ex. video vs. texte/image impose à l'élève, qui ne peut pas réguler son activité, de réaliser la tâche / traiter le support en même temps qu'il apprend.
	13. *Effet de mémoire de travail collectif* - quand tâche complexe, travail en grp > individuel; qd tâche simple : ind > grp.
	14. *Effet de renversement dû à l'expertise* - les effets précédents fonctionnent si les apprenants ont peu de connaissances dans le domaine - si avancés ou experts alors inefficaces
		- Aka *Achievement treatment interactions* (Tobias, 1976)
		- Permet de prédire que les élèves avancés seront plus efficaces dans leur apprentissage avec un probème à résoudre vs. moins avancés plus efficaces avec pb présenté avec solution
		- Elèves  différents peuvent réaliser le même apprentissage, même provlème, mais présenté différemment => différenciation sur la tâche et le support mais pas sur la connaissance à apprendre !
		- Pour tenter de contourner cette contrainte, travaux portés sur laisser les élèves choisir eux-mêmes la version la plus adéquate :
			- *"Mais effet du "paradoxe de la demande d'aide" (Nelson-Le Gall, 1985) : ce sont les élèves qui ont le plus besoin d'aide dans la réalisation d'une tâche scolaire qui sont les moins pertinents dans la demande d'aide, soit ils "sur-demandent", soit "sous-demandent" de l'aide. Le coût social et personnel de la demande d'aide (ex. exposer le fait que l'on n'est pas compéttent puisqu'on demande l'aide) n'étant pas étranger à ce paradoxe"*
			- *Plus élèves loin du but d'apprentissage, plus ont besoin de guidage.*
			- *Réciproquement, plus sont proches du but d'apprentissage, le fait de moins les guider, les conduire à produire les inférences ou les hypothèses, mais aussi à interagir avec des pairs, améliore le apprentissages (Chi & Whylie, 2014 ; McNamara et al., 1996 ; Fiorella & Mayer, 2015)*
 
Tableau tiré de Tricot (2017) :

| Avec les élèves les plus en difficultés pour l’apprentissage visé                                                                                                                                                                                                                                                                | Avec les élèves les plus avancés pour le même apprentissage visé                                                                                    |
| ---                                                                                                                                                                                                                                                                                                                              | ---                                                                                                                                                 |
| Ne pas trop spécifier le but du problème, indiquer plutôt à l’élève qu’il doit atteindre tous les buts qu’il peut atteindre, faire tout ce qu’il sait faire                                                                                                                                                                      | Spécifier le but du (même) problème                                                                                                                 |
| Donner à l’élève le problème résolu et lui demander d’étudier la solution<br>Alterner les problèmes résolus et les problèmes à résoudre<br>Donner le problème avec une solution partielle                                                                                                                                        | Donner le (même) problème à résoudre                                                                                                                |
| Intégrer physiquement les informations que l’élève devra mettre en relation mentalement pour rendre cette information intelligible<br>Éliminer toutes les informations inutiles ou décoratives<br>Présenter les sources d'information que l’élève devra mettre en relation dans des modalités différentes (auditive et visuelle) | Éviter la redondance : ne pas répéter inutilement ce qui peut être présenté une seule fois d’une seule manière                                      |
| Si l’information à présenter est complexe (beaucoup d’éléments et de relations), alors la présenter progressivement, partie par partie                                                                                                                                                                                           | Présenter le tout d’emblée plutôt que par parties, pour que l’élève puisse apprendre les relations entre les sections                               |
| Varier les exemples, avec parcimonie en début d'apprentissage                                                                                                                                                                                                                                                                    | Présenter l’information avec beaucoup de variabilité pour que l’élève puisse apprendre quelles variables sont pertinentes et quelles ne le sont pas |
| Faire disparaître le guidage progressivement                                                                                                                                                                                                                                                                                     | D’emblée, ne pas guider, laisser l’élève explorer librement                                                                                         |
| Demander à l’élève de mémoriser les relations les plus importantes                                                                                                                                                                                                                                                               | Demander à l’élève de s’auto-expliquer les relations les plus importantes                                                                           |
| Ne pas présenter d’information transitoire continue (oral, vidéo) ; présenter plutôt des informations statiques, faire des pauses aux moments pertinents et guider l’attention sur les parties pertinentes                                                                                                                       | Présenter de l’information transitoire continue (oral, vidéo)                                                                                       |
| Proposer du travail en groupe (selon un scénario précis) quand l’apprentissage visé est éloigné des élèves ; sinon, le travail peut être réalisé seul                                                                                                                                                                            | Si l’accès aux connaissances d’autrui est nécessaire, alors le travail en groupe est utile. Sinon, le travail individuel peut être mis en œuvre.    |
| Mettre en exergue ce qui est important<br> Expliciter les liens entre les parties d’un tout                                                                                                                                                                                                                                      | Ne pas tout expliquer : engager les élèves dans des activités de production d’inférences, d’hypothèses, de conjectures                              |

