2026-08-20T06:02:28Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/995572025-03-18T12:15:14Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Alonso Fernández, Cristina author Calvo Morata, Antonio author Freire Morán, Manuel author Martínez Ortiz, Iván author Fernández Manjón, Baltasar 2024-02-06T14:17:01Z 2024-02-06T14:17:01Z 2022-12-16 1929-7750 10.18608/jla.2022.7633 https://hdl.handle.net/20.500.14352/99557 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. eng Attribution-NonCommercial-NoDerivatives 4.0 International Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning journal article 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URL https://docta.ucm.es/bitstreams/ad9a55ac-8324-46d2-a4c8-df199bbbbb64/download File MD5 6f6d19d519d998fcda31998d661ff6c7 1093819 application/pdf draft_10.18608_jla.2022.7633 .pdf URL https://docta.ucm.es/bitstreams/95781f86-aa64-4d1d-80fc-1da73aa51d23/download File MD5 e7526b7cd4f277123e96c79a4c847766 69938 text/plain draft_10.18608_jla.2022.7633 .pdf.txt