img
img
Educational Data Mining: Predicting Candidates’ Placement Status in Physical Education and Sports Education Program   
Yazarlar (4)
Prof. Dr. Mustafa YAĞCI Prof. Dr. Mustafa YAĞCI
Kırşehir Ahi Evran Üniversitesi, Türkiye
Doç. Dr. Yusuf Ziya OLPAK Doç. Dr. Yusuf Ziya OLPAK
Türkiye
Kağan Gül
Türkiye
Sıdıka Seda Olpak
Devamını Göster
Özet
Educational data mining’s primary purpose being to extract useful information from educational data in order to support decision-making on educational issues. One of the most preferred methods in educational data mining is prediction. The primary purpose of the current study is to predict whether or not candidates will be admitted into the PESE program according to different algorithms. Within the scope of this research, data was obtained from 1,671 candidates who applied to join the PESE program of a state university in Turkey between 2016 and 2020 were studied. The Random Forest, kNN, SVM, Logistic Regression, and Naïve Bayes algorithms were each used to predict whether or not a candidate could admit to the PESE program. According to the findings, the algorithms’ classification accuracy from highest to lowest is Random Forest (.985), SVM (.845), kNN (.818), Naïve Bayes (.815), and Logistic Regression (.701), respectively. In other words, the Random Forest algorithm is shown to have correctly classified the instances almost exactly. Other findings from the study are discussed in detail, and suggestions put forth for future research.
Anahtar Kelimeler
Makale Türü Özgün Makale
Makale Alt Türü Diğer hakemli uluslarası dergilerde yayınlanan tam makale
Dergi Adı Bilgi ve İletişim Teknolojileri Dergisi
Dergi ISSN 2687-492X
Dergi Tarandığı Indeksler DRJI, ASOS, ISI, ESJI, ARI, DRJI, TEI
Makale Dili Türkçe
Basım Tarihi 06-2022
Cilt No 4
Sayı 4
Sayfalar 110 / 127
Doi Numarası 10.53694/bited.1118025
Makale Linki https://dergipark.org.tr/tr/pub/bited/issue/70763/1118025