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基于数据挖掘技术的高校学生成绩管理研究.docx

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基于数据挖掘技术的高校学生成绩管理研究 Title:AStudyontheApplicationofDataMiningTechniquesinCollegeStudentPerformanceManagement Abstract: Inrecentyears,educationalinstitutionshavebeenincreasinglyutilizingdataminingtechniquestoimprovetheirmanagementanddecision-makingprocesses.Thispaperfocusesontheapplicationofdataminingtechniquesincollegestudentperformancemanagement.Byanalyzingandmodelingthevastamountofstudentperformancedata,educationalinstitutionscangainvaluableinsightsintofactorsaffectingstudentsuccessandimplementtargetedinterventionsforimprovingstudentoutcomes.Thisstudyexploresvariousdataminingtechniquesandtheirapplicationsinoptimizingstudentperformancemanagement,suchaspredictingstudentgrades,identifyingat-riskstudents,andunderstandingthefactorsinfluencingacademicachievements.Additionally,thepaperdiscussesthechallengesandfuturedirectionsinusingdataminingtechniquesforstudentperformancemanagementinhighereducation. Introduction: Withtheadvancementofinformationtechnology,educationalinstitutionshaveaccesstovastamountsofdatarelatedtostudentperformance.Thisdataincludesstudentdemographics,academicrecords,courseregistrations,examresults,andvariousotherperformanceindicators.However,merelycollectingthisdataisnotsufficient;educationalinstitutionsneedtoextractmeaningfulinsightsfromittosupportdecision-makingprocessesandimprovestudentperformance.Thisiswheredataminingtechniquescomeintoplay.Dataminingreferstotheprocessofanalyzinglargedatasetstodiscoverpatterns,relationships,andhiddenknowledge.Byapplyingdataminingtechniques,educationalinstitutionscanuncovervaluableinformationfromtheirstudentperformancedata,ultimatelyimprovingtheeffectivenessoftheirprogramsandinterventions. Methods: Thisstudyemploysvariousdataminingtechniquesforcollegestudentperformancemanagement.Thetechniquesusedincludeclassification,regression,clustering,andassociationrulemining.Firstly,classificationtechniquesareappliedtopredictstudentgradesbasedonhistoricaldata.Thisenableseducationalinstitutionstoidentifystudentswhomayrequireadditi