中南民族大学学报（自然科学版）2025，Vol.44Issue(3)：373-383,11.DOI:10.20056/j.cnki.ZNMDZK.20250311
在MOOCCube中发现具有效率约束的序列模式
Discovering sequential patterns with efficiency constraints in MOOCCube
摘要
Abstract
Massive Open Online Course(MOOC)platforms have emerged as crucial tools in online learning,capturing vast learning behavior data.MOOCube,a significant data repository,encompasses 48640 learning behavior sequences extracted from XuetangX.However,traditional Sequential Pattern Mining(SPM)algorithms may struggle to handle the complexity of MOOC data,leading to irrelevant patterns.This paper proposes Efficiency-Constrained Sequential Pattern Mining(ECSPM)to facilitate the discovery of useful patterns.Three constraints are introduced:attendance,discreteness,and dropout.These constraints capture the influence of different features of learning behavior on sequential patternsmining.Importantly,these constraints are proven to satisfy the downward closure property,ensuring their effectiveness in shaping the mining process.To discover Sequential Patterns,three algorithms were proposed.These algorithms employ level-by-level search space traversal or recursive projection techniques while integrating the concept of cost into pattern mining.Experimental evaluation confirms the effectiveness of the proposed algorithm.ECSPM has successfully reduced the number of discovered patterns while maintaining comparable performance to the classical SPM algorithm.关键词
学习行为/序列模式挖掘/效率约束序列模式挖掘/成本Key words
learning behavior/sequential pattern mining/efficiency-constrained sequential pattern mining/cost分类
信息技术与安全科学引用本文复制引用
彭亚威,李艳红,杨洋,张法..在MOOCCube中发现具有效率约束的序列模式[J].中南民族大学学报（自然科学版）,2025,44(3):373-383,11.基金项目
湖北省自然科学基金资助项目(2017CFB135) （2017CFB135）
中央高校基本科研业务费专项资金资助项目(CZY23019) （CZY23019）
网络创新及应用型人才课程实践教学研究项目(2019年第一批) （2019年第一批）
