Learning Analytics Serious Games Cognitive Disabilities
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The document describes a Game Learning Analytics model called GLAID for analyzing learning in users with cognitive disabilities. GLAID collects interaction data during game sessions and analyzes it at three levels - individualized, collective, and predictive. It relates the data to the game design and educational goals. The model was applied to the serious game Downtown designed to teach subway navigation to people with Down syndrome. Observables like help button clicks were tracked over sessions to provide individualized and collective analysis of learning progress.
Learning Analytics Serious Games Cognitive Disabilities
- 1. GLAID: Designing a Game Learning Analytics Model to Analyze the Learning Process in Users with Cognitive Disabilities Baltasar Fernández-Manjón Ana R. Cano, Álvaro J. García-Tejedor Grupo e-UCM: www.e-ucm.es balta@fdi.ucm.es @BaltaFM SGames Conference, Porto, 16/06/2016 http://www.slideshare.net/BaltasarFernandezManjon/
- 2. LA & GLA 101 • Learning Analytics: Improving education based on Data Analysis ̶ Data driven ̶ Evidence-Based Education • Game Learning Analytics application of LA to Serious Games ̶ Interaction data in a Serious Game is collected and analyzed for improving the learning process supported by the game ̶ Educational game not as “black boxes” ̶ But LA & GLA is not “informagic” ̶ We need to relate data with what happens in the game and with the educational design!
- 3. The GLA Problem • Ok, we are collecting ALL the interaction data in a video game but… IT IS A HUGE AMOUNT OF DATA! Now what? • What are the relevant observables? • How do I analyze the data collected? • How do I translate it into useful information about the learning process?
- 4. And the problem gets bigger… …If the user has an intelectual condition or disability (e.g. Down Syndrome) User Features: • Interaction with the game (motor skills) • Ordering thoughts and language in a “logical” layout • Listening and taking turns in conversations • Communication in an interactive sense • Relating objects and actions to spoken or written words
- 5. H2020 Beaconing project • BEACONING stands for ‘Breaking Educational Barriers with Contextualised, Pervasive and Gameful Learning’ • Started in january 2016, 15 partners, 9 countries, 6M • Global goal is learning ‘anytime anywhere’ • Exploitation of technologies for contextual pervasive games and use of gamification techniques • Problem based approach to learning • Enriching the Gaming Learning Analytics data model with the contextual, geolocalized and accessibility information • Large pilots in real settings with content providers • Formal and informal learning across virtual and physical spaces • GLA is a key element in the games and pilots evaluation • Using RAGE infrastructure and extending it for these new requirements and applications
- 6. Our approach: The GLAID Model Present Individualized Learning Analysis Collective Learning Analysis Predictive Learning Analysis …. Group 1 Group 2 Group 3 Game Sessions LearningProgress d1.a d1.n d2.a d3.a d2.n d3.n *d = Data collected during a game session GLAID (Game Learning Analytics for Intellectual Disabilities) Model Analytics Framework User 1 User 2 User n User 1 User n User 3 User 2 User 5 User 4 User 1 Data Handling Designer Perspective Educator Perspective User cognitive restrictions Formal Requirements Game & Learning Design Group of Observables Group of Observables Descriptive Analytics Clustering Analytics Predictive/Prescriptive Analytics
- 7. First Step: From the User Restrictions to a Game Design • Challenges: 1) Transform the user characteristics into formal requirements 2) Develop a learning game design adequate for users with intelectual disabilities (such as Down Syndrome, mild cognitive impairments, ASD Autism Spectrum Disorders,…) 3) Select a group of observables/variables that measure the learning outcome of the user for future assessment …. * User User User User cognitive restrictions Formal Requirements Game & Learning Design Group of Observables Group of Observables
- 8. 1st Level Analysis: Individualized Learning Analysis • Goal: Describe and analyze historical learning data from the student’s perspective • Outcome: Gives an overview of the user’s learning behaviour through several game sessions • Observables collected individually • Timestamps • Level changes • Achievements vs. Fails • User interactions (number of clicks, heatmaps, time between clicks,…) Individualized Learning Analysis …. d1.a d1.n d2.a d3.a d2.n d3.n *d = Data collected during a game session User 1 User 2 User n
- 9. 2nd Level Analysis: Collective Learning Analysis 9 • Goal: Identify causes of trends and learning outcomes for a group of users segmented by disability or cognitive skills • Outcome: Learning patterns • Observables collected collectively • Timestamps • Level changes • Achievements vs. Fails • User interactions (number of clicks, heatmaps, time between clicks,…) Collective Learning Analysis Group 1 Group 2 Group 3User 1 User n User 3 User 2 User 5 User 4
- 10. 3rd Level Analysis: Predictive Learning Analysis • Goal: Analyze current and historical data to make predictions about future learning outcomes • Outcome: assure the effectiveness of a game as a learning tool for a user with an specific disability • Observables colected individually and collectively • Timestamps • Level changes • Achievements vs. Fails • User interactions (number of clicks, heatmaps, time between clicks,…) Predictive Learning Analysis Game Sessions LearningProgress User 1
- 11. Data Handling: stakeholders • 2 Data handling perspectives: Game Designer’s Perspective • Collect and analyze all the states that the user can reach in a game session • Are the mechanics of the game appropiate for the user? Educator’s Perspective • Learning experience of each user • Are the users learning or struggling with the game?
