Learning Analytics applied to simulations and videogames
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This document discusses using learning analytics and educational data mining applied to simulations and video games. It aims to assess student learning processes by analyzing their interactions with simulations and provide feedback to teachers. Key points discussed include using tools to understand data from e-learning systems and complex resources like simulations in order to optimize learning. Stakeholders that could benefit are listed as students, teachers, course developers, and administrators. Examples of using analytics with a video game simulation called Super Mario Bros are also provided.
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Learning Analytics applied to simulations and videogames
- 1. LEARNING ANALYTICS APPLIED TO SIMULATIONS AND VIDEOGAMES
- 4. EDUCATIONAL DATA MINING ● Educational institutions have interesting information about students ● Students are generating a lot of new data – e-learning systems (LMS, MOOCs) – Interactions with complex resources (simulations) We need tools to understand these data and optimize learning processes
- 6. STAKEHOLDERS AND OBJECTIVES ● For learners/students – Personalized e-learning, receive recommendations for paths/activities, auto-assessment ● For instructors/teachers – Get feedback about instruction, detect students at risk, predict students performance ● For course developers/researchers – Evaluate and maintain courseware ● For organizations/administrators – Develop the best way to organize institutional resources Romero, C., & Ventura, S. Educational Data Mining: A Review of the State of the Art, 40 IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews 601–618 (2010). IEEE. doi:10.1109/TSMCC.2010.2053532
- 7. IN ● Learning Analytics – Using simulations and videogames ● Objectives – Assess students learning process analyzing their interactions with simulations – Give feedback to teachers ● Real time: while students play with the simulation ● Post-experience: after students are done with the simulation
- 15. GLEANER VIDEOS
- 19. WHAT WE LEARNED ● Phase indicator: progress ● Score and badges: performance ● Detecting errors: students blocked … and that going back to the instructor computer to check the dashboard was not optimal
- 20. A GAME IS FORMED BY... ● Zones: a virtual area in which the player can enter or exit ● Variables: a variable with a meaningful weight inside the game ● Choices: a set of options offered to the player, usually with different consequences ● Quests: the current goal of the player, the next thing he needs to accomplish to advance in the game
- 21. GAME MODEL ● A zones graph: representing the map of the game ● A list of variables: containing all meaningful variables in the game ● A list of choices: all choices players can face during gameplay ● A list of quests: containing all the quests players can complete inside the game.
- 22. Player action Event Target Value Gameplay started start Empty Empty Gameplay ended end Empty Empty Entered in a zone. Implicitily, exits any previous zone zone Empty Zone identifier Variable value updated var Variable name New variable value Quest started quest_started Quest id Empty Quest finished quest_finished Quest id Quest result Selected an option in a choice choice Choice id Option id TRACES
- 23. PLAYER GAMEPLAY STATE "time": time // Time since the gameplay started "zone": "zoneId" // Current zone Id, "zones": { // Number of visits to each zone "zone1": counter, "zone2": counter, ... }, "vars": { // Current value of the variables "var1": value, "var2": value, ... }, "quests_finished": ["quest1", "quest2", ...], "quests_started": ["quest3", "quest4", ...], "choices": { // Counter of options selected in each choice "choice1": { "option1": counter } }
- 24. ASESSMENT MODEL ● Score – Overall performance of the player ● Progress – General progress in the game. How long until the player is done with the simulation ● Alerts – Gameplay state in an undesired condition – Might need instructor intervention
- 25. EXAMPLE: SUPER MARIO BROS.
- 26. GAME MODEL
- 27. TRACES
- 28. GAMEPLAY STATE
- 29. ASESSMENT MODEL
- 33. ASESSMENT MODEL IN GLEANER
- 35. PLAYERS LIST
- 37. WHAT WE HAVE ● Simulations, traces and assessment models ● Framework to track and assess simulations in real time
- 38. NEXT ● Building assessment models automatically – Using extra data in the simulation model ● All data stored in GLEANER is in a non- standard format... – xAPI ● For interactions data, so others can perform analysis ● For assessment results, so it can be shared with a LMS, LRS...
- 39. THANKS!