Personalized Desire2Learn Recommender System based on Collaborative Filtering and Ontology

Abstract

In this century, attention has grown to recommendation systems (RS), especially in e-learning, to solve the problem of overloading information in e-learning systems. E-learning providers also play a major role in helping learners to find appropriate courses that fit their learning plan using Desire2Learn at Majmaah University. Although recommendation systems generally have a clear advantage in solving problems related to overloading information in various areas of e-business and making accurate recommendations, e-learning recommendation systems still have problems with overloading information about the characteristics of the learning recipient Such as the appropriate education style, the level of skills provided and the student's level of education. In this paper, we suggest that a recommendation technique combining collaborative filtering and ontology be introduced to recommend courses for learning recipients through Desire2Learn. Ontology involves the integration of the characteristics of the learning recipient into the recommendation process as well as the classifications, while the liquidation process cooperates in the predictions and generates recommendations for e-learning. In addition, ontological knowledge is employed by the educational RS in the early stages if no assessments can be made to mitigate the cold start problem. The results of this study show that the proposed recommendation technique is distinguished and superior to the cooperative liquidation in terms of specialization and accuracy of the recommendation.