Design of Physics Teaching Reform Scoring System Based on K-Means Clustering Algorithm

With the deepening of the reform of physics education, how to accurately evaluate the effect of the reform has become an important subject in education research. The traditional evaluation methods of physics teaching reform often rely on subjective scores or a single index, which is a lack of comprehensiveness and systematism. Therefore, this paper proposes a grading system of physics teaching reform based on data clustering algorithms, aiming at objectively evaluating the implementation effect of teaching reform through automatic analysis of multidimensional data. The K-means clustering algorithm is employed to conduct cluster analysis on data collected from diverse sources related to teaching reform, including student achievement, teacher evaluations, classroom interactions, and more. This analysis aims to uncover the evaluation outcomes of various teaching reform models and subsequently assign scores to the teaching reform initiatives based on the clustering results. The experimental findings demonstrate that the system can effectively recognize the strengths and weaknesses of diverse physics teaching reforms. Furthermore, it offers tailored suggestions for enhancement, thereby furnishing theoretical backing and a solid data foundation for refining physics education reform.