Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback

Computer-supported collaborative learning (CSCL) has been broadly utilized in the field of education. However, learners often face difficulties in improving CSCL performance, including improved knowledge elaboration, knowledge convergence, and coregulation. Therefore, the present study aims to compa...

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Main Authors: Lanqin Zheng, Kaushal Kumar Bhagat, Miaolang Long, Nitesh Kumar Jha
Format: Article
Language:English
Published: SAGE Publishing 2025-08-01
Series:SAGE Open
Online Access:https://doi.org/10.1177/21582440251363298
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author Lanqin Zheng
Kaushal Kumar Bhagat
Miaolang Long
Nitesh Kumar Jha
author_facet Lanqin Zheng
Kaushal Kumar Bhagat
Miaolang Long
Nitesh Kumar Jha
author_sort Lanqin Zheng
collection DOAJ
description Computer-supported collaborative learning (CSCL) has been broadly utilized in the field of education. However, learners often face difficulties in improving CSCL performance, including improved knowledge elaboration, knowledge convergence, and coregulation. Therefore, the present study aims to compare the effects of automated informative feedback and knowledge graph-based suggestive feedback on knowledge elaboration, knowledge convergence, and coregulation. This study adopted a convenience sampling method, and a total of 104 undergraduate students registered in a mandatory course voluntarily participated in a quasiexperimental study. The students in experimental Group 1 adopted knowledge graph-based suggestive feedback, the students in experimental Group 2 adopted automated informative feedback, and the students in the control group adopted traditional online collaborative learning without any feedback. The findings revealed that knowledge graph-based suggestive feedback significantly improved group performance, knowledge elaboration, knowledge convergence, and coregulated behaviors compared to informative feedback and traditional online collaborative learning without any feedback. This study has theoretical and practical implications for feedback design and implementation in CSCL practice.
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institution Kabale University
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spelling doaj-art-a73481fa225a4b17835dd50266cffeab2025-08-20T03:42:11ZengSAGE PublishingSAGE Open2158-24402025-08-011510.1177/21582440251363298Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive FeedbackLanqin Zheng0Kaushal Kumar Bhagat1Miaolang Long2Nitesh Kumar Jha3 Faculty of Education, Beijing Normal University, Beijing, China Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur West Bengal, India Faculty of Education, Beijing Normal University, Beijing, China Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur West Bengal, IndiaComputer-supported collaborative learning (CSCL) has been broadly utilized in the field of education. However, learners often face difficulties in improving CSCL performance, including improved knowledge elaboration, knowledge convergence, and coregulation. Therefore, the present study aims to compare the effects of automated informative feedback and knowledge graph-based suggestive feedback on knowledge elaboration, knowledge convergence, and coregulation. This study adopted a convenience sampling method, and a total of 104 undergraduate students registered in a mandatory course voluntarily participated in a quasiexperimental study. The students in experimental Group 1 adopted knowledge graph-based suggestive feedback, the students in experimental Group 2 adopted automated informative feedback, and the students in the control group adopted traditional online collaborative learning without any feedback. The findings revealed that knowledge graph-based suggestive feedback significantly improved group performance, knowledge elaboration, knowledge convergence, and coregulated behaviors compared to informative feedback and traditional online collaborative learning without any feedback. This study has theoretical and practical implications for feedback design and implementation in CSCL practice.https://doi.org/10.1177/21582440251363298
spellingShingle Lanqin Zheng
Kaushal Kumar Bhagat
Miaolang Long
Nitesh Kumar Jha
Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback
SAGE Open
title Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback
title_full Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback
title_fullStr Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback
title_full_unstemmed Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback
title_short Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback
title_sort improving cscl performance a quasiexperimental study with control and intervention groups comparing informative and suggestive feedback
url https://doi.org/10.1177/21582440251363298
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