Fuzzy Multi-Criteria Evaluation Model for E-Learning Course Recommendation

Currently, there is a rapid growth of the online learning market in all areas of education. Online learning provides learners with remote access not only to educational material, but also to a great amount of supporting information that accompanies the learning process, with the help of recommender systems. Recommender systems can use many different, including multi-criteria computational methods helping online users to make the best decision. The purpose of the development and implementation of recommender systems in the e-learning environment are the tasks of the best choice of learning resources from a variety of available. The quality of online courses is quite important in generating customer satisfaction. Therefore, modeling the e-learning recommendations based on the quality analysis of learning resources and user perceptions becomes especially relevant. This work aims to assess the quality characteristics of the educational online courses that determine the effective functioning of the eLearning system. The use of expert methods and fuzzy approach is proposed to range online courses using the procedure of the online course quality assessment. This approach provides a simple and convenient tool to support the process of online course selection based on the quality assessment procedure and the learners' preferences.