Proceedings of the 3rd International Conference on Educational Innovation and Multimedia Technology, EIMT 2024, March 29–31, 2024, Wuhan, China

Research Article

Understanding Collaborative Knowledge Building for Diverse Students with LSA

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347729,
        author={Yuyan  Fu and Yinjie  Yang and Yujiao  Cheng and Huimin  Lyu},
        title={Understanding Collaborative Knowledge Building for Diverse Students with LSA},
        proceedings={Proceedings of the 3rd International Conference on Educational Innovation and Multimedia Technology, EIMT 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={EIMT},
        year={2024},
        month={6},
        keywords={knowledge building; virtual learning community; learning behavior analysis},
        doi={10.4108/eai.29-3-2024.2347729}
    }
    
  • Yuyan Fu
    Yinjie Yang
    Yujiao Cheng
    Huimin Lyu
    Year: 2024
    Understanding Collaborative Knowledge Building for Diverse Students with LSA
    EIMT
    EAI
    DOI: 10.4108/eai.29-3-2024.2347729
Yuyan Fu1,*, Yinjie Yang1, Yujiao Cheng1, Huimin Lyu1
  • 1: South China Normal University
*Contact email: 2022020848@m.scnu.edu.cn

Abstract

Collaborative knowledge building, a common activity among students in virtual learning communities, has garnered the attention of researchers. Existing studies often analyze the process of collaborative knowledge building within these communities through implicit interaction behaviors, while research focusing on explicit operational behaviors documented in activity logs is relatively scarce. Analysis based on explicit operational behaviors can provide a more comprehensive understanding of the collaborative knowledge building process, which is beneficial for enhancing student’s Knowledge building levels. This study selected a course in which 50 students engaged in collaborative knowledge building activities through virtual learning communities. The study employed lag sequence analysis to examine the behavioral data generated by student activities and conducted group analysis for learners at different proficiency levels. The findings indicate that analysis based on explicit operational behaviors effectively identifies significant behavioral sequences and characteristics. The overall behavioral patterns of learners align with Knowledge building theory, and the higher engagement of high performance group learners during the knowledge convergence phase is a primary factor affecting the level of collaborative knowledge building.