ANALISIS POLA AKTIVITAS BELAJAR MAHASISWA MENGGUNAKAN METODE CLUSTERING PADA OPEN UNIVERSITY LEARNING ANALYTICS DATASET (OULAD)
Authors
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Ahmad Maulana Ani Saputra
Universitas Nurul Huda
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Ferdi Rifaldo
Universitas Nurul Huda
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Wardianto
Universitas Nurul Huda
Keywords:
Learning Analytics, Clustering, K-Means, OULADAbstract
Learning analytics has become an important approach for understanding student learning behavior on online learning platforms. This study aims to analyze patterns of student learning activity using the K-Means clustering method on the Open University Learning Analytics Dataset (OULAD). The data used were obtained from studentInfo.csv and studentVle.csv, which were processed through preprocessing stages including removal of irrelevant attributes, missing value handling, duplicate data removal, activity filtering, total click aggregation, dataset merging, and outlier removal, resulting in a clean dataset of 25.559 rows. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, yielding k=3 as the best number of clusters with a Silhouette Score of 0.6543. The clustering results produced three groups of students, namely Cluster 0 with low activity (17.441 students or 68.24%), Cluster 1 with moderate activity (5.991 students), and Cluster 2 with high activity (2,127 students). The dominance of students with low activity indicates a significant engagement challenge on the online learning platform. These findings are expected to serve as a basis for educational institutions in designing early interventions to improve student engagement and academic achievement.
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Copyright (c) 2026 Ahmad Maulana Ani Saputra, Ferdi Rifaldo, Wardianto
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