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

Xieling CHEN, Di ZOU, Lucas KOHNKE, Haoran XIE, Gary CHENG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

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.
Original languageEnglish
Article numbere0255184
Number of pages24
JournalPLoS ONE
Volume16
Issue number7
Early online date28 Jul 2021
DOIs
Publication statusPublished - 28 Jul 2021

Bibliographical note

Haoran Xie’s work is supported by Direct Grant (DR21A5) and the Faculty Research Grant (DB21A9) of Lingnan University, Hong Kong. Gary Cheng’s work is supported by One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), Research Cluster Fund (RG 78/2019-2020R),
and Dean’s Research Fund 2019/20 (IDS-2 2020) of The Education University of Hong Kong. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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