AKADEMİK PERFORMANSIN OTOMATİK SINIFLANDIRILMASI İÇİN ÖZNİTELİKLERİN ANALİZİ

Bu makale, öğrencilerin akademik performansının otomatik olarak sınıflandırılmasında yaygın olarak kullanılan özelliklerin katkılarını analiz etmektedir. Bu sınıflandırma probleminde, çeşitli öznitelikler ve sınıflandırıcılar arasındaki ilişki, kapsamlı bir öznitelik seçim stratejisi kullanılarak analiz edilmiştir. Bu şekilde, en yüksek sınıflandırma performansını sağlayan optimal öznitelik alt kümesi elde edilmiştir. Bu amaçla 15 farklı öznitelik ve 480 örnekten oluşan bir akademik performans veri seti kullanılmıştır. Öznitelikler demografik, akademik geçmiş, ebeveyn katılımı ve davranışsal olmak üzere dört farklı kategoriye aittir. Örnekler, öğrenci başarısının düşük, orta ve yüksek seviyelerine karşılık gelen üç farklı sınıftandır. Değerlendirmeler için 10 farklı sınıflandırma algoritması kullanılmıştır. Kapsamlı deneysel analizler, öğrencilerin akademik performansını sınıflandırma doğruluğunun, özniteliklerin tamamı yerine yalnızca 8 tanesi kullanılarak, %79.40'a kadar artırılabileceğini ortaya koymaktadır.

Anahtar Kelimeler:

Academic performance, Data Mining, Classification, Feature Selection

ANALYSIS OF FEATURES FOR AUTOMATIC CLASSIFICATION OF ACADEMIC PERFORMANCE

This paper analyzes the contributions of features widely used in the automatic classification of students’ academic performance. In this classification problem, the relationship between various features and classifiers is analyzed using an exhaustive feature selection strategy. In this way, the optimal subset of features providing the highest classification performance is obtained. For this purpose, an academic performance dataset consisting of 15 distinct features and 480 samples is used. The features mainly belong to four different categories, including demographic, academic background, parent participation, and behavioral. The samples are from three different classes corresponding to the low, middle, and high levels of students’ success. For evaluations, 10 different classification algorithms are employed. Extensive experimental analysis reveals that the accuracy of the classification of students’ academic performance can be improved up to 79.40% using only 8 features rather than all.

Keywords:

Academic performance, Data mining, Classification, Feature selection,

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