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Vol.14, No.1, February 2025. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
A Performance-Driven Exploration of Combining Topic Modeling and Machine Learning for Online Learning Data Analysis
Pachisa Kulkanjanapiban, Tipawan Silwattananusarn
© 2025 Tipawan Silwattananusarn, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 14, Issue 1, Pages 511-527, ISSN 2217-8309, DOI: 10.18421/TEM141-46, February 2025.
Received: 11 June 2024. Revised: 04 December 2024.
Abstract:
The research aims to determine how well three clustering algorithms K-Means, hierarchical, and ensemble clustering work when combined with sophisticated topic modeling methods such as latent Dirichlet allocation (LDA), or nonnegative matrix factorization (NMF), and latent semantic analysis (LSA) on online learning datasets. The study’s data was scrapped from online learning resource platforms, i.e., Coursera, Udacity, edX, and FutureLearn in 2023and from Kaggle in 2021. Findings demonstrate that the LDA is more appropriate for the clustered data points generating topics. LDA-based clustering performs more efficiently than NMF-and LSA-based clustering when ensemble clustering is used. The proposed combination approach is beneficial when dealing with complicated education data with an ambiguous structure that requires interpretable insights. The study can reduce overfitting, identify more robust thematic clusters, and offer a powerful approach for unlabeled text analysis.
Keywords – Text mining, topic modeling, clustering ensembles, knowledge discovery, latent Dirichlet allocation. |
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