Research on the Application of K-Means Clustering Algorithm in Student Achievement

This paper uses the K-Means algorithm in data mining to perform cluster analysis based on the final grade data of students majoring in software and information services in a certain university to effectively divide the sample data set. Through the analysis of the cluster analysis results, the characteristics of the distribution of student performance in each cluster category are refined, which provides a reference for teachers in project grouping and personalized teaching in the “project-driven” mode of teaching. At the same time, according to the visual analysis of the clustering effect in different clustering categories based on the performance of a single subject, the importance of courses can be predicted. Important courses can appropriately increase teachers or class hours. This provides scientific basis for better implementation of teaching reforms and revision of talent training programs.