Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

Abstract

In recent years, the amount of educational data in colleges and universities has increased rapidly. Each university has set up multiple courses to recruit talents. Students cannot choose courses. The emergence of data mining technology and its application in college teaching and curriculum has been preferred particularly to streamline these activities. When analyzing the correlation of courses based on data mining technology, we usually use the correlation between the scores of various subjects to analyze the correlation between courses. Correlation among various courses that are offered at colleges or universities is reflected through many different aspects such as factors or metrics, which are affecting course interest, course content, course arrangement, etc. In this article, we have thoroughly analyzed various factors that are affecting students’ interest in literature courses with the help of association rules of data mining technology. Through the collected original data, this article uses Apriori algorithm to screen the association rules affecting students’ interest in literature courses and combines them with the current teaching situation to complete the rule analysis. The results of rule analysis show that the most relevant factors affecting students’ interest in literature curriculum mainly include the space-time dimension of textbook selection and compilation, the processing method of selected reading, and the evaluation method. The reading content is effectively processed by using the counterpoint reading method, and the literature curriculum textbooks are compiled from the perspective of cross-cultural communication, to enhance students’ interest in the literature curriculum.