基于关联规则的计算机等级考试成绩挖掘研究

Research on Mining Computer Rank Examination Score of College Students Based on Apriori Model

随着大数据时代的快速发展,数据挖掘辅助教育决策成为了热门的研究课题。全国计算机等级考试,积累了大量报名、学习、考试相关数据。该文基于湖南省某高校2247个学生真实的数据,采用Clementine数据挖掘工具中的Apriori模型,进行学生成绩关联规则数据挖掘;研究结果表明,学生参加考前操作考试训练、课前观看视频预习对提高计算机过级成绩至关重要。研究进一步发现学生程序学习兴趣度对过级成绩有较弱的影响。本来兴趣应该是强关联项,可是在应试教育下,目标才是学生学习的压力,动力来自压力。这为深化素质教育改革提供了参考依据。

With the rapid development of the era of big data,data mining has become a hot research topic in the decision-making of supplementary education.National computer level examination,accumulated a large number of registration,study,examination-related data.Based on the real data of 2247 students in a university in Hunan Province,this paper uses the Apriori model in the Clementine data mining tool to do data mining on the rules of student achievement association.The study further found that students'interest in program learning had a weak effect on grade achievement.Originally,interest should be a strong correlation,but in testbased education,the goal is the pressure of students to learn,motivation from pressure.This provides a reference for deepening the reform of quality education..