VISUALIZATION OF COMPUTERSUPPORTED COLLABORATIVE LEARNING MODELS IN THE CONTEXT OF MULTIMODAL DATA ANALYSIS

Deep learning evaluation is a new direction formed by the intersection of multiple domains, and the core issue is how to visualize collaborative learning models to motivate learners. Therefore, this paper realizes real-time knowledge sharing and facilitates learners' interaction through compute...

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Bibliographic Details
Published in:3C empresa 2023-01, Vol.12 (1), p.87-109
Main Authors: Mei, Jianqiang, Chen, Wanyan, Li, Biyuan, Li, Shixin, Zhang, Jun
Format: Article
Language:English
Subjects:
CAI
Cognition
Collaborative learning
Collaborative virtual environments
computer
Computer assisted instruction
Data analysis
Deep learning
Intelligent networks
Knowledge management
Model testing
multimodality
online classroom
process analysis model
supported collaborative learning
Visualization
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Summary:Deep learning evaluation is a new direction formed by the intersection of multiple domains, and the core issue is how to visualize collaborative learning models to motivate learners. Therefore, this paper realizes real-time knowledge sharing and facilitates learners' interaction through computer-supported collaborative learning (CSCL) technology. In this paper, we collect, label, and analyze data based on five modalities: brain, behavior, cognition, environment, and technology. In this paper, a computer-supported collaborative learning process analysis model is developed under the threshold of multimodal data analysis. The model is based on roles and CSCL for intelligent network collaboration. This paper designs and develops an interactive visualization tool to support online collaborative learning process analysis. In addition, this paper conducts a practical study in an online classroom. The results show that the model and the tool can be effectively used for online collaborative learning process analysis, and the test model results fit well. The entropy index of the test model took a value of about 0.85, and about less than 10% of the individuals were assigned to the wrong profile. During the test, the participation of participants gradually increased from 5% to about 25%, and the participation effect improved by about 80%. This indicates the strong applicability value of the computer-supported collaborative learning process analysis model under the multimodal data analysis perspective.
ISSN:2254-3376
2254-3376
DOI:10.17993/3cemp.2023.120151.87-109