A Knowledge-Based Teaching Resources Recommend Model for Primary and Secondary School Oriented Distance-Education Teaching Platform
In distance education systems, the ranked list of resources is very important for learners and teachers to find useful resources effectively. Apart from user’s interest, the knowledge point of subject is a crucial factor for education system, especially for primary and secondary school oriented distance education. Much of the previous work are models based on recommender systems, however, these models considered only user’s interest, ignoring the crucial impact of subject knowledge. In order to improve the performance of recommender systems, we considered both the subject knowledge and user’s interest. To get this target, Latent Knowledge Model (LKM) is adopted. LKM is a knowledge-based and teaching task-oriented model. It enables subject knowledge resources through knowledge tree extended search strategy, and gets personalized resources through user feature mining strategy. LKM is realized on real data sets which are obtained from a popular distance education teaching platform. Recall and precision rate are used to evaluate the performance of our proposed method for resources recommendation tasks. Experimental results show that the LKM captures subject knowledge and personal preferences for resources selection, which yields significant improvement in recommendation accuracy.
KeywordsDistance education Knowledge tree Extended search User feature Recommender system
We thank Dayong Wen and the subject experts for their helpful comments and suggestions about extracting subject knowledge nodes from the special subject.
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