Curriculum Evaluation System Based on Association Rules and Cluster Analysis

The quality of curriculum is the fundamental factor for the continuous improvement of the teaching quality and it is also the basis to realize the reform of higher education. In the long-term teaching activities, colleges and universities have accumulated a large number of curriculum data. How to use these resources to evaluate the teaching situation and provide decision support for improving the quality of course teaching is of great research value. This paper designs a curriculum evaluation system based on the association rules and cluster analysis, analyzes the functional requirements of the curriculum evaluation, and preprocesses the course evaluation data. The FP-growth algorithm is used to analyze the association rules of the score of student course and the K-means++ algorithm is used for cluster analysis. These can effectively improve the analysis accuracy of course data, realize the automation of course evaluation, and improve the efficiency and objectivity of evaluation.