K-means算法在中职学生成绩分析中的应用

Application of K-means algorithm in analysising of grades of vocational school students

针对中职计算机专业学生面临的考证挑战,通过聚类分析提供了一种新的成绩分析视角。探讨运用K-means聚类分析算法对中职计算机专业学生的考证相关课程成绩进行分析。选取《IT基础》、《信息技术》和《网络基础》3门考证核心课程的成绩作为分析对象,运用K-means算法进行聚类处理。通过算法迭代,将学生成绩划分为不同的簇群,揭示了学生学习情况的内在结构和差异性。进一步讨论了聚类结果对后续教学安排的启示,提出针对性的教学策略建议。研究结果表明,K-means聚类分析能够有效地识别学生的学习特点和问题,为教师提供科学依据,以优化教学内容和方法,从而提高教学质量。研究不仅丰富了学生成绩分析的方法论,也为中职计算机专业的教学改进提供有益的参考。

In response to the examination challenges faced by vocational computer major students,this article provides a new perspec-tive on score analysis through cluster analysis.This article explores the use of K-means clustering analysis algorithm to conduct in-depth analysis of the exam related course scores of vocational computer major students.The study selected the grades of the three core courses of"IT Fundamentals","Information Technology",and"Network Fundamentals"as the analysis objects,and used K-means algorithm for clustering processing.Through algorithm iteration,student grades were divided into different clusters,revealing the inherent structure and differences in student learning situations.This article further discusses the implications of clustering results for subsequent teaching arrangements and proposes targeted teaching strategy recommendations.The research results indicate that K-means clustering analysis can effectively identify the learning characteristics and problems of students,provide scientific basis for teachers to optimize teaching content and methods,and thus improve teaching quality.This study not only enriches the methodology of student performance analysis,but also provides useful reference for the improvement of teaching in vocational computer majors.