A Hybrid Model for E-Learning Resources Recommendations in the Developing Countries

Abstract

E-learning has changed the education style in the developed countries. However, in the developing nations such as the East African (EA) countries, the students are still challenged by the accessibility of online learning materials. In this paper, we sought to alleviate this issue by proposing a recommendation method that helps the students from the developing countries in selecting more appropriate e-learning resources. To achieve this goal, an e-learning dataset composes of 1237 students from three different universities in East Africa is used and the learners' information including contextual, demographic, and ratings predictions are hybridized by applying a developed knowledge-based computational model to generate the recommendations in a unified manner. Results from experimental evaluations are presented and discussed to demonstrate the benefits of the proposed system.