LEARNING ANALYTICS APPLIED TO

SIMULATIONS AND VIDEOGAMES

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

LEARNING ANALYTICS

ACADEMIC ANALYTICS

EDUCATIONAL DATA MINING

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

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

GLEANER VIDEOS

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

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

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.

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

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

} }

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

EXAMPLE: SUPER MARIO BROS.

GAME MODEL

TRACES

GAMEPLAY STATE

ASESSMENT MODEL

ASESSMENT MODEL IN GLEANER

CLASSROOM SUMMARY

PLAYERS LIST

INDIVIDUAL PLAYER VIEW

WHAT WE HAVE

● Simulations, traces and assessment models● Framework to track and assess simulations in

real time

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...

THANKS!