A Hybrid Recommender System based on Material Concepts with Difficulty Levels
Key: GH13-1
Author: Guibing Guo, Mojisola Helen Erdt, Bu Sung Lee
Date: November 2013
Kind: In proceedings
Book title: Proceedings of the 21st International Conference on Computers in Education
Keywords: e-learning systems, learning materials, material concepts, knowledge gain
Abstract: Recommending learning materials for e-learning systems often encounters two issues: how to classify and organise 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.
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