Making strides towards AI-supported regulation of learning in collaborative knowledge construction. (May 2023)

• Record Type: Journal Article
• Authors: Ouyang, Fan; Wu, Mian; Zhang, Liyin; Xu, Weiqi; Zheng, Luyi; Cukurova, Mutlu
• Is Part Of: Computers in human behavior. Volume 142(2023)
• Publication Date: 2023-05
• Subjects: Collaborative knowledge construction -- Computer-supported collaborative learning -- Multi-level characteristics -- Integrated analytics -- Higher education; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2023.107650
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Collaborative knowledge construction (CKC) requires individual group members to share information and resources, sustain the improvement of ideas through peer interactions, and the construction and development of collective knowledge at the group level. Investigations of this multi-level nature are of critical importance for developing AI-based regulation support systems for learners' CKC. This paper proposes a framework using an integrated analytics approach (Hidden Markov Model combined with Lag sequential analysis and Frequent sequence mining) for analyzing multi-level characteristics of CKC at cognitive and regulative behaviour dimensions. The approach was applied to a process-oriented discourse dataset of groups working collaboratively on concept-mapping activities. The results showed that the suggested approach can reveal insights into the multi-level (i.e., regulation behaviours likely to start at the group level, move to the peer-level and occasionally to individual-level self-regulation) and the dynamic nature of CKC (i.e., CKC appears to start from a group-level regulation pattern to move to a cognitive behaviour pattern of perspective sharing at the individual and peer levels, going back to regulation at individual and peer-levels and most likely end with a cognitive behaviour pattern at the group level). The novel models presented here can be used to classify learner behaviours into CKC states, enabling personalized scaffolding opportunities based on detected patterns in future intelligent support systems. Highlights: CKC emphasizes interconnections at individual, peer, and group levels. An AI-driven approach is used for analyzing multi-level characteristics of CKC. Results indicate transitions between regulations at the individual, peer, and group levels. Four patterns are detected integrating cognition and regulation dimensions. Practical and theoretical implications are proposed based on the empirical findings.