Online Learning Student Engagement: RFM Model Perspective

• Published in: Proceedings - 2023 6th International Conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2023
• Main Author: Mustapha Y.; Md Harizan S.H.; Mahmud N.; Mansor S.; Saad S.M.; Hilmi M.F.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163796187&doi=10.1109%2fWiDS-PSU57071.2023.00026&partnerID=40&md5=2842d1998796134a659caa2ba4cc8df8

Engagement in learning is seen as a key factor in figuring out how well online courses work. Perception-based surveys have been used to get a better idea of how students learn. The fact that a learning management system can keep track of log data from online learning activities opens up the possibility of adding data-driven measures to survey-based measures of student engagement. The purpose of this research is to investigate whether or not it is possible to construct a model of students' levels of engaged learning using the information taken from their online learning logs. Based on the findings of this study, a model of student engagement could be constructed using a marketing research methodology to analyze student online learning behavior. A fresh approach to determining how actively students are participating in their own education is provided by the methodology suggested in this research. What we already know about perception-based student learning engagement will be added to and improved by the information that can be gained from a new perspective on student learning engagement. © 2023 IEEE.