Student Performance Mining Based on Kernel Density Estimation Interval and Association Rules

摘要

Because the student’s course performance reflects the student’s understanding of the course, in order to find the correlation between the course and the course, this paper proposes a method of analyzing student course performance based on the kernel density estimation interval clustering algorithm and the interest-based APRIORI algorithm. Use the kernel density estimation interval clustering algorithm to discretize the student’s course performance, and then use the interest-based APRIORI algorithm to mine the relationship between the course and the course. The result can provide scientific advice for the school to modify the course schedule and help improve students Learning efficiency.