Estimation of student success with artificial neural networks

Temel Bulgular & Veri:Bu çalışmada Karadeniz Teknik Üniversitesi Tıp Fakültesi Dönem I öğrencilerinin kurul sonu çoktan seçmeli üç ara sınav ve öğrenci başarısını etkileyebilecek diğer faktörler eklenerek dönem sonu final sınavı YSA ve regresyon analizi ile tahmin edilmeye çalışılmış ve YSA’nın daha yüksek performans gösterdiği gözlenmiştir.

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Sonuç & Katkı:Sonuç olarak daha ileri düzeyde araştırmalar yapılarak erken uyarı sistemleri geliştirilebileceği görülmüştür.

🔬Tür:Araştırma Makalesi

📍Kapsam:Genişletilmiş Akademik Örneklem

⚙️Değişkenler:Alan Parametreleri

Özet

Yapay sinir ağları (YSA), sınıflandırma, kümeleme ve regresyon problemlerinin çözümünde başarıyla uygulanabilmektedir. Bu sebeple öğrenci ders başarısının erken dönemde tahmini ve gerekli önlemlerin alınabilmesi için de kullanılabilir. Bu çalışmada Karadeniz Teknik Üniversitesi Tıp Fakültesi Dönem I öğrencilerinin kurul sonu çoktan seçmeli üç ara sınav ve öğrenci başarısını etkileyebilecek diğer faktörler eklenerek dönem sonu final sınavı YSA ve regresyon analizi ile tahmin edilmeye çalışılmış ve YSA’nın daha yüksek performans gösterdiği gözlenmiştir. Sonuç olarak daha ileri düzeyde araştırmalar yapılarak erken uyarı sistemleri geliştirilebileceği görülmüştür.

Abstract

Faculties’ counseling systems need to take early action for problematic students by predicting and tracing student success and related problems. In this study, we compared two methods for predicting students’ success to take systematic and proactive actions for detecting and solving problems in early phase. In this study, it is aimed to estimate the marks that the students of the 1st semester of School of Medicine of Karadeniz Technical University, who were 111 in total, 56% of whom were males and 44 % were females. By using their final exam (F1) results that were gathered through multiple choice tests, variables that could be considered as affecting student performance were analyzed. Artificial neural networks and regression analyses were carried out with success in various research areas for the settlement of classification, clustering and regression problems As a result, it is considered that there appears a need of advanced level research and the automatic information system implementations to be developed at the end of those researches in the student performance estimation as also in F1 estimation.

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Bu Makaleye Atıf Yap

ENGİN, Y. Z., KURT, B., TURHAN, K. (2022). Estimation of student success with artificial neural networks. Eğitim ve Bilim, Cilt: 38 - Sayı: 170, 112–120. https://ajindex.com/articles/497479

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Kaynakça (10)

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