基于Apriori算法的学生成绩分析及可视化展示  

Student Achievement Analysis and Visualization Based on Apriori algorithm

作　　者：许青 李洁 王英明 张露露 XU Qing;LI Jie;WANG Ying-ming;ZHANG Lu-lu(Ma'anshan University,Ma'anshan 243002,China)

机构地区：[1]马鞍山学院,安徽当涂243100

出　　处：《通化师范学院学报》2023年第4期81-87,共7页Journal of Tonghua Normal University

基　　金：2020年度马鞍山学院校级科研基金重点项目(QS20200015);马鞍山学院2022年度校级科研基金重点项目(QS2022008)。

摘　　要：针对高校中存在着大量学生数据得不到有效利用的现状,提出用关联规则中的Apriori算法对教学管理系统中学生各科成绩进行深入挖掘,从中挖掘出课程之间的关联性.首先利用Python中的Pandas库对学生成绩进行数据处理;然后使用mlxtend库求出学生成绩的频繁项集和关联规则,并将关联规则以网络图形式进行了可视化展示;最后对挖掘出的结果进行相关分析.最终结果表明:挖掘出来的关联规则可以为教学管理者制定教学计划提供相应的参考,同时也能帮助教师了解学生学习情况,从而有效地提高教学质量.In view of the fact that a large number of students data cannot be effectively used in colleges and universities,this paper proposes to use the Apriori algorithm in association rules to dig deeply the scores of students in each subject in the teaching management system,and find out the correlation be⁃tween courses.Firstly,the Pandas library in Python is used for data processing of students'achievements.Then,the frequent itemsets and association rules of students'scores are obtained by using mlxtend li⁃brary,and the association rules are visualized in the form of network graph.Finally,the correlation analy⁃sis of the excavation results is carried out.The final results show that the excavated association rules can provide reference for teaching managers to make teaching plans,and also help teacher to understand stu⁃dents'learning conditions,so as to effectively improve the teaching quality.

关 键 词：关联规则 APRIORI PYTHON 频繁项集 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]