Educators are key to promote the application of games in education. Games need to be transparent and reliable as educators are not game experts. Game Learning Analytics can help in the process Formal validation with accepted method (e.g. pre-post) Application in class obtaining real-time information Assessing students based on in-game interactions

1. Game Learning Analytics
for Educators
Antonio Calvo-Morata, Cristina Alonso-Fernández, Manuel Freire,
Iván Martínez-Ortiz, Baltasar Fernández-Manjón
EDUCON 2019, Dubai UAE
https://www.slideshare.net/BaltasarFernandezManjon/

2. Serious Games
Serious Games: main purpose not entertainment (e.g.
learning, raising awareness, changing players’ attitudes)
For their application in education, educators may face several
issues:
➢ technology or platform requirements
➢ gameplay average duration
➢ adaptation for users with special needs or disabilities
➢ availability of enough number of devices
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3. Analytic System
collection, analysis, reports
Game Learning Analytics
Game Learning Analytics aims to collect
and analyze data from serious games.
Game Analytics + Learning Analytics
➢ evaluate and improve games
➢ provide information that helps educators
➢ evidence-based decisions for institutions
➢ more authentic learning experience for
students
play
information
& feedback
GLA
data

4. GLA System: H2020 RAGE & BEACONING

5. Teachers’ process applying games
GLA
data

6. Serious Games formal validation
First step for serious games: formal validation.
Most common method: pre-post experiments
- pre-test measures characteristic (e.g. knowledge,
awareness) before playing
- post-test measures same characteristic after playing
➢ Requirement: accepted questionnaire that measure the specific characteristic
- If no such questionnaire exists, complexity escalates as first, the validation
questionnaire itself has to be developed and validated!

7. Serious Games application in classes

8. SGs application in classes: before
Before games are in play, teachers/educators will:
1. Read the educators’ guide
○ downloading/installing instructions
○ game goals
○ information about the game content
○ additional information about context
○ instructions for students
2. Play the game
○ get the same experience their students will
○ understand design decisions and link them to educational content

9. SGs application in classes: during
While games are in play, teachers/educators
will:
1. Receive real-time information
○ follow students progress
○ get overview of errors
○ receive warnings/alerts for specific
situations
2. Act accordingly
○ help students having issues to continue
○ provide additional tasks to advanced
students

10. After games are in play,
teachers/educators will:
1. Conduct post-game activities
○ discussion/debriefing to promote
reflection
○ link game content with curricula
○ additional exercises to apply the
content of the game
○ even assessment activities
SGs application in classes: after
➢ These post-game activities may be suggested in the educators’ manual.

11. Students evaluation based on in-game interactions
Final step to simplify educators’ tasks: formal and automatic evaluation of
students based on their actions playing the game
➢ common assessment method: pre-post questionnaires
➢ drawbacks can be avoided using GLA data from in-game interactions to
predict players’ knowledge after playing
1. Create prediction models at validation step
2. Train algorithms and evaluate performance with actual data
3. Select accurate-enough models as assessment method
4. New players: predict knowledge with models and GLA data

12. Conclusions
● Educators are key to promote the application of games in education
● Games need to be transparent and reliable as educators are not game
experts
1. Formal validation with accepted method (e.g. pre-post)
2. Application in class obtaining real-time information
3. Assessing students based on in-game interactions
● Limitations:
○ Technical support still needed in many contexts
○ Still a complex and fragile infrastructure that requires an analytic server
○ Privacy and security needs to be ensured when collecting data (GDPR compliance)

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