Work

Proposal for an Adaptive Recommender System to Support Teaching Practices

Year: 2025

Type: article

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.

Source: International Journal of Interactive Mobile Technologies (iJIM)

DOI: 10.3991/ijim.v19i05.52431

Authors: German Cuaya-Simbro, Jose A. Gonzalez, Elias Ruiz, Helena CI Alemán, Alexander Barinas

Institutions: Tecnológico Nacional de México, Fundación Universitaria Juan de Castellanos

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $280