Authors: Antonio Calvo Morata, Cristina Alonso Fernández, Manuel Freire Morán, Iván Martínez Ortiz y Baltasar Fernández Manjón / Universidad Complutense de Madrid (UCM)
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
11_04_2019 EDUCON eMadrid special session on "Game learning analytics for educators"
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
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!
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)