doi: 10.18178/ijssh.2026.16.1.1274
Activity Management System for Enhancing Online Classroom Interaction Using Support Vector Machine and Random Forest
Akaraj Chunhapinyokul
*
,
Lachana Ramingwong
,
Sakgasit Ramingwong
,
Kenneth Cosh
,
Narissara Eiamkanitchat
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand
- Manuscript received June 9, 2025
- accepted September 9, 2025
- published February 11, 2026
Abstract
This paper proposes an Activity Management System (AMS) that uses Support Vector Machine (SVM) and Random Forest (RF) algorithms to classify students into different interaction levels in online classrooms. The system captures behavioral features such as time spent online and chat activity to form data-driven student groups. The approach addresses the lack of interaction in virtual learning environments and promotes more equitable and engaging peer collaboration. The system’s effectiveness was validated using real and CTGAN-generated data, showing improvements in interaction compared to random grouping.
Keywords
- online learning
- student engagement
- Support Vector Machine (SVM)
- Random Forest (RF)
- machine learning in education
- interaction classification
How to Cite
Akaraj Chunhapinyokul, Lachana Ramingwong, Sakgasit Ramingwong, Kenneth Cosh, and Narissara Eiamkanitchat, "Activity Management System for Enhancing Online Classroom Interaction Using Support Vector Machine and Random Forest," International Journal of Social Science and Humanity, vol. 16, no. 1, pp. 28-32, 2026. https://doi.org/ 10.18178/ijssh.2026.16.1.1274
Copyright & License
Copyright © 2026 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).