Title
Contextual Markup and Mining in Digital Games for Science Learning: Connecting Player Behaviors to Learning Goals.
Abstract
Digital games can make unique and powerful contributions to K-12 science education, but much of that potential remains unrealized. Research evaluating games for learning still relies primarily on pre- and post-test data, which limits possible insights into more complex interactions between game design features, gameplay, and formal assessment. Therefore, a critical step forward involves developing rich representations for analyzing gameplay data. This paper leverages data mining techniques to model learning and performance, using a metadata markup language that relates game actions to concepts relevant to specific game contexts. We discuss results from a classroom study and identify potential relationships between students’ planning/prediction behaviors observed across game levels and improvement on formal assessments. The results have implications for scaffolding specific activities, that include physics learning during gameplay, solution planning and effect prediction. Overall, the approach underscores the value of our contextualized approach to gameplay markup to facilitate data mining and discovery.
Year
DOI
Venue
2017
10.1109/TLT.2016.2521372
TLT
Keywords
Field
DocType
Games,Surges,Force,Metadata,Context,Data mining
Metadata,Game mechanics,Computer science,Knowledge management,Game design,Human–computer interaction,Knowledge engineering,Science learning,Multimedia,Affordance,Science education,Markup language
Journal
Volume
Issue
ISSN
10
1
1939-1382
Citations 
PageRank 
References 
1
0.35
5
Authors
8
Name
Order
Citations
PageRank
John S. Kinnebrew122625.72
Stephen S. Killingsworth2122.85
Douglas Clark3927.79
Gautam Biswas41594233.43
Pratim Sengupta59515.02
James Minstrell610.35
Mario Martinez-Garza7384.90
Kara Krinks811.02