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dc.contributor.authorYukselturk, Erman
dc.contributor.authorOzekes, Serhat
dc.contributor.authorTurel, Yalin Kilic
dc.date.accessioned2014-09-18T13:17:57Z
dc.date.available2014-09-18T13:17:57Z
dc.date.issued2014
dc.identifier.citationYukselturk E., Ozekes S., Turel Y.K., “Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program”, European Journal of Open, Distance and e-Learning, 17, 118-133 (2014)tr_TR
dc.identifier.issn1027‐5207
dc.identifier.urihttp://earsiv.uskudar.edu.tr/xmlui/handle/123456789/209
dc.identifier.urihttp://www.eurodl.org/?p=archives&year=2014&halfyear=1&article=616
dc.description.abstractThis study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.tr_TR
dc.language.isoengtr_TR
dc.relation.ispartofseriesUluslararası Diğer;
dc.subjectEducational data mining, student dropout prediction, k-nearest neighbour, decision tree, Naive Bayes, neural networktr_TR
dc.titlePredicting Dropout Student: An Application of Data Mining Methods in an Online Education Programtr_TR
dc.typeArticletr_TR
dc.relation.journalEuropean Journal of Open, Distance and e-Learningtr_TR
dc.contributor.departmentÜsküdar Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliğitr_TR


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