Analysis of Higher Education Teaching Data Based on Data Mining Technology

Abstract

In order to effectively mine and utilize the large amount of valuable data stored in the academic management system of universities, a data mining technology based method for analyzing higher education teaching data has been proposed. This article uses data mining technology to deeply mine and analyze the grade data in the school's academic affairs system. Firstly, the data in the academic affairs system is collected and preprocessed, and then factor analysis is used to comprehensively evaluate student grades. Then, the decision tree improvement method of K-means clustering algorithm and C5.0 algorithm is used to predict the target grades. Finally, the above method is compared and analyzed with other methods. The results show that the estimation accuracy of the improved decision tree method is the highest 64.8%, and the generated decision tree has the smallest depth and the smallest number of leaf nodes. This indicates that the decision Tree model generated by it is more accurate and robust than the other two methods, and can try to avoid over fitting.