Interaction and learning engagement in online learning: The mediating roles of online learning self-efficacy and academic emotions
Learning engagement is recognized as a critical indicator in the evaluation of online courses, as it is related to the quality of online education and students' performance. Prior studies have found that interactions among learners, instructors, and content were associated with students' l...
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| Published in: | Learning and individual differences Vol. 94; p. 102128 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.02.2022
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| Subjects: | |
| ISSN: | 1041-6080, 1873-3425 |
| Online Access: | Get full text |
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| Abstract | Learning engagement is recognized as a critical indicator in the evaluation of online courses, as it is related to the quality of online education and students' performance. Prior studies have found that interactions among learners, instructors, and content were associated with students' learning engagement, yet gaps remain in identifying the internal mechanisms. To contribute to this gap in the knowledge, this study uses self-report survey to examine the mediating roles of online learning self-efficacy and academic emotions in the relationship between interaction and learning engagement in online learning. Data were collected from 474 college students who participated in online courses in China. Multiple mediation analysis showed that (1) learner-content interaction and learner-learner interaction, but not learner-instructor interaction, could predict online learning engagement; (2) online learning self-efficacy and academic emotions (enjoyment; boredom) mediated the link between interactions (learner-content interaction and learner-learner interaction) and learning engagement; (3) both learner-content interaction and learner-learner interaction could predict learning engagement through the sequential mediation of online learning self-efficacy and academic emotions (enjoyment; boredom). This research sheds light on the internal mechanism of different interactions on learners' learning engagement, and provides important theoretical and practical implications for promoting learners' learning engagement in the online learning context.
•Relations among interaction, online learning self-efficacy, academic emotions, and learning engagement were explored.•Learner-learner interaction and learner-content interaction could predict learning engagement during online learning.•Learner-content interaction was found to be the strongest predictor of learning engagement.•Learner-instructor interaction did not predict learning engagement.•Online learning self-efficacy and academic emotions mediated the association between interaction and learning engagement. |
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| AbstractList | Learning engagement is recognized as a critical indicator in the evaluation of online courses, as it is related to the quality of online education and students' performance. Prior studies have found that interactions among learners, instructors, and content were associated with students' learning engagement, yet gaps remain in identifying the internal mechanisms. To contribute to this gap in the knowledge, this study uses self-report survey to examine the mediating roles of online learning self-efficacy and academic emotions in the relationship between interaction and learning engagement in online learning. Data were collected from 474 college students who participated in online courses in China. Multiple mediation analysis showed that (1) learner-content interaction and learner-learner interaction, but not learner-instructor interaction, could predict online learning engagement; (2) online learning self-efficacy and academic emotions (enjoyment; boredom) mediated the link between interactions (learner-content interaction and learner-learner interaction) and learning engagement; (3) both learner-content interaction and learner-learner interaction could predict learning engagement through the sequential mediation of online learning self-efficacy and academic emotions (enjoyment; boredom). This research sheds light on the internal mechanism of different interactions on learners' learning engagement, and provides important theoretical and practical implications for promoting learners' learning engagement in the online learning context.
•Relations among interaction, online learning self-efficacy, academic emotions, and learning engagement were explored.•Learner-learner interaction and learner-content interaction could predict learning engagement during online learning.•Learner-content interaction was found to be the strongest predictor of learning engagement.•Learner-instructor interaction did not predict learning engagement.•Online learning self-efficacy and academic emotions mediated the association between interaction and learning engagement. |
| ArticleNumber | 102128 |
| Author | Li, Na Wang, Yanqing Gong, Shaoying Cao, Yang Ai, Li Wang, Zhen |
| Author_xml | – sequence: 1 givenname: Yanqing surname: Wang fullname: Wang, Yanqing organization: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China – sequence: 2 givenname: Yang surname: Cao fullname: Cao, Yang organization: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China – sequence: 3 givenname: Shaoying surname: Gong fullname: Gong, Shaoying email: gongsy@ccnu.edu.cn organization: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China – sequence: 4 givenname: Zhen surname: Wang fullname: Wang, Zhen organization: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China – sequence: 5 givenname: Na surname: Li fullname: Li, Na organization: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China – sequence: 6 givenname: Li surname: Ai fullname: Ai, Li organization: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China |
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