Öğrenci Başarısının Sınıflandırılmasında Farklı Makine Öğrenmesi Modellerinin Karşılaştırılması

Öz

Eğitim-öğretim sürecinde öğrenci başarısının değerlendirilmesi ve sınıflandırılması, öğretim yöntemlerinin etkinliğini belirlemek açısından kritiktir. Öğrencilerin başarı durumlarını "geçti", "kaldı", "başarılı" ve "başarısız" gibi kategorilere ayırmak hem öğrencilerin akademik gelişimini izlemek hem de öğretim stratejilerini iyileştirmek için önemli bilgiler sağlar. Makine Öğrenmesi (MÖ) modellerinin bu sınıflandırmalarda kullanılması, büyük veri setlerinde daha doğru ve objektif değerlendirmeler yapılmasını sağlar. Bu sebeple bu araştırma, MÖ modellerinin öğrenci başarısını sınıflandırmadaki doğruluğunu incelemeyi amaçlamaktadır. MÖ, eğitimde büyük ve karmaşık veri setlerini analiz ederek daha doğru ve objektif değerlendirmeler sağlar. Bu araştırmada, Naive Bayes (NB), Destek Vektör Makineleri (DVM) ve Rastgele Orman (RO) makine öğrenmesi modellerinin sınıflandırma doğrulukları incelenmiştir. Bu araştırma, öğrencilerin başarı durumunu farklı makine öğrenmesi algoritmalarıyla tahmin ederek elde edilen performans ölçütlerini karşılaştırmakta ve model sonuçlarını incelemektedir. Bu yönüyle araştırma, betimsel tarama türünde ve uygulamalı bir çalışmadır. Etnik köken, ebeveyn eğitim düzeyi ve matematik başarısı gibi değişkenlerden oluşan 1000 örneklem içeren bir veri seti kullanılmıştır. Analizler SPSS ve R programı ile gerçekleştirilmiştir. Araştırma bulguları, en yüksek sınıflandırma doğruluğuna sahip modelin RO modeli olduğunu göstermiştir. MÖ modellerinin eğitimde kullanılması öğrenci başarısını, başarısızlık risklerini, öğretim yöntem ve materyallerinin etkinliğini tahmin ederek eğitim kalitesinin iyileştirilmesine katkı sağlayabilir.

Anahtar Kelimeler

• Eğitim
• Makine öğrenmesi
• Sınıflandırma doğruluğu
• Rastgele orman

A Comparative Analysis of Different Machine Learning Models for Classifying Student Achievement

Öz

In the context of teaching and learning, evaluating and classifying student achievement is critical for determining the effectiveness of instructional methods. Categorizing students’ academic performance into groups such as “passed,” “failed,” “successful,” and “unsuccessful” provides valuable insights for tracking academic progress and improving instructional strategies. The use of Machine Learning (ML) models in such classifications enables more accurate and objective evaluations, particularly when dealing with large datasets. Therefore, this study aims to examine the accuracy of various ML models in classifying student performance. ML offers enhanced precision and objectivity by analyzing large and complex educational datasets. In this study, the classification accuracies of three machine learning algorithms—Naive Bayes (NB), Support Vector Machines (SVM), and Random Forest (RF)—were evaluated. The research compares the performance metrics of these models in predicting students' academic success and examines the results in detail. As such, the study adopts a descriptive survey design and has an applied nature. A dataset comprising 1,000 samples and variables such as ethnicity, parental education level, and mathematics achievement was used. The analyses were conducted using SPSS and R software. The findings reveal that the Random Forest model achieved the highest classification accuracy. The integration of ML models in education can contribute to improving educational quality by predicting student success, identifying risk of failure, and evaluating the effectiveness of instructional methods and materials.

Anahtar Kelimeler

• Education
• Machine learning
• Classification accuracy
• Random forest
• Classification models

Kaynakça

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