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

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Listed:
  • 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
    Geopolymer and Green Technology, Centre of Excellence (CEGeoGTech), 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
    Advanced Material Research Cluster, Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Jeli 17600, Malaysia)

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.

Suggested Citation

  • 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 Z, 2022. "Modeling Physical Interaction and Understanding Peer Group Learning Dynamics: Graph Analytics Approach Perspective," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1430-:d:800904
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    References listed on IDEAS

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    1. Attila Mester & Andrei Pop & Bogdan-Eduard-Mădălin Mursa & Horea Greblă & Laura Dioşan & Camelia Chira, 2021. "Network Analysis Based on Important Node Selection and Community Detection," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    2. Meead Saberi & Hani S. Mahmassani & Dirk Brockmann & Amir Hosseini, 2017. "A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks," Transportation, Springer, vol. 44(6), pages 1383-1402, November.
    3. Vinícius da Fonseca Vieira & Carolina Ribeiro Xavier & Nelson Francisco Favilla Ebecken & Alexandre Gonçalves Evsukoff, 2014. "Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-15, December.
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    1. Nurul Zahirah Abd Rahim & Nurun Najwa Bahari & Nur Syaza Mohd Azzimi & Zamira Hasanah Zamzuri & Hafizah Bahaludin & Nurul Farahain Mohammad & Fatimah Abdul Razak, 2023. "Comparing Friends and Peer Tutors Amidst COVID-19 Using Social Network Analysis," Mathematics, MDPI, vol. 11(4), pages 1-17, February.

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