IMPLEMENTATION AND EVALUATION OF A RESOURCE-BASED LEARNING RECOMMENDER BASED ON LEARNING STYLE AND WEB PAGE FEATURES

It is generally believed that recommender systems are a suitable key to overcome the information overload problem. In recent years, a special research area in this domain has emerged that concerns recommender systems for Technology Enhanced Learning, in particular, self-regulated learning with resources on the web, known as Resource-Based Learning. Grey-sheep users are a major challenge in RecsysTEL. This group of users have completely different opinions from other users. They do not profit from collaborative algorithms, so they must be supported in discovering learning resources relevant to their characteristics and needs. The main contribution of this work is to develop a feature-based educational recommender system which interacts with the user based on his or her learning style. The learning style dimensions would be determined based on Felder-Silverman theory. In addition, the system crawls and extracts the necessary meta-data of sample OCW's web pages. Based on the proposed web page ranking formula, the user's learning style dimension and web page feature's vector would be accommodated to generate learning object suggestions. The general satisfaction, perception and motivation towards the proposed method measured among 77 science and engineering students by a questionnaire. Moreover, the system has been evaluated to provide feedbacks on its suitability. The research findings imply that the proposed method outperforms the general search algorithm. This system can be used as a template in formal and informal learning and educational environments as a RecsysTEL.