面向学生成绩分析的数据挖掘系统设计与实现  

Design and implementation of data mining system for student performance analysis

作　　者：曾令思 任辉[2] ZENG Ling-si;REN Hui(Elementary Education College,Capital Normal University,Beijing 100000,China;Beijing University Elmentary School,Beijing 100000,China)

机构地区：[1]首都师范大学初等教育学院,北京100000 [2]北京大学附属小学,北京100000

出　　处：《信息技术》2022年第8期120-125,共6页Information Technology

摘　　要：针对当前高校普遍使用的教学管理系统无法对学生学习成绩进行深层数据挖掘的现状,研究了一种效率更高,能够对学生成绩进行关联性分析的成绩分析系统。该系统的设计灵感来自于如何深层次挖掘与学生成绩相关的影响因子,基于关联规则对Apriori算法进行优化(主要是减少候选集的产生量,连接后生成的候选集中候选项集的个数小于原始项集的个数),构建基于关联规则的学生成绩分析系统。该系统能够在获得学生成绩的基础上对影响成绩的相关因素进行挖掘分析,实验验证了该系统在学生成绩数据挖掘上的高效性。Regarding the current situation that the teaching management system widely used in colleges and universities cannot carry out deep data mining on students’academic performance,the study explores a performance analysis system with higher efficiency,which can make correlation analysis on students’performance.The design of the system is inspired from how to deeply mine the influencing factors related to students’performance,optimize the Apriori algorithm based on association rules(it mainly reduces the number of candidate sets,and the number of candidate sets generated after connection is less than the number of original sets),and build a student performance analysis system based on association rules.The system can mine and analyze the related factors that affect the students’grades on the basis of obtaining the students’grades.The experiment verifies the efficiency of the system in data mining of students’grades.

关 键 词：数据挖掘技术 关联规则 APRIORI算法 学生成绩分析系统 影响因素 

分 类 号：TP315[自动化与计算机技术—计算机软件与理论]