This document proposes using learning analytics to analyze data from location-based serious games. It introduces extensions to the xAPI standard to track player locations, orientations, areas entered/exited, and directions followed. A platform is described that collects these location-based traces, analyzes the data, and visualizes it to provide insights. The analysis and visualizations can help assess student behavior and learning over time to detect misconceptions. While the approach provides a simple location-based assessment model, teachers are still responsible for extracting meaningful insights about learning from the data.

1. LEARNING
ANALYTICS FOR
LOCATION-BAS
ED SERIOUS
GAMES
Victor M. Perez-Colado, Dan Cristian Rotaru, Manuel Freire,
Ivan Martinez-Ortiz, Baltasar Fernandez-Manjon

2. INDEX
1. PROBLEM AND OBJECTIVES
2. PERVASIVE GAMES
3. xAPI
4. LOCATION-BASED xAPI
5. ANALYSIS
6. VISUALIZATIONS
7. ASSESSMENT
8. CONCLUSIONS & FUTURE WORK

3. PROBLEM
● Growth in Pervasive
Gaming
● Serious Games can
benefit from
location-based
mechanics
● No location-based
Standard to Track and
Analyze data
● Player movement
● Player interactions
with and outdoor
area
● Entering and exiting
areas
● Looking at and
following directions

4. MAIN RESEARCH OBJECTIVES
● Fostering a tool ecosystem
● Creating tools that are meaningful to
teachers
● Adopt a “simply works” (but
customizable) approach for our
analytics platform.

5. LOCATION-BASED
GAMES
Player: The character represented by an oriented
avatar or symbol.
Augmented Map: A real world map that is augmented
with the game model. The model is mainly divided in:
- Areas/Regions: Real world region that can be
accessed or exited (i.e. parks, cities, districts)
- Points of interest: A real world element that
can be looked (i.e. monuments, buildings).
- Virtual elements: Objects that exist only in
the game and can be interacted (i.e. monsters,
characters, portals)
Navigation: Indications to interesting
game elements.
Points
of
Interest
Virtual
Elements
Regions
Midoki GO Deliver Pokémon GO
Ingress

6. COLLECT DATA
● Actions happen in real-time and everywhere
● The game can have a diverse range of interactions
including location-based, serious-game based or
game-specific
● Data has to be gathered and presented by analyzing
different types of data
Player entered park
(Area)
Player looked at
fountain (POI)
Player interacted
with dragon (VE)
Player followed
indications

7. EXPERIENCE API (xAPI)
xAPI is Generic and
Extensible
The model allows tracking of all
in-game interactions as xAPI
traces (statements). It also
simplifies data sharing.
To use the Profile, several
open-source tracker
implementations are available.
Experience API (xAPI) for
Serious Games Profile:
standard interactions model
implemented in xAPI with
ADL.

8. LOCATION-BASED xAPI
Extensions (to append in any action):
● Location: where the action happens (coordinates).
● Orientation: of the actor’s view (degrees)
● Guide: that the player is following (steps)
Targets (to identify augmented map elements):
● Area: subtypes building, urban-area, green-zone, water.
● POI: to identify points of interest.
● Direction: to follow.
Verbs (to perform actions with):
● Moved: when an actor changes its position
● Entered/Exited: to access or leave zones.
● Looked: to detect sightseeing
● Followed: when directions are followed.

9. TRACES
The most common actions in location-based games can
be created by combining the previous elements.
Action Noun Verb Target Extensions
Moving Player Move Areas (*) Location
Entering place Player Enter Areas (*) Location
Exiting place Player Exit Areas (*) Location
Looking at place Player Look Areas (*), especially
POIs
Location
Orientation
Following
navigation
indications
Player Follow Direction Location
Guide

10. ANALYSIS
Traces generated are sent to a LA server that analyzes
the traces and generates both visualizations and
metrics.
There are two types of analysis + alerts:
● Default: is provided by the system and therefore
generic for all location-based games.
● Custom: specific visualizations that requires
programming to extract game-specific insigth
● Rule -based alerts. Simply enough to be created by
instructors ⇒ Simple, but yet powerful,
customization

11. VISUALIZATIONS
Analysis outcomes are presented in an easy to
understand way:
● Default: Are provided in the LA system for the
given events.
○ Default visualization for location-based include:
movement in the game environment, area/POI
interactions (enter, exit, look), directions followed,
etc.
● Custom: Can be created to represent
game-specific metrics.
○ For traces with location extension when position is
relevant:
■ Heatmaps
■ Route maps (when order matters)
○ For traces without location or when position
is not relevant (i.e. distance, angle)
■ Bars, lines, metrics, averages, etc.

12. ASSESSMENT
▸ Complements the analysis
▸ Offers insights not just a score
▸ Offers a qualitative view of the students’
learning
▸ Assessment can combine location-based data
with additional time-related information to
assess the behavior over time of the students
▸ Relevant to detect misconceptions and allow
the use of targeted feedback to improve the
learning experience

13. CONCLUSIONS
▸ xAPI location-based profile is extended for
pervasive experiences
▸ (Limitation) teachers are responsible of
extracting meaning from the insights if no
additional layer of customization is
provided to link inputs to actual learning
▸ The solution provides a simple
location-based assessment model that
can aid and support traditional
assessment methods

14. THANK YOU!
Any questions
Ivan Martinez-Ortiz
imartinez@fdi.ucm.es
www.e-ucm.es
github.com/e-ucm/