Design and implementation of teaching analysis system based on data mining

Abstract

With the development of informatization and data collection, more and more educational process data can be obtained by education practitioners, and these massive data need to be processed in order to be used by people. Based on the classical algorithm and technical principle of data mining, this paper designs and implements a teaching analysis system based on data mining, which mainly provides the related functions of clustering analysis, regression analysis and association analysis for users. According to the K-means algorithm, the students' data are divided into several clusters in order to complete the clustering analysis of the students’ scores. FP-Growth algorithm is used to analyze the strong association rules between courses. Through Python’s drawing package, the data can be displayed clearly and intuitively, thus completing the data visualization. Finally, a user-friendly interface is built by PyQt5, and the analysis results are visualized and output.