## [Tricot, 2021 : Le numérique permet-il des apprentissages scolaires moins contraints ? Une revue de la littérature](https://doi.org/10.3917/es.045.0037)

**THM** : Numérique est perçu comme exigences (niveau attentionnel, autonomie et efforts) + enseignant·e·s charge en plus. Mais permet d'alléger les contraintes de temps, lieu et de manière d'apprendre, mais différences interindividuelles. Quid des élèves plus fragiles et moins compétentes pour gérer leur temps, lieu et manière d'apprendre ?

**Authors** : André Tricot

**Tags** : :learning: :technology: :edtech:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Apprendre à l'école repose sur l'acceptation des contraintes de temps, de lieu, des savoirs à apprendre et de la manière d eles apprendre
- Enseigner, c'est les aménager pour permettre à autrui d'apprendre.
- Le numérique soulage-t-il la contrainte de **temps** ?
	- Apprentissage mobile résultats positifs sur la satisfaction des élèves (86% des publications analysées de la MA de Wu et al., 2012) - quid de l'apprentissage ?
	- La gestion du temps et de l'effort est le meilleur prédicteur de la réussite aux examens (Neroni et al., 2019) - étudiant·e·s non traditionnel·le·s > traditionnel·le·s dans gestion de temps d'apprentissage et plus persistants face aux défis
	- Elaboration, pannification, contrôle et régulation de ses propres apprentissages sont un bon prédicteur de la réussite aux examens (Neroni et al., 2019) - non-trad > trad
	- Numérique assouplit la contrainte temporelle mais déplace la responsabilité sur l'élève qui doit prendre en compte qu'apprendre des connaissances scolaires requiert du temps
- Le numérique soulage-t-il la contrainte de **lieu** ?
	- Aller au dela de la dichotomie présence/distance mais se focaliser plutôt sur la pédagogie
	- Numérique permet de concevoir des solutions efficaces, si le soutien aux efforts des élèves n'est pas oublié
	- Encore : la contrainte de lieu ne disparaît pas : elle doit être autogérée par l'élève
- Le numérique soulage-t-il la contrainte des **savoirs à apprendre** ?
	- Selon Anderson (2000), sa théorie d'apprentissage : on apprend quand le rapport bénfice/coût de l'apprentissage est >0. Les connaissances scolaires présentent souvent un rapport bénéfice/coût <0: elle ne sont pas utiles immédiatement, pas ici.
	- "Pour chercher une connaissance dont nous avons besoin, nous devons en effet savoir ce que nous ignorons"
	- "Le savoir scolaire est un pari sur l'avenir. Si nous n'apprenons pas aujourd'hui un savoir qui nous paraît innutile, nous n'aurons pas demain la connaissance qui nous permet de douter, de nous poser des questions."
	- Numérique comme une bibliothèque, apporte des données plutôt que des connaissances
	- Confère donc à l'école le rôle d'apporter aux apprenants de sinformations qui ne leur semble pas utile de prime abord.
	- "Plus la durée moyenne de la scolarité est longue au sein d'une société, plus les personnes peu diplômées son pénalisées socialmement, économiquement, professionnellement."
- Le numérique soulage-t-il la contrainte de la **manière d'apprendre** ?
	- Ecouter un cours ou regarder une vidéo :
		- Effet de l'information transitoire (transient information effect) - lorsque modalité de présentation permanente de l'information (écrit) est transformée en présentation transitoire équivalente (écrite) > détériore l'apprentissage (Leahy & Sweller, 2011)
		- Difficulté à prendre des décisions et revenir en arrière > surcharge cognitive (Roussel, 2020)
		- Avantage global moyen (d = 0.37) et faible (g = 0.23) des animations par rapport aux images statiques (Höffler et Leutner, 2007; Berney et Bétrancourt, 2016)
		- Quand mise à dispo des vidéos d'un cours obligatoire (Edwards & Clinton, 2019) : moins de présence, présence = meilleurs résultats à l'évaluation, 17.5% ne vont pas en cours ni watch video, same proportion go en cours et watch, contenus additionnels = réussite
		- Vidéo ou classes virtuelles = détériorent régulation et communication non verbale. Présence l'enseignant : pose question, jauge intérêt, ralenti, hausse/baisse voix, utilise gestions, expressions, illustre, mime, souligne, capte regard et attentnion, synchronise en fonction des réactions (van Wermeskerken & van Gog, 2017)
