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Journals / Gazi Üniversitesi Ticaret ve Turizm Eğitim Fakültesi Dergisi / 2004 / Cilt: 0 - Sayı: 1

Logistic Regression Analysis and Neural Networks Approach in the Classification of Students' Chievement

Öğrenci Başarılarının Sınıflandırılmasında Lojistik Regrasyon Analizi ve Sinir Ağları Yaklaşımı

Nuray GÜNERİ
Ayşen APAYDIN
Pages
170–188
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These methods have been applied to a data set taken from Gazi University, Faculty of Commerce and Tourism Education and it is observed that the correct classification probability obtained from neural networks is equals to the correct classification probability obtained from logistic regression.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aimed to compare neural networks with logistic regression method to identify causes of student failures and therefore predicting future failures.
📊
Key Findings & Data:Logistic regression and neural networks methods have been compared with respect to their correct classification probabilities of individuals.
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Conclusion & Impact:These methods have been applied to a data set taken from Gazi University, Faculty of Commerce and Tourism Education and it is observed that the correct classification probability obtained from neural networks is equals to the correct classification probability obtained from logistic regression.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This study aimed to compare neural networks with logistic regression method to identify causes of student failures and therefore predicting future failures. Logistic regression and neural networks methods have been compared with respect to their correct classification probabilities of individuals. These methods have been applied to a data set taken from Gazi University, Faculty of Commerce and Tourism Education and it is observed that the correct classification probability obtained from neural networks is equals to the correct classification probability obtained from logistic regression.

Özet

Bu çalışma öğrenci başarısızlıklarının nedenlerini tanımlamak ve böylece gelecekte karşılaşılabilecek başarısızlıkları kestirmek için sinir ağları ile lojistik regresyon yöntemini karşılaştırmayı hedeflemektedir. Lojistik regresyon ve sinir ağları yöntemleri bireylerin doğru sınıflandırılma oranlarına göre karşılaştırılmıştır.Yöntemler; Gazi Üniversitesi Ticaret ve Turizm Eğitim Fakültesi'nden alınan Veriler üzerine uygulanmış ve sinir ağları yönteminden elde edilen oranın,lojistik regresyon yönteminden elde edilen orana eşit olduğu görülmüştür

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Cite This Article

GÜNERİ, N., APAYDIN, A. (2022). Logistic Regression Analysis and Neural Networks Approach in the Classification of Students' Chievement. Gazi Üniversitesi Ticaret ve Turizm Eğitim Fakültesi Dergisi, Cilt: 0 - Sayı: 1, 170–188. https://ajindex.com/articles/524441

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