1. Game Learning Analytics is not informagic!
Iván Pérez-Colado, Cristina Alonso-Fernández,
Manuel Freire, Iván Martínez-Ortiz,
Baltasar Fernández-Manjón
EDUCON 2018, Tenerife, Spain

2. Game Learning Analytics
Learning Analytics collect interaction
information from e-learning system to
get insight and improve the educational
process
Game Learning Analytics is LA applied
to Serious Games
to collect, analyze and visualize
information from players interactions for
the different stakeholders (teacher,
student, developer, manager)

3. Learning Analytics Model (LAM)
Game Learning Analytics needs a model to drive the analysis of the inputs at
different levels to better understand learners and to improve educational outcomes
For LAMs to provide insight into learning, it is necessary to clearly establish:
- requirements to fulfill a LAM definition
- realistic expectations of what the outcomes can be
Too often, users expect GLA with deep meaning based on shallow interaction
data, assuming the GLA system can infer the game educational design
This is not Game Learning Analytics but an unrealistic expectation (informagic).

4. LAM in a Game Learning Analytics platform
LAM is the model that drive and details how information should be tracked,
collected, aggregated and reported to a Learning Analytics System (LAS).

5. Learning Analytics Model (LAM)
LAMs must provide an executable definition of:
● What: the data the system gathers, manages and uses for analysis
● How: the way in which the analysis is performed on the collected data
● Who: the stakeholders targeted by the analysis
LAMs interplay with the analytics
system and the main stakeholders.
Blue boxes -> mechanism (LAS)
Red boxes -> policy (LAM)

6. Learning Analytics Model (LAM)
Several steps to define a LAM, involving
stakeholders in charge of the definitions.
Each step corresponds to an activity that
provides a result to fulfill that step in the
LAM.
Default LAM available, but particular
LAMs need to be created for new games.

7. Steps to define a LAM
1. Learning goals to be achieved in
the game.
2. Game goals (e.g. tasks, levels)
Correspondence between learning
& game goals.
3. Traces to be sent by the game
(follow some standard)
4. Analysis model defined how
traces should be analyzed &
interpreted.
5. Visualizations game-dependent
to extend the default LAM.

8. Meta - Learning Analytics Model
LAMs are designed for individual games. When several games are connected, we
need a larger structure to deal with multi-scale games: meta-LAMs that stitch
together the individual game LAMs into a larger whole.
e.g. geolocalized game launching minigames
A complete Meta-LAM definition requires:
● the hierarchy between the games
● the information flow from one level to another
Meta-LAM allows to cover progress and completion in the whole structure to show
global information.

9. Meta-LAM structure proposal
Meta-LAM can be defined for general structures of games (e.g. trees).
We propose a structure based on the IMS Simple Sequencing specification as
described in SCORM 2004 4th Edition Sequencing and Navigation (SN).
Learning activities (with associated LAMs)
correspond to nodes in the Activity Tree
(meta-LAM). Activities allow to define
completion and mastery conditions.
Rollup rules define how information is
passed from children to parent nodes.

10. LAM and Meta-LAM in EU H2020 BEACONING
Meta-LAM proposal
considered for the H2020 EU
BEACONING Project.
Hierarchical structure of
games and mini-games.
Learning designers define
GLPs as tree of missions,
quests and activities.

11. Data Collection
Experience API for Serious
Games Profile (xAPI-SG):
standard interactions model
implemented in xAPI with ADL.
The model allows tracking of all
in-game interactions as xAPI
traces (statements). It also
simplifies data sharing.
To use the xAPI-SG Profile,
several open-source tracker
implementations are available.
https://github.com/e-ucm/unity-tracker

12. Data Analysis and Visualization
xAPI traces allows for:
- a default set of analyses
and visualizations
- for different stakeholders
(e.g. teachers, developers).
Visualization for specific
games can be developed
based on game-specific
LAMs.
Correct/incorrect answers per
question (alternative in xAPI-SG).
Progress per player in each task /
level (completable in xAPI-SG).

13. Conclusions
● Learning Analytics Models describe and drive how interaction data from
serious games is gameplay is to be collected, analyzed and displayed.
● The information extracted is essential to provide feedback to different
stakeholders (e.g. teachers).
● Effective LAMs should be based on the learning design and provide a
process establishing clear definitions and expected outputs.
● LAMs are applied for a single game. More complex structures require
meta-LAMs to define how information is passed between levels.
● Next steps:
- improve and provide more examples of LAMs to simplify adoption
- test meta-LAM structure in large scale experiments

14. Thank you!
Baltasar Fernandez-Manjon
@baltaFM
balta@fdi.ucm.es
Code: https://github.com/e-ucm
www.e-ucm.es