Revisión de estudios sobre análisis de los algoritmos de recomendación para plataformas educativas orientados a la recomendación de recursos didácticos

Melanie Baque Varela; Tatiana Zambrano Solórzano; Christian Torres Morán

doi:10.56124/encriptar.v9i17.006

Authors

Melanie Baque Varela Universidad Técnica de Manabí
Tatiana Zambrano Solórzano Universidad Técnica de Manabí
Christian Torres Morán Universidad Técnica de Manabí

DOI:

https://doi.org/10.56124/encriptar.v9i17.006

Keywords:

Educational Platforms, Recommendation Algorithms, Artificial Intelligence in Education

Abstract

Educational platforms have become dynamic and valuable digital tools within academic institutions. This study aims to critically analyze the recommendation algorithms employed in educational platforms. A total of 317 studies were reviewed, of which 73 were selected through a systematic methodology comprising three phases: (a) planning, where research questions regarding the implementation of recommendation algorithms were defined; (b) execution, involving a systematic search of articles across various databases; and (c) analysis, evaluating the selected publications. The findings indicate that recommendation algorithms are essential for the personalization of educational content. These algorithms are categorized into collaborative filtering, content-based filtering, hybrid systems, demographic filtering, and knowledge-based systems. Platforms that integrate these algorithms enhance learning effectiveness by helping users discover relevant and tailored content. In this study, artificial intelligence is considered an enabling technology that facilitates personalized learning, rather than a research variable. Accordingly, the importance of addressing learner diversity and data privacy in the implementation of these systems is emphasized. Ultimately, the integration of recommendation algorithms and educational platforms holds significant potential to transform education, if personalization ensures users retain control over their data. Among the selected articles, 15 studies report concrete algorithmic implementations: 40% involve deep learning approaches, 20% collaborative filtering, 20% hybrid systems, 13% knowledge-based systems, and 7% demographic filtering—highlighting the limited adoption of the latter categories.

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