Computerized Adaptive Testing for Cognitive Diagnosis in Classroom: A Nonparametric Approach

dc.contributor蔡蓉青zh_TW
dc.contributorTsai, Rung-Chingen_US
dc.contributor.author張沅培zh_TW
dc.contributor.authorChang, Yuan-Peien_US
dc.date.accessioned2019-09-05T01:06:45Z
dc.date.available不公開
dc.date.available2019-09-05T01:06:45Z
dc.date.issued2017
dc.description.abstractThe Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) has been suggested by researchers as a diagnostic tool for assessment and evaluation. While model-based CD-CAT is relatively well-researched in the context of large-scale assessments, this type of system has not received the same degree of development in small-scale settings, where it would be most useful. The main challenge is that the statistical estimation techniques successfully applied to the parametric CD-CAT require large samples to guarantee the reliable calibration of item parameters and accurate assignments of examinees. In response to the challenge, a nonparametric approach that does not require any parameter calibration, and thus can be used in small educational programs, is proposed. Unlike other CD-CAT algorithms, the proposed nonparametric CD-CAT uses the nonparametric classification (NPC) method to assess and update the student's ability state while the test proceeds. Based on a student's responses, possible proficiency classes are identified, and items which can discriminate them are chosen next. The simulation results show that the proposed nonparametric item selection (NPS) method outperformed the compared parametric CD-CAT algorithms and the differences were more significant when the item parameter calibration was not optimal.zh_TW
dc.description.abstractThe Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) has been suggested by researchers as a diagnostic tool for assessment and evaluation. While model-based CD-CAT is relatively well-researched in the context of large-scale assessments, this type of system has not received the same degree of development in small-scale settings, where it would be most useful. The main challenge is that the statistical estimation techniques successfully applied to the parametric CD-CAT require large samples to guarantee the reliable calibration of item parameters and accurate assignments of examinees. In response to the challenge, a nonparametric approach that does not require any parameter calibration, and thus can be used in small educational programs, is proposed. Unlike other CD-CAT algorithms, the proposed nonparametric CD-CAT uses the nonparametric classification (NPC) method to assess and update the student's ability state while the test proceeds. Based on a student's responses, possible proficiency classes are identified, and items which can discriminate them are chosen next. The simulation results show that the proposed nonparametric item selection (NPS) method outperformed the compared parametric CD-CAT algorithms and the differences were more significant when the item parameter calibration was not optimal.en_US
dc.description.sponsorship數學系zh_TW
dc.identifierG060540023S
dc.identifier.urihttp://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G060540023S%22.&%22.id.&
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/101584
dc.language英文
dc.subjectcognitive diagnosiszh_TW
dc.subjectnonparametric classificationzh_TW
dc.subjectcomputerized adaptive testingzh_TW
dc.subjectnonparametric item selectionzh_TW
dc.subjectin classroomzh_TW
dc.subjectcognitive diagnosisen_US
dc.subjectnonparametric classificationen_US
dc.subjectcomputerized adaptive testingen_US
dc.subjectnonparametric item selectionen_US
dc.subjectin classroomen_US
dc.titleComputerized Adaptive Testing for Cognitive Diagnosis in Classroom: A Nonparametric Approachzh_TW
dc.titleComputerized Adaptive Testing for Cognitive Diagnosis in Classroom: A Nonparametric Approachen_US

Files

Collections