Proposal for an Adaptive Recommender System to Support Teaching Practices

German Cuaya-Simbro; Jose A. Gonzalez; Elias Ruiz; Helena CI Alemán; Alexander Barinas

doi:10.3991/ijim.v19i05.52431

Proposal for an Adaptive Recommender System to Support Teaching Practices

Authors

German Cuaya-Simbro Tecnológico Nacional de México/ITS del Oriente del Estado de Hidalgo, Hidalgo, México https://orcid.org/0000-0001-6303-154X
Jose A. Gonzalez Tecnológico Nacional de México/ITS del Oriente del Estado de Hidalgo, Hidalgo, México https://orcid.org/0009-0007-5246-034X
Elias Ruiz Tecnológico Nacional de México/ITS del Oriente del Estado de Hidalgo, Hidalgo, México https://orcid.org/0000-0002-6659-3780
Helena CI Alemán Fundación Universitaria Juan de Castellanos Colombia, Tunja, Colombia
Alexander Barinas Fundación Universitaria Juan de Castellanos Colombia, Tunja, Colombia https://orcid.org/0000-0002-6883-1957

DOI:

https://doi.org/10.3991/ijim.v19i05.52431

Keywords:

adaptive artificial intelligence, data analysis, machine learning, educational technology tools, teaching assistant

Abstract

The shift to online learning, which has grown by 70% in recent years, has brought unprecedented challenges to the educational landscape. This emphasizes the urgent need for innovative solutions that address the evolving needs of both educators and students. This study introduces a novel adaptive recommender system for educators, which has demonstrated significant improvements in teaching effectiveness through personalized strategy recommendations. Preliminary findings reveal a notable increase in student engagement and comprehension, underscoring the system’s potential to revolutionize educational practices.

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Published

2025-03-13

How to Cite

Cuaya-Simbro, G., Gonzalez, J. A., Ruiz, E., Alemán, H. C., & Barinas, A. (2025). Proposal for an Adaptive Recommender System to Support Teaching Practices. International Journal of Interactive Mobile Technologies (iJIM), 19(05), pp. 4–21. https://doi.org/10.3991/ijim.v19i05.52431