Learning Path Prediction in Social Learning Network

Document Type : Original Article


Network Science and Technology Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran


Due to the growing use of Information Technology (IT) and its impact on individuals' learning styles, enhancing Social Learning Networks (SLNs) is necessary. Predicting learners' requirements is crucial in aiding the learning process and boosting performance. Hence, predicting 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 provides the subsequent learning topics based on the topics previously studied. We propose a user-based Collaboration Filtering (CF) method to perfect this approach.  To evaluate the proposed method's performance, we extracted the dataset from one of the well-known SLNs. The results indicate that individuals who follow similar learning topics in a network share the same learning needs. The method was able to predict approximately 60% of learning needs based on recall criteria.


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