A revised Girvan–Newman Clustering Algorithm for Cooperative Groups Detection in Programming Learning

Wen-Chih Chang1

  1. International Master Program in Information Technology and Applications
    National Pingtung University, Pingtung City 900, Taiwan

Abstract

Learning to program is a challenging task for novices. Students vary substantially in their ability to understand complex and abstract topics in computer programming logic, such as loop logic, function recursion, arrays, passing parameters, and program structure design. Cooperative learning is an effective method of learning and teaching programming. In traditional cooperative learning, students group themselves, or teachers group students intuitively. This paper proposes a clustering method based on item response theory (IRT) and the revised Girvan–Newman clustering for clustering students by learning ability. Item response theory calculated the learner’s ability and interpersonal relationship questionnaire generated by the social network analysis. The proposed method was validated by conducting a quasi-experimental test in a freshmen programming course, and the method significantly improved learning outcomes in this course.

Key words

Learner ability, Girvan–Newman clustering, Social Network Analysis, Programming

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS220830069C

Publication information

Volume 21, Issue 2 (April 2024)
Special Issue on Deep Learning Techniques in Intelligent Internet of Things and 5G Communication Networks
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Chang, W.: A revised Girvan–Newman Clustering Algorithm for Cooperative Groups Detection in Programming Learning. Computer Science and Information Systems, Vol. 21, No. 2, 491–505. (2024), https://doi.org/10.2298/CSIS220830069C