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

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Title: Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
Language: English
Authors: Alonso-Fernández, Cristina (ORCID 0000-0003-2965-3104), Calvo-Morata, Antonio (ORCID 0000-0001-8701-7582), Freire, Manuel (ORCID 0000-0003-4596-3823), Martínez-Ortiz, Iván (ORCID 0000-0001-6595-5690), Fernández-Manjón, Baltasar (ORCID 0000-0002-8200-6216)
Source: Journal of Learning Analytics. 2022 9(3):32-49.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 18
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Descriptors: Educational Games, Learning Analytics, Data Collection, Educational Improvement, Game Based Learning, Visualization, Student Evaluation, Evidence Based Practice
ISSN: 1929-7750
Abstract: 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.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1375345
Database: ERIC
FullText Text:
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  Data: Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
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  Data: <searchLink fieldCode="AR" term="%22Alonso-Fernández%2C+Cristina%22">Alonso-Fernández, Cristina</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2965-3104">0000-0003-2965-3104</externalLink>)<br /><searchLink fieldCode="AR" term="%22Calvo-Morata%2C+Antonio%22">Calvo-Morata, Antonio</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8701-7582">0000-0001-8701-7582</externalLink>)<br /><searchLink fieldCode="AR" term="%22Freire%2C+Manuel%22">Freire, Manuel</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4596-3823">0000-0003-4596-3823</externalLink>)<br /><searchLink fieldCode="AR" term="%22Martínez-Ortiz%2C+Iván%22">Martínez-Ortiz, Iván</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6595-5690">0000-0001-6595-5690</externalLink>)<br /><searchLink fieldCode="AR" term="%22Fernández-Manjón%2C+Baltasar%22">Fernández-Manjón, Baltasar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8200-6216">0000-0002-8200-6216</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2022 9(3):32-49.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Educational+Games%22">Educational Games</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Collection%22">Data Collection</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Improvement%22">Educational Improvement</searchLink><br /><searchLink fieldCode="DE" term="%22Game+Based+Learning%22">Game Based Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Evaluation%22">Student Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+Based+Practice%22">Evidence Based Practice</searchLink>
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  Data: 1929-7750
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  Data: 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.
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        PageCount: 18
        StartPage: 32
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      – SubjectFull: Educational Games
        Type: general
      – SubjectFull: Learning Analytics
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      – SubjectFull: Evidence Based Practice
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      – TitleFull: Game Learning Analytics: Blending Visual and Data Mining Techniques to Improve Serious Games and to Better Understand Player Learning
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