Bir Öğrenciyi Üstün Zekâlı ve Yetenekli Olarak Aday Göstermek İçin Doğru Soruları Sormak: Bir Makine Öğrenmesi Yaklaşımı

Year 2020, Volume 13, Issue 4, 385 - 400, 30.10.2020

Elif KARTAL Melodi ÖZYAPRAK Zeki ÖZEN İrfan ŞİMŞEK Sezer KÖSE BİBER Mahir BİBER Tuncer CAN

https://doi.org/10.17671/gazibtd.591158

Cited By: 1

Abstract

Bu çalışmada, bir öğrencinin üstün zekâlı ve yetenekli olarak aday gösterilmesi için geliştirilen 69 soruluk ölçekten öğretmenin kararında en etkili soruların seçilerek ölçekteki soru sayısının azaltılması amaçlanmıştır. Bu amaçla Nitelik Eleme ve Ki-kare Filtresi nitelik seçimi yöntemleri kullanılmıştır. Ayrıca çalışmada bir öğrenciyi üstün zekâlı ve yetenekli olarak aday göstermede en iyi performansı veren makine öğrenmesi algoritmasının bulunması da hedeflenmiştir. Bunu gerçekleştirebilmek için Rastgele Orman Algoritması, C4.5 Karar Ağacı Algoritması ve Naive Bayes Sınıflandırıcı makine öğrenmesi algoritmaları kullanılmıştır. Analizler sonucunda Ki-kare Filtresi yöntemiyle 69 soruluk ölçek 20 soruya indirilmiş, sonrasında Naive Bayes Sınıflandırıcı bu yeni veri setine uygulandığında, model %92 doğrulukla bir öğrenciyi üstün zekâlı ve yetenekli olarak aday göstermiştir. Önerilen bu modelin, aday gösterme sürecinde zamandan tasarruf edilmesini sağlayacağı ve ölçeğin öğretmenler tarafından doldurulması esnasında çok sayıda soruyla ilgilenmekten kaynaklı dikkat dağınıklığını önleyerek sonuçların doğruluğunu artıracağı düşünülmektedir. Ayrıca, veriye dayalı öngörü modellerinin aday gösterme sürecinde kullanılmasıyla daha rasyonel kararlar elde edileceğine inanılmaktadır.

Keywords

üstün zekâlı ve yetenekli öğrenciler, nitelik seçimi, makine öğrenmesi, aday gösterme

Asking the Right Questions to Nominate A Student as Gifted and Talented: A Machine Learning Approach

Abstract

In this study, it is aimed to reduce the number of questions from a 69-item scale, which is developed to nominate a student as gifted and talented by selecting the most effective questions. For this purpose, Recursive Feature Elimination and Chi-Square Filter feature selection methods are used. Also, it is aimed to find the best performing machine learning algorithm to nominate a student as gifted and talented in this study. To achieve this, analyses are performed with Random Forest Algorithm, C4.5 Decision Tree Algorithm, and Naive Bayes Classifier machine learning algorithms. As a result of the analyses; the 69-item scale was reduced to 20 questions by using Chi-Square Filter method, and then when Naive Bayes Classifier was applied to this new data set, the model nominated a student with 92% accuracy as gifted and talented. It is thought that the proposed model will save time in the nomination process and prevent the distraction of attention that can be caused by the high number of questions when teachers fill out the scale. Also, it is believed that more rational decisions will be made in the nomination process by working with data-based prediction models.

Keywords

gifted and talented students, feature selection, machine learning, nomination

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