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Analyzing students records to identify patterns of students' performance

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dc.citedby23
dc.contributor.authorHoe A.C.K.en_US
dc.contributor.authorAhmad M.S.en_US
dc.contributor.authorHooi T.C.en_US
dc.contributor.authorShanmugam M.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorCob Z.C.en_US
dc.contributor.authorRamasamy A.en_US
dc.contributor.authorid56105282800en_US
dc.contributor.authorid56036880900en_US
dc.contributor.authorid55175180600en_US
dc.contributor.authorid36195134500en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid25824919900en_US
dc.contributor.authorid56106081100en_US
dc.date.accessioned2023-12-29T07:43:45Z
dc.date.available2023-12-29T07:43:45Z
dc.date.issued2013
dc.description.abstractAcademic failures among university students have been the subject of interest in higher education community. Students drop out due to poor academic performance as early as in the first year of their university enrolment. Many interested parties' debate and try to find reasons for this poor performance. Consequently, the ability to predict a student's performance could be useful in many ways to stakeholders of higher education institutions. This paper discusses the data mining technique used to identify the significant variables that affects and influences the performance of undergraduate students. Students' demographic and past academic performance data are then used to study the academic pattern. Early phases of the CRISP-DM methodology is also described in detail consisting business understanding, data understanding and data preparation. The data modeling and mining tool used identifies the most significant correlation of variables associated with academic success based on the past ten years of demographic and students' performance data of the College of Information Technology, Universiti Tenaga Nasional. Finally, the results from the application of the CHAID algorithm aimed at predicting students' academic success is presented. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6716767
dc.identifier.doi10.1109/ICRIIS.2013.6716767
dc.identifier.epage547
dc.identifier.scopus2-s2.0-84897831570
dc.identifier.spage544
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84897831570&doi=10.1109%2fICRIIS.2013.6716767&partnerID=40&md5=0ea43efd14d83b330d0682218847a776
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29956
dc.pagecount3
dc.sourceScopus
dc.sourcetitleInternational Conference on Research and Innovation in Information Systems, ICRIIS
dc.subjectCRISP-DM
dc.subjectdata mining
dc.subjectdata modeling clustering
dc.subjectdata preparation
dc.subjectData mining
dc.subjectInformation systems
dc.subjectPopulation statistics
dc.subjectAcademic performance
dc.subjectBusiness understanding
dc.subjectCRISP-DM
dc.subjectData preparation
dc.subjectHigher education institutions
dc.subjectSignificant variables
dc.subjectStudent's performance
dc.subjectUndergraduate students
dc.subjectStudents
dc.titleAnalyzing students records to identify patterns of students' performanceen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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