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...

Descripción completa

Detalles Bibliográficos
Autores: Alonso Fernández, Cristina, Calvo Morata, Antonio, Freire, Manuel R., Martínez Ortiz, Iván, Fernández-Manjón, Baltasar
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/712248
Acceso en línea:http://hdl.handle.net/10486/712248
https://dx.doi.org/10.18608/jla.2022.7633
Access Level:acceso abierto
Palabra clave:Serious games
game learning analytics
game-based learning
stealth assessment
visualization
Informática
id ES_6f3dccf2a79a4931e77238a5fd472353
oai_identifier_str oai:repositorio.uam.es:10486/712248
network_acronym_str ES
network_name_str España
repository_id_str
spelling Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player LearningAlonso Fernández, CristinaCalvo Morata, AntonioFreire, Manuel R.Martínez Ortiz, IvánFernández-Manjón, BaltasarSerious gamesgame learning analyticsgame-based learningstealth assessmentvisualizationInformáticaGame 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.This work has been partially funded by the Regional Government of Madrid (eMadrid S2018/TCS4307, co-funded by the European Structural Funds FSE and FEDER) and by the Ministry of Education (PID2020-119620RB-I00).Beaumont, Alberta, Canada Society for Learning Analytics ResearchDepartamento de Ingeniería InformáticaEscuela Politécnica Superior20222022-12-11research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/712248https://dx.doi.org/10.18608/jla.2022.7633reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7122482026-06-23T12:46:27Z
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
Serious games
game learning analytics
game-based learning
stealth assessment
visualization
Informática
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, Manuel R.
Martínez Ortiz, Iván
Fernández-Manjón, Baltasar
author Alonso Fernández, Cristina
author_facet Alonso Fernández, Cristina
Calvo Morata, Antonio
Freire, Manuel R.
Martínez Ortiz, Iván
Fernández-Manjón, Baltasar
author_role author
author2 Calvo Morata, Antonio
Freire, Manuel R.
Martínez Ortiz, Iván
Fernández-Manjón, Baltasar
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Serious games
game learning analytics
game-based learning
stealth assessment
visualization
Informática
topic Serious games
game learning analytics
game-based learning
stealth assessment
visualization
Informática
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
2022-12-11
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/712248
https://dx.doi.org/10.18608/jla.2022.7633
url http://hdl.handle.net/10486/712248
https://dx.doi.org/10.18608/jla.2022.7633
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
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Beaumont, Alberta, Canada Society for Learning Analytics Research
publisher.none.fl_str_mv Beaumont, Alberta, Canada Society for Learning Analytics Research
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410481966415872
score 15,300719

Similares en LA Referencia