Research on Precision Teaching Model of Ideology Course Based on Collaborative Filtering Algorithm

Abstract

The ideological and political course should not only keep the academic rationality and political nature of the course itself, but also take into account the characteristics of colleges and universities and students’ growth and development needs. At present, there are some problems in the curriculum of ideology, such as mechanical rigidity, weak pertinence, lack of synergy, and inability to form a personalized collaborative and precise education mechanism. Aiming at related problems, this article constructs an accurate teaching model of ideological and political course based on collaborative filtering algorithm. First, the public test set of recommended fields is used to test and verify the effectiveness and practicability of the algorithm. For the data sparseness and cold start of collaborative filtering algorithm, the course feature attributes and attribute value preference matrix are used to solve the problem, and the similarity is calculated offline, so as to realize the real-time recommendation and accurate teaching of the course. In order to verify the effectiveness of this method for precise teaching, we conducted a test. The test results show that the precise teaching model has a positive effect on the improvement in students’ academic performance. The method proposed in this article realizes the identity transformation of students from passive acceptance to active construction, and the teaching effectiveness of ideological and political course is effectively improved.