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Conference Paper: Application of Naive Bayesian Classifier for Teaching Reform Courses Examination Data Analysis in China Open University System

TitleApplication of Naive Bayesian Classifier for Teaching Reform Courses Examination Data Analysis in China Open University System
Authors
KeywordsExamination data analysis
Naïve Bayesian classifier
Open education quality
Teaching reform
Issue Date2015
PublisherIEEE.
Citation
The 8th International Symposium on Computational Intelligence and Design (ISCID 2015), Hangzhou, Zhejiang, China, 12-13 December 2015. In Conference Proceedings, 2015, p. 25-29 How to Cite?
AbstractOpen education quality guarantee is a core issue in field of distance education. Data mining techniques are used to design effective teaching reform courses examination data analysis method would be a good way for checking teaching reform effects and could provide objective basis for open education quality assurance. This paper proposes a teaching reform courses examination data analysis solution based on Naive Bayesian classifier for checking the impacts of teaching reform measures act on open education quality. Naive Bayesian classifier is a famous classifying method, as a supervised learning, can extract valuable classifying rules by using data whose class label is known to train the classifier, and the trained classifier or classifying rules can be used to classify new data whose class label is unknown, and who is based on Bayes principal, has characteristics of accuracy and fast in aspect of classifying data in large scale database. Proposed solution's effectiveness is verified by processing practical teaching reform courses examination data in China Open University system. Hidden rules in teaching reform courses examination data are revealed, and also changing conditions of examination data caused by teaching reform measures are presented, which would be valuable in aspect of modifying open education quality assurance measures. © 2015 IEEE.
DescriptionSession 3
Persistent Identifierhttp://hdl.handle.net/10722/232271
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLiu, F-
dc.contributor.authorZhang, LQ-
dc.contributor.authorWang, Y-
dc.contributor.authorLu, F-
dc.contributor.authorSun, FW-
dc.contributor.authorZhang, SG-
dc.contributor.authorFok, WWT-
dc.contributor.authorTam, VWL-
dc.contributor.authorYi, J-
dc.date.accessioned2016-09-20T05:28:52Z-
dc.date.available2016-09-20T05:28:52Z-
dc.date.issued2015-
dc.identifier.citationThe 8th International Symposium on Computational Intelligence and Design (ISCID 2015), Hangzhou, Zhejiang, China, 12-13 December 2015. In Conference Proceedings, 2015, p. 25-29-
dc.identifier.isbn978-146739586-1-
dc.identifier.urihttp://hdl.handle.net/10722/232271-
dc.descriptionSession 3-
dc.description.abstractOpen education quality guarantee is a core issue in field of distance education. Data mining techniques are used to design effective teaching reform courses examination data analysis method would be a good way for checking teaching reform effects and could provide objective basis for open education quality assurance. This paper proposes a teaching reform courses examination data analysis solution based on Naive Bayesian classifier for checking the impacts of teaching reform measures act on open education quality. Naive Bayesian classifier is a famous classifying method, as a supervised learning, can extract valuable classifying rules by using data whose class label is known to train the classifier, and the trained classifier or classifying rules can be used to classify new data whose class label is unknown, and who is based on Bayes principal, has characteristics of accuracy and fast in aspect of classifying data in large scale database. Proposed solution's effectiveness is verified by processing practical teaching reform courses examination data in China Open University system. Hidden rules in teaching reform courses examination data are revealed, and also changing conditions of examination data caused by teaching reform measures are presented, which would be valuable in aspect of modifying open education quality assurance measures. © 2015 IEEE.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofInternational Symposium on Computational Intelligence and Design (ISCID) Proceedings-
dc.rightsInternational Symposium on Computational Intelligence and Design (ISCID) Proceedings. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectExamination data analysis-
dc.subjectNaïve Bayesian classifier-
dc.subjectOpen education quality-
dc.subjectTeaching reform-
dc.titleApplication of Naive Bayesian Classifier for Teaching Reform Courses Examination Data Analysis in China Open University System-
dc.typeConference_Paper-
dc.identifier.emailFok, WWT: wilton@hkucc.hku.hk-
dc.identifier.emailTam, VWL: vtam@hkucc.hku.hk-
dc.identifier.emailYi, J: alexyi@eee.hku.hk-
dc.identifier.authorityFok, WWT=rp00116-
dc.identifier.authorityTam, VWL=rp00173-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISCID.2015.81-
dc.identifier.scopuseid_2-s2.0-84978062861-
dc.identifier.hkuros263444-
dc.identifier.spage25-
dc.identifier.epage29-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161007-

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