Personalized Learning Path Recommender Based on User Profile Using Social Tags

Nowadays, many researchers focus on developing learning systems with personalized learning mechanisms to adaptively provide learning paths in order to promote the learning performance of individual learner. Meanwhile, finding a suitable learning path has become a crucial issue for learners who want to learn new things quickly and effectively. We propose a personalized learning path recommender in this paper, which can recommend learning materials of every step in the learning process of a learner. as we all known, the performance of a recommender system depends on the accuracy of the user profiles used to represent the characteristics of the users. We firstly make advantage of social tags to construct user profiles. We consider that the knowledge units in the learning path have precedence relationship. then we make use of Bayes formula to predict the probability of the next learning materials within mostly similar learners. the Experiments show that our method is practical and effective.