	- Prendre des notes :
		- Sur ordinateur = détériore qualité de prise de notes -> augmentation de la charge cognitive de la tâche de réalisation technique (Mueller & Oppenheimer, 2014)
		- Ordi en cours = distraction du web
		- Pas de certiture sur les +/- de chacune de ces façons de faire.
	- Lire un texte :
		- Léger avantage (g = 0.21) papier vs ordi (MA de Delgado et al., 2018)
		- Temps de lecture : Avantage lecture plus important si temps lecture limité (g = .26)
		- Rythme : Si lit à son rythme (g = .09)
		- Genre : Avantage papier si texte informatif (g = .27), rien si narratifs
	- Etudier un document multimédia :
		- Fausses croyances (Clark & Feldon, 2014), il est faux de croire que :
			- Docs multimédia améliorent apprentissage vs présence ou médias anciens
			- Docs mm sont plus motivants que d'autres supports
			- Docs mm permettent conception d'agents pédagogiques animés qui facilitent l'app
			- Tiennent compte des styles et améliorent l'app pour tous les élèves
			- Facilitent approches constructivistes via app par découverte ainsi qu'autonomie et contrôle
			- Mise en oeuvre pensée plus haut niveau
			- Apprentissage incident
			- Interactivité
			- Environnements et activités d'app authentiques
	- Demander de l'aide :
		- "Cette aide est bénéfiques pour la réalissation de la tâche, mais l'est aussi pour l'apprentissage (Karabenick & Newman, 2013)"
		- Différences interindividuelles importantes dans la compétence é formuler cette demande : élève plus âgés demandent plus explicitement (Puustinen et al., 2009)
		- Coût sociaux peuvent être moins importants, voire absents, dans des env numériques = anonymat (Lery Santos, 2018)
		- Mais ajoute coût : manipulation des outils et de la formulation d'une requête écrite (Huet et al., 2013)
	- Rechercher de l'information pour préparer un exposé, réaliser une enquête documentaire
		- Trouer un document est plus rapide, plus de qtt, mais quid de la qualité et fiabilité à évaluer ?
		- Gestion du but informationnel : difficile de prendre conscience qu'on manque de connaissances et de l'exprimer avec des mots (Tricot et al., 2016)
		- Examen de la liste de résultat : consultation que de la première page, pensant que les dix premiers résultats sont les plus pertinents (Granka et al., 2004; Cutrell & Guan, 2007)
		- Biais de confirmation : préférence à corroborer point de vue plutôt que de le contredire. A l'extrême, peut aboutir à adhésion à des thèses conspirationnistes (Bronner, 2013)
		- Jugement de pertinence : Qualité source, mise en forme, compréhension, intéressant et récent =/= classement dans recherche (Taylor et al., 2007)
		- Evaluation de la fiabilité des sources : évaluation de la crédibilité, de la confiance, de l'autorité (Taylor et al., 2007)
		- Compréhension : quand pas compris, tendance à croire qu'il en a saisi quelque chose, notemment dans domaines où il en sait très peu
		- Traitement des sources multiples : quand deux sources même événement mais écrits différemment, lecteur rejette la source avec laquelle ils ne sont pas d'accord
	- Produire un texte, un document, seul ou à plusieurs :
		- Bangert-Drowns (1993) : guidage métacognitif pour les élèves, non seulement avant mais pendant ou après la rédaction
		- Goldberg et al. (2003) : ttt de textes sur ordinateur améliore la longueur (d = .50) et leur qualité (d = .41) + favorisent rédaction collaborative, itérative et sociale
	- Faire des exercices, s'entraîner :
		- Quand les exerciseurs numériques sont utilisés en classe, le élèvent demandent moins d'aide à l'enseignant que quand papier (Lemercier et al., 2001)
		- Limite : utiles que quand renforcement des savoir-faire, telativement simples et bien définis
	- Coopérer entre élèves :
		- Kirschner et al. (2018), apprennent mieux en groupe mais :
			- Possible coût de prise en main et si tâche suffisament simple pour être faite par 1 élève, détériore sa réalisation et l'apprentissage

