Recommender E-Learning platform using sentiment analysis aggregation

Abstract

The ubiquity and the fast growth of online resources has made it more and more difficult to try to respect the differences between learners in terms of cognitive ability and knowledge structure. This is even clearer with recommendation algorithms that use traditional collaborative filtering as they struggle through identifying more helpful, user friendly and easy learning resources. On top of that, the incoherent recommended content and the compound and nonlinear data on online learning users cannot be effectively handled, thus making the recommendations less efficient. To increase the level of efficiency of learning resource recommendations, this paper introduces a two steps efficient resource recommendation model. this model is based on unsupervised deep learning machine to identify learning styles and users' clusters, and a sentiment analyzer bonus system, based on user experience, to improve or decrease recommender items list classification. The model integrates also teachers to incite them to enhance the quality and the success rate of appropriate selected items. The elaboration of such a model requires the use of a considerable quantity of data learners' features, course content and assessment attributes. Furthermore, this model needs to incorporate learner interactions features. These are the requirements to build Learner features vector as input for the first step and Learner-Content ratings vector to choose the more efficient learning resource to recommend.