Promoting knowledge elaboration, socially shared regulation, and group performance in collaborative learning: an automated assessment and feedback approach based on knowledge graphs
Lanqin Zheng, Miaolang Long, Bodong Chen, Yunchao Fan
Abstract
Abstract Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated assessment and feedback approach that is based on knowledge graph and artificial intelligence technologies. Following a quasi-experimental design, we assigned a total of 108 college students into two conditions: an experimental group that participated in online collaborative learning and received automated assessment and feedback from the tool, and a control group that participated in the same collaborative learning activities without automated assessment and feedback. Analyses of quantitative and qualitative data indicated that the introduced automated assessment and feedback significantly promoted group performance, knowledge elaboration, and socially shared regulation of collaborative learning. The proposed knowledge graph-based automated assessment and feedback approach shows promise in providing a valuable tool for researchers and practitioners to support online collaborative learning.