Intelligent clustering and analysis method of student course score data based on K-Means algorithm

Students' academic performance is not only an important indicator for evaluating their academic level, but also a key factor in understanding their learning characteristics and needs. In traditional university teaching, emphasis is placed on the continuity of course arrangements, but for completed courses, in addition to evaluating course grades, there is a lack of more relevant and instructive evaluation. This paper proposes an intelligent clustering and analysis method for student course grade data based on the K-Means algorithm. By collecting and collating students' grade data in multiple courses, we use the K-Means clustering algorithm to divide student groups into different categories. This method can reveal the patterns and characteristics of students' performance in different courses, providing valuable reference information for educators and school administrators. Based on the clustering results, we further analyzed the learning characteristics of various types of students and put forward targeted teaching suggestions. This study not only improves the accuracy and efficiency of student group classification, but also provides a scientific basis for the formulation and implementation of personalized teaching plans, which helps to improve teaching effectiveness and promote the all-round development of students.