工业技术与职业教育

现有的学生成绩分析方法多依赖于单一维度的成绩数据，难以反映学生多元学习表现。从课程类型的宏观视角出发，运用K-means算法对学生的三类课程平均分进行聚类分析，聚类结果表明，学生在三类课程中的表现存在显著差异。针对聚类结果各个群体的特点，提出了有针对性的教学改进建议。同时，从学生、教师与教学管理者角度提出具体建议：学生应主动参与实践与操作任务，提升综合能力；教师应以学习者为中心，设计差异化教学任务；教学管理者需依据学生群体的学习特点，优化课程设置并提供精准资源支持，修订人才培养方案，确保课程设置与学生的学习需求相匹配。

Existing methods for analyzing student performance often rely on single-dimensional grade data,making it difficult to reflect students，diverse learning outcomes.This study,from a macro perspective of course types,applies the K-means algorithm to conduct a clustering analysis of students，average scores across three types of courses.The clustering results reveal significant differences in students，performance across these three course categories.Based on the characteristics of each cluster,targeted suggestions for teaching improvement are proposed.Additionally,specific recommendations are provided from the perspectives of students,teachers and educational administrators:students should actively engage in practical and operational tasks to enhance their comprehensive abilities;teachers should adopt a learner-centered approach and design differentiated teaching tasks;educational administrators need to optimize course offerings and provide precise resource support based on the learning characteristics of student groups,as well as revise talent development plans to ensure that course designs align with students，learning needs.