Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning

• Published in: Journal of Applied Engineering and Technological Science (Online) 2024-12, Vol.6 (1), p.744-753
• Main Authors: Thangavel, Chandrakumar, S E, Valliammai, P. P, Amritha, Chandran, Karthik, Chowdhury, Subrata, Thu, Nguyen Thi, Bao, Bo Quoc, Tran, Duc-Tan, Tran, Duc-Nghia, Trang, Do Quang
• Format: Article
• Language: English
• Subjects: COVID-19; Machine Learning; Management System; Moodle; Online Learning
• ISSN: 2715-6087; 2715-6079
• DOI: 10.37385/jaets.v6i1.6128

Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources.