An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL. (11th October 2022)

• Record Type: Journal Article
• Authors: Zheng, Lanqin; Niu, Jiayu; Long, Miaolang; Fan, Yunchao
• Is Part Of: British journal of educational technology. Volume 54:Number 3(2023)
• Page Start: 686
• Page End: 711
• Publication Date: 2022-10-11
• Subjects: CSCL -- collaborative knowledge building -- group performance -- knowledge graph -- social interaction -- socially shared regulation; Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.13283
• Languages: English
• ISSNs: 0007-1013

Abstract

Abstract: Computer‐supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance. Practitioner notes What is already known about this topic Knowledge graphs have been widely used in many fields for searches, recommendations, analytics, and automation. Researchers have explored how to construct knowledge graphs. However, few studies have automatically constructed knowledge graphs in the CSCL context. What this paper adds An automatic knowledge graph construction approach is proposed and validated in the CSCL context. The findings indicate that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. Implications for practice and/or policy The AAUKG approach is an effective method for improving knowledge building and group performance in the CSCL context. Researchers and practitioners are encouraged to consider how knowledge graphs can be applied to facilitate collaborative learning efficiently.