Exploring Topics in Information Technology Open Educational Resources through the LDA Algorithm

Keywords: metadata, OER, LDA, text mining, topic modeling


This paper explores the application of machine learning and text mining techniques to discover OER issues in the context of Engineering Education. Applying the LDA (Latent Dirichlet Allocation) algorithm, themes are extracted from OER, it is possible to consider them as additional metadata. This augmentation serves to enhance the description and categorization of OER. Furthermore, this study introduces a methodology to automatically identify topics in open educational resources. In this research, a dataset of 80 OER was obtained from the Skills Commons repository. The highest coherence value achieved at 0.42, emerged when the number of topics was 9 in the LDA model. These nine topics are closely associated with Information Technology Education.



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How to Cite
V. Segarra-Faggioni, A. Romero-Pelaez, J. C. Morocho-Yunga, and R. Ludeña, “Exploring Topics in Information Technology Open Educational Resources through the LDA Algorithm”, LAJC, vol. 11, no. 1, pp. 106-115, Jan. 2024.
Research Articles for the Regular Issue