An Artificial Intelligence driven Learning Analytics Method to Examine the Collaborative Problem solving Process from a Complex Adaptive Systems Perspective
Project Overview
The document explores the role of generative AI in education, specifically within the context of collaborative problem-solving (CPS) in online learning environments. It emphasizes the integration of AI-driven learning analytics with multimodal data to assess the complexities inherent in CPS. The research identifies three distinct collaborative patterns that correlate with varying performance levels, highlighting the dynamic and synergistic interactions among group members. These findings suggest that understanding CPS through the lens of complex adaptive systems theory can provide significant theoretical, analytical, and pedagogical insights, ultimately enhancing collaborative learning experiences. By leveraging generative AI, educators can better facilitate and analyze group interactions, leading to improved educational outcomes and deeper engagement in collaborative tasks.
Key Applications
AI-driven learning analytics to analyze collaborative problem-solving processes in online learning
Context: Graduate-level seminar courses on Distance and Online Education and Online Learning Analytics
Implementation: Used a three-layered analytical framework integrating AI algorithms with learning analytics to analyze multimodal data collected from CPS activities.
Outcomes: Identified three types of collaborative patterns (behavior-oriented, communication-behavior-synergistic, and communication-oriented) with varying levels of performance.
Challenges: Methodological difficulties in applying traditional statistics to complex adaptive systems; difficulties in capturing the dynamic interrelations among dimensions of collaboration.
Implementation Barriers
Methodological
Traditional educational research methods may not adequately model the complexity of collaborative problem-solving processes.
Proposed Solutions: Integrate AI-driven learning analytics and multimodal data analysis for a more holistic and dynamic understanding of CPS.
Cultural/Social
Weak connections with the socio-emotional dimension in collaborative learning due to cultural factors that discourage expression of emotions.
Proposed Solutions: Instructors can provide social support to enhance the emotional engagement of students during collaboration.
Project Team
Fan Ouyang
Researcher
Weiqi Xu
Researcher
Mutlu Cukurova
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Fan Ouyang, Weiqi Xu, Mutlu Cukurova
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai