Analysis of Student Engagement Levels in E-Learning Using Clustering Methods

Aoni Aslihatin; Nisreen Alzahrani; Alejandro Mozo Quesada

doi:10.70716/jocsit.v2i3.550

Authors

Aoni Aslihatin Program Studi Teknik Informatika, Universitas Mataram, Mataram, Indonesia
Nisreen Alzahrani Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
Alejandro Mozo Quesada Department of Computer Science, University of Granada, Granada, Spain

DOI:

https://doi.org/10.70716/jocsit.v2i3.550

Keywords:

E-learning , Clustering , K-Mean, Student Engagement, Learning Analytics

Abstract

The rapid growth of e-learning has increased the need to analyze student engagement levels in online learning environments. This study aims to analyze student activity levels using a clustering method based on activity log data from a Learning Management System (LMS). The approach applied is K-Means Clustering with an exploratory method on student activity data, including frequency of access to learning materials, discussion forum participation, assignment submission, and duration of learning activities. The sample consisted of 200 Informatics Engineering students selected through purposive sampling, with data collected over one academic semester. The optimal number of clusters was determined using the elbow method, while cluster quality was evaluated using the Silhouette Score. The results show that students can be grouped into three main clusters: high, medium, and low engagement levels. The high-engagement cluster showed consistent participation and potential correlation with better academic performance, while the low-engagement cluster indicated a higher risk of academic difficulty. These findings are consistent with previous studies showing that interaction levels are strongly correlated with academic performance. Clustering methods proved effective in automatically identifying student behavior patterns, thereby supporting the development of data-driven adaptive learning strategies.

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References

Ahuja, R., Agarwal, P. P., & Kumar, T. (2021). Clustering Methods Analysis In The E-Learning. In Advances In Intelligent Systems And Computing. Https://Doi.Org/10.1007/978-981-15-7961-5_118

Alzahrani, N., Meccawy, M., & Samra, H. (2025). Identifying Weekly Student Engagement Patterns In E-Learning Via K-Means Clustering And Label-Based Validation. Electronics. Https://Doi.Org/10.3390/Electronics14153018

Bara, M. W., Ahmad, N. B., & Modu, M. M. (2018). Self-Organizing Map Clustering Method For The Analysis Of E-Learning Activities. In Proceedings Of The International Conference (MINTC). Https://Doi.Org/10.1109/MINTC.2018.8363155

Bhurre, S., Raikwar, S., & Prajapat, S. (2023). Analyzing And Comparing Clustering Algorithms For Student Academic Data. Https://Doi.Org/10.1007/978-3-031-47508-5_49

Chen, J., Huang, K., & Wang, F. (2009). E-Learning Behavior Analysis Based On Fuzzy Clustering. In Proceedings Of WGEC. Https://Doi.Org/10.1109/WGEC.2009.214

Cobo Rodríguez, G. (2014). Parameter-Free Agglomerative Hierarchical Clustering To Model Learners' Activity In Online Discussion Forums (Doctoral Dissertation).

Delgado, S., Morán, F., San Jose, J. C., Et Al. (2021). Analysis Of Students’ Behavior Through User Clustering In Online Learning Settings, Based On Self Organizing Maps Neural Networks. IEEE Access. Https://Doi.Org/10.1109/ACCESS.2021.3115024

Evans, J., & Nataliani, Y. (2024). Analisis Perilaku Siswa Pada Pembelajaran Daring Menggunakan K-Means Clustering Dari Log Data Moodle Pada SMPN 6 Salatiga. Jusitik (Jurnal Sistem Dan Teknologi Informasi Komunikasi). Https://Doi.Org/10.32524/Jusitik.V7i2.1108

Farías, R., Durán, E. B., & Figueroa, S. G. (2008). Detección De Estilos De Aprendizaje Mediante Técnicas De Análisis De Cluster.

Farías, R., Durán, E. B., & Figueroa, S. G. (2008). Las Técnicas De Clustering En La Personalización De Sistemas De E-Learning.

Filipović, D., Balaban, I., & Oreški, D. (2018). Cluster Analysis Of Students’ Activities From Logs And Their Success In Self-Assessment Tests.

Güvenç, E., & Çetin, G. (2018). Clustering Of Participation Degrees Of Distance Learning Students To Course Activity By Using Fuzzy C-Means Algorithm. In Proceedings Of SIU. Https://Doi.Org/10.1109/SIU.2018.8404292

Hussain, M., Hussain, S., Zhang, W., Et Al. (2018). Mining Moodle Data To Detect The Inactive And Low-Performance Students During The Moodle Course. In Proceedings Of ACM. Https://Doi.Org/10.1145/3291801.3291828

Ikhsan, E. (2021). Penerapan K-Means Clustering Dari Log Data Moodle Untuk Menentukan Perilaku Peserta Pada Pembelajaran Daring.

Kim, S. M., Cho, S.-H., & Kim, J. Y. (2023). Statistical Assessment On Student Engagement In Asynchronous Online Learning Using The K-Means Clustering Algorithm. Sustainability. Https://Doi.Org/10.3390/Su15032049

Leksono, M. R., & Prastiyanto, A. E. (2014). Penerapan Algoritma Clustering Untuk Mengelompokkan Ketertarikan Siswa Berdasarkan Aktivitas Di Model Pembelajaran Elektronik. Semnasteknomedia Online.

Mozo Quesada, A., López Valverde, F., & Gálvez Rojas, S. (2022). Clustering Students’ Online Behavior And Relation With Academic Performance. In Proceedings Of ICECET. Https://Doi.Org/10.1109/ICECET55527.2022.9872960

Octaviani, D., Othman, M. S., & Yusof, N. (2015). Applied Clustering Analysis For Grouping Behaviour Of E-Learning Usage Based On Meaningful Learning Characteristics. Jurnal Teknologi. Https://Doi.Org/10.11113/JT.V76.3904

Parvathavarthini, S., Sharvanthika, K. S., & Jagadeesh, M. (2021). Analysis Of Student Performance In E-Learning Environment Using Crow Search Based Fuzzy Clustering. In Proceedings Of ICOSEC. Https://Doi.Org/10.1109/ICOSEC51865.2021.9591920

Pecuchova, J., & Drlík, M. (2024). Enhancing The Early Student Dropout Prediction Model Through Clustering Analysis Of Students’ Digital Traces. IEEE Access. Https://Doi.Org/10.1109/Access.2024.3486762

Peraic, I., & Grubišić, A. (2023). Exploring Student Engagement In Online Programming Courses: A Two-Level K-Means Analysis. In Proceedings Of Softcom. Https://Doi.Org/10.23919/Softcom58365.2023.10271619

Tamba, A. R., Lumbantoruan, K., & Pakpahan, A. (2023). A Cluster And Association Analysis Visualization Using Moodle Activity Log Data. International Journal Of Informatics And Communication Technology. Https://Doi.Org/10.11591/Ijict.V12i2.Pp150-161

Triayudi, A., & Fitri, I. (2019). A New Agglomerative Hierarchical Clustering To Model Student Activity In Online Learning. TELKOMNIKA (Telecommunication Computing Electronics And Control). Https://Doi.Org/10.12928/TELKOMNIKA.V17I3.9425

Vela-Pérez, M., Hernández-Estrada, A., & Tirado Domínguez, G. (2017). Learning Analytics To Classify Students According To Their Activity In Moodle. In Proceedings Of EDULEARN. Https://Doi.Org/10.21125/EDULEARN.2017.1241

Wang, J., & Zhang, Y. (2019). Clustering Study Of Student Groups Based On Analysis Of Online Learning Behavior. In Proceedings Of ACM. Https://Doi.Org/10.1145/3341042.3341065

Xue, Y., Qin, J., Su, S., Et Al. (2021). Brain Storm Optimization Based Clustering For Learning Behavior Analysis. Computer Systems: Science & Engineering. Https://Doi.Org/10.32604/CSSE.2021.016693

Zakrzewska, D. (2008). Using Clustering Technique For Students' Grouping In Intelligent E-Learning Systems. In Lecture Notes In Computer Science. Https://Doi.Org/10.1007/978-3-540-89350-9_28