## [(YT) Van Beckhoven, 2025 : Cracking the code review](Jansen & Vandeperre, 2025 : Platform Engineering 101: Building Internal Dev Platforms)

**THM** : Be specific, use we, ask questions and help

**Authors** : Paco van Beckhoven

**Tags** : :devops: :code-review:

**Notes**
- In PR:
	- Good title https://www.conventionalcommits.org/en/v1.0.0/
	- Description: Context, what changes, why, examples, keep it concise and readable
	- Set templates + checklists : Changes are covered by tests; There is clear logging and error handling; Update architecture diagrams; service documentation updated
	- Review your own code + add context to smaller changes + highlight important parts + ask specific questions
- Giving reviews:
	- Start days doing reviews
	- Get the context, read the description
	- Design, functionality, complexity, tests
	- Use suggestions

## [Van Maanen & Schein, 1977 : Toward a Theory of Organizational Socialization](https://dspace.mit.edu/bitstream/handle/1721.1/1934/?sequence=1)

**THM** : Six tactical dimensions which caracterize the structural side of organizational socialization

**Authors** : John Van Maanen and Edgar H. Schein

**Tags** : :work-organization: :socialization: :onboarding:

**Notes**
- 1) Collective vs. individual socialization processes
- 2) Formal vs. informal socialization processes
- 3) Sequential vs. random steps in the socialization process
- 4) Fixed vs. variable socialization processes
- 5) Serial vs. dis. uctive socialization processes
- 6) Investiture vs. divestiture socialization processes

## [van Opheusden et al., 2023 : Expertise increases planning depth in human gameplay](https://doi.org/10.1038/s41586-023-06124-2)

**THM** : 

**Authors** : Bas van Opheusden, Ionatan Kuperwajs, Gianni Galbiati, Zahy Bnaya, Yunqi Li & Wei Ji Ma

**Tags** : :expertise: :planning:

**Abstract** : A hallmark of human intelligence is the ability to plan multiple steps into the future1,2. Despite decades of research3,4,5, it is still debated whether skilled decision-makers plan more steps ahead than novices6,7,8. Traditionally, the study of expertise in planning has used board games such as chess, but the complexity of these games poses a barrier to quantitative estimates of planning depth. Conversely, common planning tasks in cognitive science often have a lower complexity9,10 and impose a ceiling for the depth to which any player can plan. Here we investigate expertise in a complex board game that offers ample opportunity for skilled players to plan deeply. We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times and eye movements. We also perform a Turing test and a reconstruction experiment. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Experts memorize and reconstruct board features more accurately. Using complex tasks combined with precise behavioural modelling might expand our understanding of human planning and help to bridge the gap with progress in artificial intelligence.

