An Artificial Intelligence‐Enabled Group Cognitive Diagnosis Approach With the Goal of Promoting Online Collaborative Learning.
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 artif...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 5; pp. 1 - 16 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188234211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188234211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Oct2025 vid: 41 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 188234211 188234211 188234211 10.1111/jcal.70113 188234211 ppf: 1 ppct: 15 formats: tig: atl: An Artificial Intelligence‐Enabled Group Cognitive Diagnosis Approach With the Goal of Promoting Online Collaborative Learning. aug: au: Zheng, Lanqin Huang, Zichen Gao, Lei Fan, Yunchao affil: School of Educational Technology, Faculty of Education, Beijing Normal University, Beijing, China sug: subj: Artificial Intelligence Collaboration Learning Methods Cognition Online Education Goals and Objectives Diffusion of Innovation Health Promotion Human Male Female Young Adult Funding Source China Students, College Quasi-Experimental Studies Multimethod Studies Descriptive Statistics Semi-Structured Interview Comparative Studies Interrater Reliability Content Analysis Thematic Analysis Analysis of Covariance Post Hoc Analysis ROC Curve Feedback Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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