An Automatic Knowledge Graph Construction Approach to Promoting Collaborative Knowledge Building, Group Performance, Social Interaction and Socially Shared Regulation in CSCL

Autor/inn/en	Zheng, Lanqin; Niu, Jiayu; Long, Miaolang; Fan, Yunchao
Titel	An Automatic Knowledge Graph Construction Approach to Promoting Collaborative Knowledge Building, Group Performance, Social Interaction and Socially Shared Regulation in CSCL
Quelle	In: British Journal of Educational Technology, 54 (2023) 3, S.686-711 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13283
Schlagwörter	Cooperative Learning; Computer Assisted Instruction; Graphs; College Students; Interpersonal Relationship; Teaching Methods; Educational Technology; Computer Uses in Education + Suchen Sie Ihr Suchwort? Kooperatives Lernen; Computer based training; Computerunterstützter Unterricht; Grafische Darstellung; Collegestudent; Interpersonal relation; Interpersonal relations; Interpersonelle Beziehung; Zwischenmenschliche Beziehung; Teaching method; Lehrmethode; Unterrichtsmethode; Unterrichtsmedien; Computernutzung
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. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01