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.
learning path Bayes formula user profile social tags
Dihua Xu Zhijian Wang Kejia Chen Weidong Huang
College of Computer and Information, Hohai University, China College of Computer, Nanjing University College of Computer and Information, Hohai University, China College of Computer, Nanjing University of Posts and Telecommunications, China College of Economics and Management, Nanjing University of Posts Telecommunications Nanjing, China
国际会议
杭州
英文
511-514
2012-10-28（万方平台首次上网日期，不代表论文的发表时间）