Modeling Physical Interaction and Understanding Peer Group Learning Dynamics: Graph Analytics Approach Perspective

Zuraida Abal Abas, Mohd Natashah Norizan, Zaheera Zainal Abidin, Ahmad Fadzli Nizam Abdul Rahman, Hidayah Rahmalan, Ida Hartina Ahmed Tharbe, Wan Farah Wani Wan Fakhruddin, Nurul Hafizah Mohd Zaki and Sharizal Ahmad Sobri
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Zuraida Abal Abas: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
Mohd Natashah Norizan: Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia
Zaheera Zainal Abidin: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
Ahmad Fadzli Nizam Abdul Rahman: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
Hidayah Rahmalan: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
Ida Hartina Ahmed Tharbe: Department of Educational Psychology and Counseling, Faculty of Education, Universiti of Malaya, Kuala Lumpur 50603, Malaysia
Wan Farah Wani Wan Fakhruddin: Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Nurul Hafizah Mohd Zaki: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
Sharizal Ahmad Sobri: Geopolymer and Green Technology, Centre of Excellence (CEGeoGTech), Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia

Mathematics, 2022, vol. 10, issue 9, 1-18

Abstract: Physical interaction in peer learning has been proven to improve students’ learning processes, which is pertinent in facilitating a fulfilling learning experience in learning theory. However, observation and interviews are often used to investigate peer group learning dynamics from a qualitative perspective. Hence, more data-driven analysis needs to be performed to investigate the physical interaction in peer learning. This paper complements existing works by proposing a framework for exploring students’ physical interaction in peer learning based on the graph analytics modeling approach focusing on both centrality and community detection, as well as visualization of the graph model for more than 50 students taking part in group discussions. The experiment was conducted during a mathematics tutorial class. The physical interactions among students were captured through an online Google form and represented in a graph model. Once the model and graph visualization were developed, findings from centrality analysis and community detection were conducted to identify peer leaders who can facilitate and teach their peers. Based on the results, it was found that five groups were formed during the physical interaction throughout the peer learning process, with at least one student showing the potential to become a peer leader in each group. This paper also highlights the potential of the graph analytics approach to explore peer learning group dynamics and interaction patterns among students to maximize their teaching and learning experience.

Keywords: physical interaction; peer learning; graph analytics; centrality analysis; community detection; peer leaders (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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