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

Dados Bibliográficos

ID21297189
AutoresLanqin Zheng (0000-0001-9378-5027, School of Educational Technology Faculty of Education Beijing Normal University Beijing China), Jiayu Niu (School of Educational Technology Faculty of Education Beijing Normal University Beijing China), Lu Zhong (0009-0004-0910-0515, School of Educational Technology Faculty of Education Beijing Normal University Beijing China)
Ano2022
Volume53
Fascículo1
Páginas130-149
Data de publicação2022-01-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoBritish Journal of Educational Technology (JOURNAL)
Identificadores do periódicoISSN: 0007-1013 • E-ISSN: 1467-8535
EditoraWiley (PUBLISHER • GB)
DOI10.1111/bjet.13156
OpenAlexW3193466539
IdiomaEN
Citações recebidas27
Referências citadas58

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.

Collaborative learning · Computer-supported collaborative learning · Context (archaeology) · Convergence (economics) · Data science · Elaboration · Knowledge management · Learning analytics · Computer Science · E-Learning and Knowledge Management · Innovative Teaching and Learning Methods · Online Learning and Analytics

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