The document discusses the development of a learning analytics system tailored for educational video games, focusing on assessing student performance and sharing collected data. Key challenges include the wide variability in game mechanics and a lack of public research on game learning analytics. It outlines a framework for capturing data, analyzing gameplay states, and visualizing results, ultimately suggesting the use of the xAPI standard for communication.

1. Applied Games,
Learning Analytics and Standards
Ángel Serrano Laguna

2. The Ideal
Learning Analytics
System

3. Well, it depends...
Who?
First year law student
Teacher of “Applied Maths”
Vice Dean of the School of Medicine
Minister of Education
Where?
Khan Academy
Udacity
Complutense University
UNED
Why?
Suggest educational pathways after completing a law degree
Assess students performance in “Applied Maths”
Identify teachers whose students perform best in the School
of Medicine
Evaluate child's reading level in Spain

6. Some ideas
Rich reporting is awesome for research and data
analysts...
… but not necessarily for most final users.
Largest amount of information in the most concise
representation.
Interventions and actions required by users of the
system's users in the center.

7. Videogames and
Learning Analytics

8. Goals
1) Reliable framewok to assess students
performance through educational videogames.
2) All data and information collected by the
framework could be shared with others.

9. Main challenges
1) The great variability in videogames
Mechanics, genre, aesthetics...
2) Lack of public research in Game Analytics.
Far less in Game Learning Analytics
Public research mostly focused in monetization.

11. 1) Capturing data
2) Analysis
3) Visualizations
4) Sharing
Steps

12. Modeling and
capturing data from
videogames

13. Game model
●
Quests: the current goal of the player, the next
thing he needs to accomplish to advance in the
game
●
Zones, areas, levels...: 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

14. Traces
{
"event": "var",
"target": "score",
"value": 100
}
Piece of data representing an event in the game.

15. 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

17. Game model

18. Traces

19. Analyzing traces

20. Gameplay State

21. Gameplay State
{
"zone": "menu"
"vars": {
"score": 100,
},
"quests_finished": ["quest1", "quest2"],
"quests_started": ["quest3", "quest4"],
"choices": {
"players": {
"player1": 1
}
},
"..."
}
Built in real time... (with Apache Storm)
Server holds gameplay state for each indivdual player

22. Assessment 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
Gameplays are the base for assessment. In our
assessment model:

23. Gameplay State

24. Assessment Model

25. Visualizing the
results

26. Gameplay State: Classroom (I)

27. Gameplay State: Classroom (II)

28. Gameplay State: Student

29. Sharing through
standards: xAPI

30. xAPI standard

31. xAPI for tracking gameplays
{
"event": "var",
"target": "score",
"value": 100
}
John Doe updated variable score to 100

32. xAPI transmiting results
{
"score": 10,
"progress": 1.0
"alerts": []
}
John Doe completed Lost in Space Game with 10

33. Conclusions
● General Game Model to define games in a
common vocabulary
● Analysis over game states represented as an
aggregation of traces
● Simple visualizations, focused on actions
● xAPI as communication standard

34. Thanks!