{"title":"智能辅导系统中基于学习行为数据的学生聚类","authors":"Ines Šarić-Grgić, Ani Grubišić, Ljiljana Šerić, T. Robinson","doi":"10.4018/ijdet.2020040105","DOIUrl":null,"url":null,"abstract":"The idea of clustering students according to their online learning behavior has the potential of providingmoreadaptivescaffoldingbytheintelligenttutoringsystemitselforbyahumanteacher. WiththeaimofidentifyingstudentgroupswhowouldbenefitfromthesameinterventioninACwareTutor, this researchexaminedonline learningbehaviorusing8 trackingvariables: the total numberofcontentpagesseeninthelearningprocess;thetotalnumberofconcepts;thetotalonline score;thetotaltimespentonline;thetotalnumberoflogins;thestereotypeaftertheinitialtest,the finalstereotype,andthemeanstereotypevariability.Thepreviousmeasureswereusedinafour-step analysisthatconsistedofdatapreprocessing,dimensionalityreduction,theclustering,andtheanalysis ofaposttestperformanceonacontentproficiencyexam.Theresultswerealsousedtoconstructthe decisiontreeinordertogetahuman-readabledescriptionofstudentclusters. KEywoRDS Blended Learning, Clustering, Decision Tree, Educational Data Mining, Flipped Classroom, Intelligent Tutoring System, Online Learning Behavior, Principal Component Analysis","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System\",\"authors\":\"Ines Šarić-Grgić, Ani Grubišić, Ljiljana Šerić, T. Robinson\",\"doi\":\"10.4018/ijdet.2020040105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The idea of clustering students according to their online learning behavior has the potential of providingmoreadaptivescaffoldingbytheintelligenttutoringsystemitselforbyahumanteacher. WiththeaimofidentifyingstudentgroupswhowouldbenefitfromthesameinterventioninACwareTutor, this researchexaminedonline learningbehaviorusing8 trackingvariables: the total numberofcontentpagesseeninthelearningprocess;thetotalnumberofconcepts;thetotalonline score;thetotaltimespentonline;thetotalnumberoflogins;thestereotypeaftertheinitialtest,the finalstereotype,andthemeanstereotypevariability.Thepreviousmeasureswereusedinafour-step analysisthatconsistedofdatapreprocessing,dimensionalityreduction,theclustering,andtheanalysis ofaposttestperformanceonacontentproficiencyexam.Theresultswerealsousedtoconstructthe decisiontreeinordertogetahuman-readabledescriptionofstudentclusters. KEywoRDS Blended Learning, Clustering, Decision Tree, Educational Data Mining, Flipped Classroom, Intelligent Tutoring System, Online Learning Behavior, Principal Component Analysis\",\"PeriodicalId\":298910,\"journal\":{\"name\":\"Int. J. Distance Educ. Technol.\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Distance Educ. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdet.2020040105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Distance Educ. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijdet.2020040105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System
The idea of clustering students according to their online learning behavior has the potential of providingmoreadaptivescaffoldingbytheintelligenttutoringsystemitselforbyahumanteacher. WiththeaimofidentifyingstudentgroupswhowouldbenefitfromthesameinterventioninACwareTutor, this researchexaminedonline learningbehaviorusing8 trackingvariables: the total numberofcontentpagesseeninthelearningprocess;thetotalnumberofconcepts;thetotalonline score;thetotaltimespentonline;thetotalnumberoflogins;thestereotypeaftertheinitialtest,the finalstereotype,andthemeanstereotypevariability.Thepreviousmeasureswereusedinafour-step analysisthatconsistedofdatapreprocessing,dimensionalityreduction,theclustering,andtheanalysis ofaposttestperformanceonacontentproficiencyexam.Theresultswerealsousedtoconstructthe decisiontreeinordertogetahuman-readabledescriptionofstudentclusters. KEywoRDS Blended Learning, Clustering, Decision Tree, Educational Data Mining, Flipped Classroom, Intelligent Tutoring System, Online Learning Behavior, Principal Component Analysis
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