A Hybrid Recommender System based on Material Concepts with Difficulty Levels
Guibing Guo, Mojisola Erdt, Bu‐Sung Lee · International Conference on Computers in Education · 2013
Recommending learning materials for e-learning systems often encounters two issues: how to classify and organize learning materials and how to make effective recommendations. In this paper, we propose a new algorithm to handle these two problems. Specifically, we compile each learning material to concepts according to their relevance which is modeled as the length of a term-weight vector. Then recommendations are generated by taking into account the document’s similarity with some good learning material, the personalized time-aware usefulness of the learning material, the concepts of the learning material as well as their difficulty levels. Experimental results based on a small sample demonstrate the effectiveness of our method in terms of knowledge gain obtained.