Affective states in digital game-based learning: Thematic evolution and social network analysis.

Research has indicated strong relationships between learners' affect and their learning. Emotions relate closely to students' well-being, learning quality, productivity, and interaction. Digital game-based learning (DGBL) has been widely recognized to be effective in enhancing learning exp...

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Autores principales:	Xieling Chen, Di Zou, Lucas Kohnke, Haoran Xie, Gary Cheng
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0073bd6734f5406da17662bffa1640d7
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spelling	oai:doaj.org-article:0073bd6734f5406da17662bffa1640d72021-12-02T20:09:02ZAffective states in digital game-based learning: Thematic evolution and social network analysis.1932-620310.1371/journal.pone.0255184https://doaj.org/article/0073bd6734f5406da17662bffa1640d72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255184https://doaj.org/toc/1932-6203Research has indicated strong relationships between learners' affect and their learning. Emotions relate closely to students' well-being, learning quality, productivity, and interaction. Digital game-based learning (DGBL) has been widely recognized to be effective in enhancing learning experiences and increasing student motivation. The field of emotions in DGBL has become an active research field with accumulated literature available, which calls for a comprehensive understanding of the up-to-date literature concerning emotions in virtual DGBL among students at all educational levels. Based on 393 research articles collected from the Web of Science, this study, for the first time, explores the current advances and topics in this field. Specifically, thematic evolution analysis is conducted to explore the evolution of topics that are categorized into four different groups (i.e., games, emotions, applications, and analytical technologies) in the corpus. Social network analysis explores the co-occurrences between topics to identify their relationships. Interesting results are obtained. For example, with the integration of diverse applications (e.g., mobiles) and analytical technologies (e.g., learning analytics and affective computing), increasing types of affective states, socio-emotional factors, and digital games are investigated. Additionally, implications for future research include 1) children's anxiety/attitude and engagement in collaborative gameplay, 2) individual personalities and characteristics for personalized support, 3) emotion dynamics, 4) multimodal data use, 5) game customization, 6) balance between learners' skill levels and game challenge as well as rewards and learning anxiety.Xieling ChenDi ZouLucas KohnkeHaoran XieGary ChengPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255184 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xieling Chen Di Zou Lucas Kohnke Haoran Xie Gary Cheng Affective states in digital game-based learning: Thematic evolution and social network analysis.
description	Research has indicated strong relationships between learners' affect and their learning. Emotions relate closely to students' well-being, learning quality, productivity, and interaction. Digital game-based learning (DGBL) has been widely recognized to be effective in enhancing learning experiences and increasing student motivation. The field of emotions in DGBL has become an active research field with accumulated literature available, which calls for a comprehensive understanding of the up-to-date literature concerning emotions in virtual DGBL among students at all educational levels. Based on 393 research articles collected from the Web of Science, this study, for the first time, explores the current advances and topics in this field. Specifically, thematic evolution analysis is conducted to explore the evolution of topics that are categorized into four different groups (i.e., games, emotions, applications, and analytical technologies) in the corpus. Social network analysis explores the co-occurrences between topics to identify their relationships. Interesting results are obtained. For example, with the integration of diverse applications (e.g., mobiles) and analytical technologies (e.g., learning analytics and affective computing), increasing types of affective states, socio-emotional factors, and digital games are investigated. Additionally, implications for future research include 1) children's anxiety/attitude and engagement in collaborative gameplay, 2) individual personalities and characteristics for personalized support, 3) emotion dynamics, 4) multimodal data use, 5) game customization, 6) balance between learners' skill levels and game challenge as well as rewards and learning anxiety.
format	article
author	Xieling Chen Di Zou Lucas Kohnke Haoran Xie Gary Cheng
author_facet	Xieling Chen Di Zou Lucas Kohnke Haoran Xie Gary Cheng
author_sort	Xieling Chen
title	Affective states in digital game-based learning: Thematic evolution and social network analysis.
title_short	Affective states in digital game-based learning: Thematic evolution and social network analysis.
title_full	Affective states in digital game-based learning: Thematic evolution and social network analysis.
title_fullStr	Affective states in digital game-based learning: Thematic evolution and social network analysis.
title_full_unstemmed	Affective states in digital game-based learning: Thematic evolution and social network analysis.
title_sort	affective states in digital game-based learning: thematic evolution and social network analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0073bd6734f5406da17662bffa1640d7
work_keys_str_mv	AT xielingchen affectivestatesindigitalgamebasedlearningthematicevolutionandsocialnetworkanalysis AT dizou affectivestatesindigitalgamebasedlearningthematicevolutionandsocialnetworkanalysis AT lucaskohnke affectivestatesindigitalgamebasedlearningthematicevolutionandsocialnetworkanalysis AT haoranxie affectivestatesindigitalgamebasedlearningthematicevolutionandsocialnetworkanalysis AT garycheng affectivestatesindigitalgamebasedlearningthematicevolutionandsocialnetworkanalysis
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Affective states in digital game-based learning: Thematic evolution and social network analysis.

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