**Notes**
- Lit explained the superior planning performance of experts chess players as being due to better pattern recognition and/or deeper search
- 

## [Vienneau, 2005 : Apprentissage et enseignement : théories et pratiques](https://tecfalms.unige.ch/moodle/pluginfile.php/39498/mod_resource/content/4/COMEL1_Vienneau_Concepts%20de%20base%20en%20apprentissage%20et%20en%20enseignement.pdf)

**THM** : l'apprentissage scolaire est un processus interne, actif/constructif, interactif, commulatif, culturel et multidimensionnel

**Authors** : Raymond Vienneau

**Tags** : :learning: :teaching:

**Notes**
(liste de points importants provenant du cours de G. Molinari du cours ADID 2022, MALTT et enrichi par la lecture)
- Quelles sont les conceptions de l'apprentissage ?
	- *3 sens* (Reboul, 1999) : apprendre que ... mémoriser une information / apprendre à ... acquisition d'un savoir-faire / apprendre ... d'un savoir-être
	- *processus* -> *produit*
	- conception behaviorale = association stimulus-réponse
	- c. cognitive = interaction entre l'apprenant·e et son environnement éducatif / ttt de l'information
	- c. constructiviste = structure cognitive unique de chaque apprenant·e / individu qui apprend en fonction de ses expériences et de ses connaissances antérieures
	- c. socio-constructivitste = savoir co-construit en interaction avec ses pairs et l'enseignant / app. déterminé par la qualité du climat d'apprentissage et les conflits sociocognitifs
	- c. humaniste = reconnaissance de la valeur subjective des connaissances / app. déterminé par la qualité de l'engagement personnel de l'élève
	- c. traspersonnelle = app. comme processus de développement personnel / recherche de sens, croissance continue qui se réalise au long de la vie
- Quelle est la définition générale de l'apprentissage humain ? (~cognitivo-behaviorale)
	- *L'apprentissage est un changement relativement permamenent (oubli = phénomène naturel) dans le potentiel de comportement (manifestation de la transformation) dû à l'expérience (=/= développement / génétique)*
- Quels sont les principes sous-jacents à la conception actuelle de l'apprentissage scolaire ?
	- Divers types de savoirs = ensemble de connaissances :
		- savoirs (connaissances déclaratives),
		- savoir-faire (connaissances procédurales et conditionnelles),
		- savoir-être (attitudes et valeurs),
		- savoir-devenir (buts personnels),
		- savoir-vivre ensemble (habiletés interpersonnelles et relations sociales)
		- savoir-agir (compétences disciplinaires et transversales)
	1. **processus** : s'inscrit dans le temps et dont l'app. n'est que le moment final
	2. **interne** : manifestation pas toujours observanle (app. =/= performance). 
	> Le meilleur médiateur du monde peut emmener l'élève aux portes de la connaissances, mais seul l'apprenant peut en franchir le seuil (Gibran, 1978)
	3. **actif/constructif** : apprenant·e doit s'engager cognitivement et affectivement, nécessite un effort conscient au moment de la réception, du ttt et de l'emmagasinage de l'information.
	> L'apprentissage est quelque chose que l'on construit, et non quelque chose qui nous arrive (Dalceggio, 1991)
	4. **interactif** : autant sociales que par les ressources à disposition
	5. **cummulatif** : "Plus on apprend, plus on est en mesure d'apprendre" (#learningtolearn)
	6. **produit d'une culture**
	7. **multidimentionnel** : pas seulement dimension cognitive, mais dans tous les autres savoirs énoncés avant

