E-Learning Resource Recommendation Based on Fuzzy Sets
Lian Hong Ding · Applied Mechanics and Materials · 2014
A challenging topic for e-learning system is to find appropriate learning assets for users when e-learning takes place in an open and dynamic environment. A good personalized e-learning environment should recommend right learning content for learners. In order to find appropriate learning materials for learners with different preferences, this paper presents a fuzzy set theoretic method for e-learning system. Firstly, e-learning resource and user interest are represented with fuzzy value. Secondly, an algorithm based on various fuzzy set theoretic similarity measures is introduced to find the e-learning contents matching learners request. Lastly, the approach to introduce learning materials for learners, based on the similarity computing, is given. Compared to the baseline crisp set based method presented, our method shows an improvement in precision without loss of recall.