Nurfaizah Nurfaizah

Analisis Pola Aktivitas Belajar Mahasiswa pada Learning Management System Menggunakan Teknik Clustering

Introduction

Analisis pola aktivitas belajar mahasiswa pada learning management system menggunakan teknik clustering. Analisis pola aktivitas belajar mahasiswa di LMS dengan clustering. Temukan perilaku belajar beragam untuk pengembangan strategi pendidikan tinggi berbasis data.

Abstract

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

Review

This study presents a timely and relevant exploration into understanding student learning activity patterns within Learning Management Systems (LMS) through a clustering approach. Recognizing the vast, yet often underutilized, data generated by LMS platforms, the research effectively demonstrates the potential of systematic analysis to uncover student heterogeneity. By applying the K-Means algorithm to the Open University Learning Analytics Dataset (OULAD), the authors identify two distinct groups: students with high learning activity and those with lower or moderate engagement. This finding provides a valuable foundation for acknowledging and addressing the diverse ways students interact with online learning environments, setting a crucial precedent for data-driven educational insights. While the paper offers a significant initial step, several areas could benefit from further elaboration and methodological justification. The abstract mentions "feature extraction" from LMS activity data, but a more explicit description of the specific features (e.g., forum posts, resource views, quiz attempts) that define these activity patterns would greatly enhance the interpretability and practical utility of the results. Additionally, while the Silhouette Score assesses clustering quality, a more detailed rationale for the determination of *k*=2 (the number of clusters) would strengthen the methodological rigor. The generalizability of the findings is also an important consideration, as the study is focused on a single course and academic term from a specific institution, which may not fully represent the diversity of LMS usage across different educational contexts. Despite these points for refinement, this paper makes a pertinent contribution to the field of learning analytics by showcasing a practical application of data mining techniques to educational data. The identification of distinct learning activity patterns provides educators and researchers with an initial framework for monitoring student engagement and for developing more adaptive, data-informed pedagogical interventions. Future research could build upon this foundation by exploring a wider array of datasets across institutions and disciplines, delving deeper into the specific characteristics that define each activity group, and crucially, linking these patterns to actual learning outcomes such as academic performance or retention. Such extensions would move beyond pattern identification towards predictive analytics and targeted support strategies, ultimately enhancing student success and optimizing the learning experience within LMS.

Full Text

Comments

You need to be logged in to post a comment.