IJIET 2025 Vol.15(5): 1024-1044
doi: 10.18178/ijiet.2025.15.5.2308
doi: 10.18178/ijiet.2025.15.5.2308
Using Clustering Techniques to Understand Student Involvement in an Online Environment
Vandana Naik1,2,* and Venkatesh Kamat3
1. Goa Business School, Goa University, Goa, India
2. Centre for Research, Development and Innovation, Goa State Higher Education Council, Directorate of Higher Education, Goa, India
3. School of Mathematics and Computer Science, Indian Institute of Technology, Goa, India
Email: dcst.vandana@unigoa.ac.in (V.N); vvkamat@iitgoa.ac.in (V.K.)
*Corresponding author
2. Centre for Research, Development and Innovation, Goa State Higher Education Council, Directorate of Higher Education, Goa, India
3. School of Mathematics and Computer Science, Indian Institute of Technology, Goa, India
Email: dcst.vandana@unigoa.ac.in (V.N); vvkamat@iitgoa.ac.in (V.K.)
*Corresponding author
Manuscript received August 13, 2024; revised September 4, 2024; accepted January 3, 2025; published May 21, 2025
Abstract—Student engagement in online learning environments is critical in improving educational outcomes and instructional strategies. Previous studies on engagement patterns using online log datasets often focus on interaction frequency, neglecting intensity and comprehensive activity coverage. This study addresses these gaps by introducing a novel approach grounded in the Community of Inquiry (CoI) model to calculate engagement parameters. The research objectives include deriving meaningful engagement metrics, clustering students based on these metrics, and evaluating clustering algorithms to identify the most effective method. The methodology involves processing Moodle log data to extract three key engagement parameters: Number of sessions, session duration, and engagement levels encompassing social and cognitive dimensions. These derived parameter values were then compared to the labels set manually by two raters. High agreement (0.9409 correlation) between these two methods validates the algorithm’s efficiency and reliability in measuring student engagement. Next, clustering algorithms, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), K-means, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), etc., are applied to group students, with cluster quality assessed using indices like Davies-Bouldin, silhouette coefficient, and Calinski-Harabasz. The findings reveal that Kmeans and Birch algorithms effectively categorize students, with the CoI-derived engagement parameters proving to be the most influential. These insights highlight the critical role of cognitive and social interactions in engagement and demonstrate the superiority of such methods in discovering patterns in student data. This study provides a robust framework for analyzing student engagement, offering actionable insights for educators to enhance online learning experiences.
Keywords—engagement, agglomerative hierarchy clustering algorithm, K-means, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM)
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—engagement, agglomerative hierarchy clustering algorithm, K-means, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM)
Cite: Vandana Naik and Venkatesh Kamat, "Using Clustering Techniques to Understand Student Involvement in an Online Environment," International Journal of Information and Education Technology, vol. 15, no. 5, pp. 1024-1044, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).