Proposal for an Adaptive Recommender System to Support Teaching Practices
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.
Authors
- Elías Ruiz (ORCID: https://orcid.org/0000-0002-6659-3780)
- Jose Angel Gonzalez
- H. Alemán
- German Cuaya-Simbro (ORCID: https://orcid.org/0000-0001-6303-154X)
- Alexander Barinas (ORCID: https://orcid.org/0000-0002-6883-1957)
Institutions
- Universidad Autónoma del Estado de Hidalgo (MX)
- Fundación Universitaria Juan de Castellanos (CO)
- Centro Universitario del Oriente de Hidalgo (MX)
Publication Details
- Journal
- International Journal of Interactive Mobile Technologies (iJIM)
- Published
- 2025-03-13
- DOI
- https://doi.org/10.3991/ijim.v19i05.52431
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00