Document Type : Original Article
K. N. Toosi University of Technology, Computer Engineering Faculty, Tehran, Iran
Due to the increasing use of Information Technology (IT) and its impact on individual learning styles, there is a need to improve Social Learning Networks (SLNs). Predicting learners' needs is crucial in supporting the learning process and improving performance. Therefore, the prediction of learning needs remains a key component in supporting learners' progress and improving their overall performance. In this paper we present an interpreter designed to predict the learning needs of SLN users. The interpreter suggests and delivers subsequent learning topics based on previously studied topics. We propose a user-based Collaboration Filtering (CF) method to perfect this approach. To evaluate the performance of the proposed method, we extracted the dataset from one of the well-known SLNs. The results show that individuals who follow similar learning topics in a network share the same learning needs. The method was able to predict about 60% of the learning needs based on recall criteria.