RT Journal Article
T1 Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
A1 Alonso Fernández, Cristina
A1 Calvo Morata, Antonio
A1 Freire Morán, Manuel
A1 Martínez Ortiz, Iván
A1 Fernández Manjón, Baltasar
AB Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners’ actions and results in serious games.
SN 1929-7750
YR 2022
FD 2022-12-16
LK https://hdl.handle.net/20.500.14352/99557
UL https://hdl.handle.net/20.500.14352/99557
LA eng
DS Docta Complutense
RD 9 abr 2025