Learning path prediction in Social Learning Network

Because of the increasing application of Information Technology (IT) and its role in changing people’s learning styles, it is necessary to increase the performance of Social Learning Networks (SLN). Prediction of learners’ requirement is important to support the learning process and improve learner’s performance learning needs prediction is so important to support the learners’ learning process and improve their performance. In this paper, we propose an interpreter to predict the learner’s learning needs in SLN. The interpreter then guesses and offers the next learning topics in regards to the corresponding topics which were studied previously. The proposed perfection method uses a user-based Collaboration Filtering (CF) approach. The performance of the proposed method is evaluated through extracting the data-set from one of the familiar SLNs. The results shows the people who follow similar learning topics in a network, share the same learning needs. The method could predict about 60 percent of learning needs in recall criteria.