## [Wang & Nunes, 2019 : Matching serious games with museum’s educational roles: smart education in practice](https://www.emerald.com/insight/content/doi/10.1108/ITSE-03-2019-0013/full/html)

**THM** : 

**Authors** : Meng Wang, Miguel Baptista Nunes

**Tags** : :museum-educational-roles: :educational-games: :museum:

**Abstract** : Purpose. This study aims to present a meta-analysis of the use of serious educational games in museums. The analysis is based on a critical literature review that maps educational roles of museums against serious educational games used in support of those roles. The meta-analysis focuses on the specific context of informal learning in museums. Design/methodology/approach. The research design presented in this study is based on a meta-analysis research design that consists of a critical literature review, a multi-matrix representation of findings of the literature review and a conceptual visualization of the multidisciplinary area of the usage of serious games in support of educational roles in museums. Findings. Clear and detailed categorizations of educational roles and serious games types for informal learning are presented. These are followed by matching these educational roles with published reports of the use of serious games within museums. The study concludes with observations and a conceptual map of the design of serious games for museums. Originality/value. This study presents the first meta-analysis of research in this emergent multidisciplinary field. It will help serious game designers, museum educators and educational practitioners to make decisions regarding the choice of game type, customization and content design to support informal learning in the specific context of museum educational activities.

**Notes**
- Focus on informal learning in museums
- First MA in this field
- Visualization, closeness and een manipulation of emulated items displayed in museums can contribute to visitor learning processes and have a strong impact on understanding and knowledge acquisition ([de Rijcke and Beaulieu, 2011](https://doi.org/10.1353/lib.2011.0020))
- Therefore, informal learning, in contrast to the structured and directed formal learning, is the sum of learning experiences and activities undertaken by individuals when they are not in the presence of a teacher ([Gerber et al., 2001](https://doi.org/10.1080/09500690116959))
- 3 aims:
	- Identify and classify educational roles of museums
	- Identify and classify SEG suitable for use in museum environments
	- Map games to the educational roles identifies according to their suitability
- Educational roles of museums:
	- Since the late 1880s, 1880s, museums have adopted formal teaching practices to educate visitors about their collections and displays, as well as their preservation strategies and methods (Joel and Mintz, 2018)
	- 1992 is commonly regarded as the changing point in the educational role of museum because of the publication of the American Association of Museum Museums report entitled “Excellence and Equity: Education and the Public Dimension of Museums” (Anastasia and Sylaiou, 2015; [Nuzzaci, 2006](https://redined.educacion.gob.es/xmlui/bitstream/handle/11162/125025/16724-16800-1-PB.PDF?sequence=1); [Williams, 1996](https://doi.org/10.1080/00393541.1996.11649989)). The report proposed ways in which museums should expand their role in education and move beyond the traditional boundaries of walls and collections to reach broader audiences. The aim of this new vision was to provide guidance in supporting and increasing educational opportunities for people of all ages and reflecting the pluralism of a diverse and heterogeneous modern society (American Association of Museums, 1992).
	- Promoting awareness:
		- Because the museums are open to the public and seek to have a social impact beyond their walls, it has become a common practice for advanced museums to transform the museum into a teaching place that provides learning about the social contexts, historical events, cultural backgrounds and scientific knowledge that are designed to help visitors to interpret their exhibits ([Efird, 2014](https://doi.org/10.1080/13504622.2014.976607); Akamca et al., 2017; [Ames, 1988](https://doi.org/10.1111/j.2151-6952.1988.tb00671.x))
	- Changing habits:
		- Learning by doing: The aspiration to enact change in visitor’s habits and engagement led to a shift in museology, aiming at turning visitors’ attitudes from “cultural consumption to cultural production”. This was initially the base philosophy of an initiative that originated in the UK, which was aimed at developing cultural and creative industries ([O’Connor, 2009](https://doi.org/10.1086/599549)). 
		- Response to the challenge of changing societal and visitor's habits related to perceptions of museums visits -> towards a more social and shared experience -> more constructivist process by the sharing of learning experiences and social negociation of meanings. Visitors' understanding and existing knowledge of phenomena can be influenced by others by the sharing of experiences, discussion and other forms of social interaction (Hooper-Greenhill, 1999; [Mortara et al., 2014](https://doi.org/10.1016/j.culher.2013.04.004); Cao et al., 2018; Liu, 2014)
	- Contextualizing visits:
		- Transfer (...). A less formal but more playful approach is the use of customized educational games that encourage visitors to complete museum-related challenges, adventures and quizzes ([Perezvalle et al., 2014](https://doi.org/10.1007/978-3-319-13695-0_54); [Gee, 2004](https://www.raco.cat/index.php/IEM/article/download/204239/272773); Mortara et al., 2014; Zheng, 2017; Li, 2018).
- Serious EdGames for museums:
	- [Djaouti et al. (2011)](https://doi.org/10.4018/978-1-60960-495-0.ch006) -> Gameplay (game structure and how it is played) / Purpose (purpose beyond entertainment for which the game was designed) / Scope (targeted applications(s) of the SG considering actual uses, target market and intended audience)  model.
	- Three purposes:
		1. Message broadcasting or the communication of specific information about the museum specs. -> formal educative content which is informative, persuasive and customized
		2. Training learners for the acquisition of intellectual knowledge and/or physical skills -> encourage from passive into active cultural producers. Either well-defined learning objectives and skills acquisition is more directed, either games designed to promote experiential learning are less prescriptive and offer more opportunity for exploration either by allowing navigation through a virtual world or by requiring players to inhabit a role that requires them to make decisions and face the consequences of those decisions (AL2049).Experiential learning games are aimed at providing interactive and complex serious game environments along with complex challenges to encourage experiential learning ([Kiili, 2005](https://doi.org/10.1016/j.iheduc.2004.12.001); [Breuer and Bente, 2010](https://hal.science/hal-00692052); Mortara et al., 2014). 
- Matching museums' informal educational roles to serious edgames:

