Promoting knowledge elaboration, socially shared regulation, and group performance in collaborative learning: an automated assessment and feedback approach based on knowledge graphs

• Published in: International Journal of Educational Technology in Higher Education Vol. 20; no. 1; pp. 46 - 20
• Main Authors: Zheng, Lanqin, Long, Miaolang, Chen, Bodong, Fan, Yunchao
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; BioMed Central, Ltd; Springer Nature B.V; SpringerOpen
• Subjects: Artificial Intelligence; Automated assessment; Automation; Collaborative learning; College Students; Colleges & universities; Computer Appl. in Social and Behavioral Sciences; Computer Assisted Testing; Computer Science; Computers and Education; Concept Mapping; Control Groups; Cooperative Learning; Design of experiments; Educational Technology; Electronic Learning; Experimental Groups; Feedback; Feedback (Response); Foreign Countries; Graphs; Group dynamics; Higher Education; Humanities; Information Systems Applications (incl.Internet); Knowledge; Knowledge elaboration; Knowledge graph; Knowledge representation; Law; Learning; Learning Activities; Learning Analytics; Natural Language Processing; Qualitative analysis; Quasiexperimental Design; Research Article; Research Design; Socially shared regulation; Statistics for Social Sciences; China; Knowledge elaboration; Learning analytics; Socially shared regulation; Collaborative learning; Automated assessment; Knowledge graph
• ISSN: 2365-9440; 2365-9440
• DOI: 10.1186/s41239-023-00415-4

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.