The System of Personalized Learning Resource Recommendation and Experimental Teaching Based on Collaborative Filtering

Abstract

“Electronic system design” is an important course closely related to electronic design competition. A wide range of knowledge modules are involved in the class, but some modules are not studied by students. Due to the constraints of classroom time, they need to preview independently before class under the guidance of teachers. We design a recommendation system based on improved collaborative filtering algorithm, so that students can learn by themselves according to the learning resources pushed by teachers. To increase the accuracy of collaborative filtering algorithm, the user's attribute similarity is combined with the traditional collaborative filtering recommendation algorithm to improve the cold start problem and data sparsity of the algorithm. Then we build an experimental teaching platform of "Electronic System Design" curriculum to achieve automatic recommendation of experimental learning resources under teachers’ guidance.