## [Wood & Wood, 1999 : Help seeking, learning and contingent tutoring](https://www.sciencedirect.com/science/article/pii/S0360131599000305?casa_token=SNnc3y-SOFUAAAAA:_woEegyc81EM6XXLUPFmwwNs9zdaMDe1k0RC9S7_xRVYAgFaIXxP8p0u05dRzILJelUBZnbdCA)

**THM** : 

**Authors** : H. Wood & D Wood

**Tags** : :help-seeking: :learning:

**Abstract** :The focus of this paper is the application of the theory of contingent tutoring to the design of a computer-based system designed to support learning in aspects of algebra. Analyses of interactions between a computer-based tutoring system and 42, 14- and 15-year-old pupils are used to explore and explain the relations between individual differences in learner–tutor interaction, learners’ prior knowledge and learning outcomes. Parallels between the results of these analyses and empirical investigations of help seeking in adult–child tutoring are drawn out. The theoretical significance of help seeking as a basis for studying the impact of individual learner differences in the collaborative construction of ‘zones of proximal development’ is assessed. In addition to demonstrating the significance of detailed analyses of learner–system interaction as a basis for inferences about learning processes, the investigation also attempts to show the value of exploiting measures of on-line help seeking as a means of assessing learning transfer. Finally, the implications of the findings for contingency theory are discussed, and the theoretical and practical benefits of integrating psychometric assessment, interaction process analyses, and knowledge-based learner modelling in the design and evaluation of computer-based tutoring are explored.

**Notes** :
- 

## [Zuboff, 2021 : L'âge du capitalisme de surveillance](https://www.zulma.fr/wp-content/uploads/2022-01-Capitalisme-de-surveillance-Za.pdf)

**Authors** : Shoshana Zuboff

**Tags** : :surveillance: :capitalism:

**Notes**
- p. 414 : libre arbitre selon l'autorégulation, l'autodétermination et l'autonomie (régulation par le moi)

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