An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective

Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspec...

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Published in:	International journal of computer-supported collaborative learning Vol. 18; no. 1; pp. 39 - 66
Main Authors:	Ouyang, Fan, Xu, Weiqi, Cukurova, Mutlu
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2023 Springer Springer Nature B.V
Subjects:	Adaptive systems Algorithms Artificial Intelligence Cognitive Psychology Cognitive tasks Collaboration Complexity Computers and Education Concept mapping Cooperative Learning Education Educational Technology Empirical analysis Learning Analytics Learning and Instruction Machine learning Mathematical analysis Problem Solving Self organizing systems Systems Approach User Interfaces and Human Computer Interaction Collaborative pattern Learning analytics AI-driven learning analytics Complex adaptive systems theory Collaborative problem solving Computer-supported collaborative learning
ISSN:	1556-1607, 1556-1615
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Abstract	Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.
AbstractList	Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.
Author	Xu, Weiqi Cukurova, Mutlu Ouyang, Fan
Author_xml	– sequence: 1 givenname: Fan orcidid: 0000-0002-4382-1381 surname: Ouyang fullname: Ouyang, Fan organization: College of Education, Zhejiang University – sequence: 2 givenname: Weiqi surname: Xu fullname: Xu, Weiqi organization: College of Education, Zhejiang University – sequence: 3 givenname: Mutlu surname: Cukurova fullname: Cukurova, Mutlu email: m.cukurova@ucl.ac.uk organization: UCL Knowledge Lab, University College London
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Snippet	Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued...
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SubjectTerms	Adaptive systems Algorithms Artificial Intelligence Cognitive Psychology Cognitive tasks Collaboration Complexity Computers and Education Concept mapping Cooperative Learning Education Educational Technology Empirical analysis Learning Analytics Learning and Instruction Machine learning Mathematical analysis Problem Solving Self organizing systems Systems Approach User Interfaces and Human Computer Interaction
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Title	An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective
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