Enhancing Predictive Analytics for Students' Performance in Moodle: Insight from an Empirical Study

Abstract

This paper explores the application of learning analytics in predicting students' performance (SP) within Moodle, a widely used Learning Management System (LMS). The study focuses on measurable academic progress and outcomes, aiming to assist educators in early identification and resolution of issues to boost student productivity and success. Our approach began with a literature review to identify predictive attributes for student performance. We then collected and analyzed data from a year-long study involving 160 students at the Cambodia Academy of Digital Technology (CADT). The dataset included attendance, interaction logs, quiz submissions, task completions, assignments, time spent on courses, and outcome scores. We utilized these data points to train and evaluate various classifiers, identifying the Random Forest classifier as the most effective. A predictive algorithm was developed using the coefficients from this classifier, tailored for practical application in educational settings. Our analysis confirmed significant correlations between the identified attributes and prediction accuracy, enhancing the algorithm's efficacy. A follow-up survey with the same participants one year later provided further validation, affirming the predictive indicators' effectiveness in improving academic performance. This comprehensive approach demonstrates the robustness of our findings and underscores the potential of predictive analytics in enhancing educational outcomes.

Received: 6 July 2024 | Revised: 3 September 2024 | Accepted: 20 September 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Dynil Duch: Conceptualization, Methodology, Software, Investigation, Data Curation, Writing - original draft, Writing - Review & Editing, Project administration. Madeth May: Conceptualization, Methodology, Validation, Formal analysis, Writing - Review & Editing, Supervision, Funding acquisition, Project administration. Sébastien George: Validation, Writing - Review & Editing, Supervision, Project administration.