基于关联规则与聚类分析的课程评价技术  

Curriculum Evaluation System Based on Association Rules and Cluster Analysis

作　　者：范圣法 张先梅 虞慧群[2] FAN Shengfa;ZHANG Xianmei;YU Huiqun(Office of Academic Affairs,East China University of Science and Technology,Shanghai 200237,China;Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学教务处,上海200237 [2]华东理工大学计算机科学与工程系,上海200237

出　　处：《华东理工大学学报（自然科学版）》2022年第2期258-264,共7页Journal of East China University of Science and Technology

基　　金：国家自然科学基金(61702334,61772200);上海市自然科学基金(17ZR1406900,17ZR1429700);上海市高等教育学会规划课题(GJEL18135)。

摘　　要：高校在长期的教学活动中积累了大量的课程数据,如何利用数据资源分析课程教学状况,为提高课程教学质量提供决策支持,具有重要的研究价值。本文设计实现了一个基于关联规则与聚类分析的课程评价体系,对课程评价系统进行了功能需求分析,并对课程评价数据进行预处理。采用FP-growth算法对学生课程成绩数据进行关联规则分析,采用K-means++算法进行聚类分析,提高了课程数据分析的精度,实现了课程评价的自动化,提高了效率和评价的客观性。The quality of curriculum is the fundamental factor for the continuous improvement of the teaching quality and it is also the basis to realize the reform of higher education.In the long-term teaching activities,colleges and universities have accumulated a large number of curriculum data.How to use these resources to evaluate the teaching situation and provide decision support for improving the quality of course teaching is of great research value.This paper designs a curriculum evaluation system based on the association rules and cluster analysis,analyzes the functional requirements of the curriculum evaluation,and preprocesses the course evaluation data.The FP-growth algorithm is used to analyze the association rules of the score of student course and the K-means++algorithm is used for cluster analysis.These can effectively improve the analysis accuracy of course data,realize the automation of course evaluation,and improve the efficiency and objectivity of evaluation.

关 键 词：课程评价 数据预处理 FP-GROWTH算法 关联规则 数据聚类 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]