Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning

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. How...

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Detalles Bibliográficos
Autores: Alonso Fernández, Cristina, Calvo Morata, Antonio, Freire Morán, Manuel, Martínez Ortiz, Iván, Fernández Manjón, Baltasar
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/99557
Acceso en línea:https://hdl.handle.net/20.500.14352/99557
Access Level:acceso abierto
Palabra clave:Software
Educación
1203.10 Enseñanza Con Ayuda de Ordenador
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spelling Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player LearningAlonso Fernández, CristinaCalvo Morata, AntonioFreire Morán, ManuelMartínez Ortiz, IvánFernández Manjón, BaltasarSoftwareEducación1203.10 Enseñanza Con Ayuda de OrdenadorGame 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.Universidad Complutense de Madrid20242024-02-0620222022-12-1620222022-12-16journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/99557reponame:Docta Complutenseinstname:Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/995572025-08-02T12:44:52Z
dc.title.none.fl_str_mv Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
title Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
spellingShingle Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
Alonso Fernández, Cristina
Software
Educación
1203.10 Enseñanza Con Ayuda de Ordenador
title_short Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
title_full Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
title_fullStr Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
title_full_unstemmed Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
title_sort Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
dc.creator.none.fl_str_mv Alonso Fernández, Cristina
Calvo Morata, Antonio
Freire Morán, Manuel
Martínez Ortiz, Iván
Fernández Manjón, Baltasar
author Alonso Fernández, Cristina
author_facet Alonso Fernández, Cristina
Calvo Morata, Antonio
Freire Morán, Manuel
Martínez Ortiz, Iván
Fernández Manjón, Baltasar
author_role author
author2 Calvo Morata, Antonio
Freire Morán, Manuel
Martínez Ortiz, Iván
Fernández Manjón, Baltasar
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Software
Educación
1203.10 Enseñanza Con Ayuda de Ordenador
topic Software
Educación
1203.10 Enseñanza Con Ayuda de Ordenador
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-12-16
2022
2022-12-16
2024
2024-02-06
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/99557
url https://hdl.handle.net/20.500.14352/99557
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:
instname_str
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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