An Artificial Intelligence-Enabled Group Cognitive Diagnosis Approach With the Goal of Promoting Online Collaborative Learning

IF 4.6 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2025-08-29 DOI:10.1111/jcal.70113

Lanqin Zheng, Zichen Huang, Lei Gao, Yunchao Fan

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引用次数: 0

Abstract

Background

Online collaborative learning has been broadly applied in the field of higher education. Nevertheless, not all types of collaborative learning can produce the desired learning results.

Objectives

To facilitate online collaborative learning, the present study proposed an innovative artificial intelligence-enabled group cognitive diagnosis approach with the goal of improving online collaborative learning.

Methods

A total of 135 college students was included in the current study and divided into 45 groups. A total of 15 groups consisting of 45 students used the group cognitive diagnosis approach. An additional 15 groups were assigned to the group knowledge graph approach, while the remaining 15 groups were assigned to the traditional online collaborative learning approach.

Results and Conclusions

The findings of this research indicated that the group cognitive diagnosis approach had more significant and positive impacts on collaborative learning performance, knowledge elaboration, and higher-order cognitive engagement than did the group knowledge graph and traditional online collaborative learning approaches.

Implications

The current study deepens our understanding of group cognition and the corresponding complex interactions and provides a new method for improving online collaborative learning.

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以促进在线协作学习为目标的人工智能支持的群体认知诊断方法

在线协作学习在高等教育领域得到了广泛的应用。然而，并不是所有类型的协作学习都能产生预期的学习结果。为了促进在线协作学习，本研究提出了一种创新的基于人工智能的群体认知诊断方法，旨在改善在线协作学习。方法将135名大学生分为45组。15组45名学生采用群体认知诊断方法。另外15组被分配到小组知识图方法，而其余15组被分配到传统的在线协作学习方法。结果与结论本研究结果表明，群体认知诊断方法对协作学习绩效、知识阐述和高阶认知投入的影响比群体知识图谱和传统的在线协作学习方法更为显著和积极。本研究加深了我们对群体认知及其复杂相互作用的认识，为改进在线协作学习提供了一种新的方法。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope