基于改进Apriori算法的学生网上学习行为关联特征挖掘模型  

An Associative Feature Mining Model of Students'Online Learning Behavior Based on Improved Apriori Algorithm

作　　者：卢鹏飞 LU Pengfei(School of Shibo art and Media Jiangsu Vocational College of Business,Nantong Jiangsu 226011)

机构地区：[1]江苏商贸职业学院世博艺术与传媒学院,江苏南通226011

出　　处：《软件》2024年第1期98-100,共3页Software

摘　　要：学生在线学习行为与成绩之间存在密切关联,为了挖掘学生学习行为特征间的隐含关系,研究采用改进的关联规则挖掘算法对其进行相关性分析。实验结果表明,研究设计的算法在精确率及召回率指标上表现较好,相比传统的关联规则挖掘算法运行速度更快,用时降低幅度达26.49%。该方法从多维度实现了学习行为的分析与关联,为教学决策和研究提供了科学的支持和指导。There is a close correlation between students'online learning behaviors and their grades,and in order to explore the implicit relationship between students'learning behaviors,the study adopts an improved association rule mining algorithm to analyze their correlation.The experimental results show that the algorithm designed in the study performs better in terms of precision and recall,and runs faster than the traditional association rule mining algorithm,with a time reduction of 26.49%.The method realizes the analysis and correlation of learning behaviors from multi-dimensions,which provides scientific support and guidance for teaching decisions and research.

关 键 词：APRIORI算法 在线学习 关联特征 数据挖掘 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]