- 12. Collecting data with xAPI • We can collect the relevant data in a standard format using xAPI • We are working in a xAPI serious games profile with ADL • This will simplify the analysis and visualization of data (e.g. dashboards) 12 xAPI
- 13. Case study: Downtown • Serious Game designed and develop to teach young people with Down Syndrome to move around the city using the subway • Status: Designed and developed. Analysis pending • Type of game: Serious Game • Audience: People between 15 and 30 y/o with Down syndrome • Platform: PC and Android (work in progress)
- 14. Case Study: Downtown • From user requirements to a game design and its observables • Standards: W3C cognitive accessibility, accessibility guidelines 14
- 15. Case Study: From user requirements to a game design User Requirement Game Restrictions Game Design & Mechanics Observable Limited intellectual autonomy The game should be able to guide the user during the learning session through interactive help, pop-up tips or other mechanics There will be a "help" button permanently in the screen where the user can ask for help at anytime during the game session Clicks in the Help buttons during a game session If the user doesn't perform any interaction for more than 2 minutes, a pop-up aid will appear providing guide, tips and advices Total inactivity time Inactivity time after pop-up help appears The phone will act as a help button. If the user needs tips or advices, he can call the police asking for clues to complete the ongoing task
- 16. Case Study: From user requirements to a game design User Requirement Game Restrictions Game Design Observable Difficulty in the process of abstractions, conceptualization, generalization and learning transfer The game should explain any action to do, even the easiest, without assuming that the user already know how to complete it Tutorials: The description about how to achieve the goals in the game will be performed as a video explanation before the task starts Time consumed in completing the task Previous research prove that visual explanations help to understand the assignments better than hearing or reading. Savidis, Grammenos and Stephanidis "Developing inclusive e-learning and e-entertainment“. 2007
- 17. Case Study: Applying GLAID to the game • Observable: Clicks in the “Help” button during a game session Session #1 Session #n User #1 3 clicks 1 click Group of users #1 5 clicks (avg) 4 clicks (avg) GLAID Individualized Learning Analysis Collective Learning Analysis •Game designer’s persp: The user improved in the use of the game through sessions •Educator’s persp: The learning experience of the user seems to improve through sessions (measure with other observables) •Game designer’s persp: Users with XX disability slightly improved in the use of the game through sessions. May reconsider game design and mechanics for certain tasks •Educator’s persp: The learning experience of the user slightly improved through sessions. May reconsider the learning experience User#1 Assessment: •The user is able to use the game as a learning tool better than other users •His intellectual autonomy seems to be above the average for his type of disability •His learning experience seems to be improving through game sessions
- 18. Just another BEACONING initiative … 18
- 19. Thanks! Questions? Mail: balta@fdi.ucm.es Twitter: @BaltaFM GScholar: https://scholar.google.es/citations?user=eNJxjcwAAAAJ&hl=en&oi=ao ResearchGate: www.researchgate.net/profile/Baltasar_Fernandez-Manjon Slideshare: http://www.slideshare.net/BaltasarFernandezManjon