Modeling physical interaction and understanding peer group learning dynamics: graph analytics approach perspective

Abstract

Physical interaction in peer learning has been proven to improve students’ learning pro-cesses, 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.