基于改进Apriori算法的高校课程预警规则库构建  

Early Warning Rule Repository of University Student Achievement Based on Improved Apriori Algorithm

作　　者：任鸽[1] 吴猛 汗古丽·力提甫 杨勇[1] REN Ge;WU Meng;HANGVL Litip;YANG Yong(School of Computer Science and Technology,Xinjiang Normal University,Urumqi 830054,China)

机构地区：[1]新疆师范大学计算机科学技术学院,乌鲁木齐830054

出　　处：《计算机系统应用》2021年第7期290-295,共6页Computer Systems & Applications

基　　金：新疆维吾尔自治区教学改革与研究项目(ZJG2019-11);新疆维吾尔自治区高等学校科研计划(XJEDU2016S066);新疆师范大学博士科研启动基金(XJNUBS1609)。

摘　　要：在如何构建完善的课程预警规则库是高校成绩预警研究中的一个重点问题,本文对高校学生成绩进行清洗、离散化后,利用Apriori算法挖掘不及格课程之间的相关关联,构建基础预警规则库,在此基础上进一步挖掘“及格”,“良好”等级课程对其他课程的影响,从而进一步扩充预警规则库.针对大量冗余规则的情况,在传统的支持度-置信度框架下利用提升度、兴趣度等方法筛选出强关联规则,提高规则库的准确度,并对挖掘出的规则进行了针对性的分析,研究方法和结论可为教学管理提供决策支持.How to build a complete early warning rule repository is a key issue in the research on the early warning of university student achievements.In this study,after cleaning and discretizing the data on university student achievements,we use the Apriori algorithm to mine the correlation between failed courses and construct the basic early warning rule repository.On this basis,the influence of courses with“pass”and“good”grades are explored to further expand the early warning rule repository.In the case of copious redundant rules,strong association rules are filtered out by lift and interest in the traditional support-confidence framework to improve the accuracy of the repository and specifically analyze the mined rules.Our methods and conclusions can support the decision-making of teaching management.

关 键 词：数据挖掘 成绩预警 关联规则 APRIORI算法 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] G647.3[自动化与计算机技术—计算机科学与技术]