Educational Data Mining: Predicting Candidates’ Placement Status in Physical Education and Sports Education Program

Abstract

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.

Keywords

• educational data mining
• big data
• special talent exam

Eğitsel Veri Madenciliği: Adayların Beden Eğitimi ve Spor Eğitimi Programına Yerleşme Durumlarının Tahmini

Abstract

Eğitsel veri madenciliğinin temel amacı, eğitimle ilgili konularda karar vermeyi desteklemek için eğitim verilerinden faydalı bilgiler çıkarmaktır. Eğitsel veri madenciliğinde en çok tercih edilen yöntemlerden biri de tahmindir. Mevcut çalışmanın birincil amacı, adayların Beden Eğitimi ve Spor Eğitimi programına kabul edilip edilmeyeceklerini farklı algoritmalar kullanarak tahmin etmektir. Bu araştırma kapsamında 2016-2020 yılları arasında Türkiye'de bir devlet üniversitesinin Beden Eğitimi ve Spor Eğitimi programına katılmak için başvuran 1.671 adaydan elde edilen verilerle çalışılmıştır. Random Forest, kNN, SVM, Logistic Regression ve Naïve Bayes algoritmalarının her biri, bir adayın ilgili programına kabul edilip edilmeyeceğini tahmin etmek için kullanılmıştır. Elde edilen bulgulara göre algoritmaların sınıflandırma doğruluğu en yüksekten en düşüğe doğru sırasıyla; Random Forest (.985), SVM (.845), kNN (.818), Naïve Bayes (.815) ve Logistic Regression (.701) şeklindedir. Başka bir deyişle, Random Forest algoritmasının, örnekleri neredeyse tam olarak doğru bir şekilde sınıflandırdığı bulunmuştur. Bu bağlamda, çalışmadan elde edilen diğer bulgular ayrıntılı olarak tartışılmış ve gelecek araştırmalar için önerilerde bulunulmuştur.

Keywords

• eğitsel veri madenciliği
• büyük veri
• özel yetenek sınavı

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