Effects of a learning analytics-based real-time feedback approach on knowledge elaboration, knowledge convergence, interactive relationships and group performance in CSCL

被引:27
|
作者
Zheng, Lanqin [1 ]
Niu, Jiayu [1 ]
Zhong, Lu [1 ]
机构
[1] Beijing Normal Univ, Fac Educ, Sch Educ Technol, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
computer-supported collaborative learning; learning analytics; real-time feedback; STUDENTS; EXPERIENCE; IMPACT; GOALS;
D O I
10.1111/bjet.13156
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Learning analytics (LA) has been widely adopted in research on education. However, most studies in the area have conducted LA after computer-supported collaborative learning (CSCL) activities rather than during CSCL. To address this problem, this study proposed a LA-based real-time feedback approach based on a deep neural network model to improve CSCL performance. In total, 72 university students participated in the study and were randomly assigned to an experimental or control group. The students in the experimental group learned with the LA-based real-time feedback approach, whereas the students in the control group learned with the conventional online collaborative learning approach. To analyse the data, both quantitative and qualitative methods were adopted. The results indicated that the LA-based real-time feedback approach significantly promoted knowledge convergence, knowledge elaboration, interactive relationships and group performance. The interview results also confirmed the effectiveness of the proposed approach. Practitioner notes What is already known regarding this topic Learning analytics (LA) has been widely used in education. Most studies in the area have presented LA results only after collaborative learning and have lacked real-time analysis and feedback. What this paper adds A LA-based real-time feedback approach was proposed and validated in the computer-supported collaborative learning (CSCL) context. The experimental results indicated that the LA-based real-time feedback approach significantly promoted knowledge elaboration, knowledge convergence, interactive relationships and group performance. Implications for practice and/or policy To shed light on progress in CSCL, real-time LA are recommended. Deep neural network models, such as bidirectional encoder representations from transformers, can be adopted to automatically analyse online discussion transcripts. Real-time feedback based on LA results can promote CSCL performance.
引用
收藏
页码:130 / 149
页数:20
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    [J]. INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 2023, 20 (01)
  • [2] Promoting knowledge elaboration, socially shared regulation, and group performance in collaborative learning: an automated assessment and feedback approach based on knowledge graphs
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    Miaolang Long
    Bodong Chen
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    [J]. International Journal of Educational Technology in Higher Education, 20
  • [3] Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
    Lim L.
    Bannert M.
    van der Graaf J.
    Singh S.
    Fan Y.
    Surendrannair S.
    Rakovic M.
    Molenaar I.
    Moore J.
    Gašević D.
    [J]. Computers in Human Behavior, 2023, 139
  • [4] An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL
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    Long, Miaolang
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    [J]. INTERNATIONAL JOURNAL OF COMPUTER-SUPPORTED COLLABORATIVE LEARNING, 2023, 18 (01) : 101 - 133
  • [5] An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL
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    Miaolang Long
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    Lu Zhong
    [J]. International Journal of Computer-Supported Collaborative Learning, 2023, 18 : 101 - 133
  • [6] A KNOWLEDGE BASED APPROACH FOR REAL-TIME SYSTEMS DEBUGGING
    TSAI, JP
    FANG, KY
    THALLA, VRK
    GANDHI, H
    [J]. PROCEEDINGS OF THE TWENTY-FIRST, ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, VOLS 1-4: ARCHITECTURE TRACK, SOFTWARE TRACK, DECISION SUPPORT AND KNOWLEDGE BASED SYSTEMS TRACK, APPLICATIONS TRACK, 1988, : B533 - B540
  • [7] The Impact of Cross-age Peer Tutors on Knowledge Elaboration, Knowledge Convergence, and Group Performance in Computer Supported Collaborative Learning
    Zheng, Lanqin
    [J]. 15TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2015), 2015, : 175 - 179
  • [8] Real-time ICT-based interactive learning analytics to facilitate blended classrooms
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  • [9] The Impact of Assigned Roles and Cross-Age Peer Tutors on Knowledge Elaboration, Knowledge Convergence, and Group Performance in Synchronous Online Learning Environment
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    [J]. STATE-OF-THE-ART AND FUTURE DIRECTIONS OF SMART LEARNING, 2016, : 221 - 230
  • [10] A novel approach to assess collaborative learning processes and group performance through the knowledge convergence
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    [J]. Journal of Computers in Education, 2014, 1 (2-3) : 167 - 185