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Dergiler / Journal of Computational Design / 2025 / Cilt: 6 Sayı: 2

Yüz Yüze Eğitime Geçiş Sürecinde Güvenli Sınıf Kapasitelerinin Belirlenmesine Yönelik Veri Odaklı Bir Yaklaşım

A Data-Driven Approach to Determining Safe Classroom Capacities During the Transition to Face-to-Face Education

Fadime Diker(BALIKESIR UNIVERSITY, FACULTY OF ARCHITECTURE, DEPARTMENT OF ARCHITECTURE, ARCHITECTURE PR.)0000-0001-8088-1570
İlker Erkan(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0001-7104-245X
Sayfa
281–316
Yayın Tarihi
2025-09-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Elde edilen modellerin değerlendirilmesi sonucunda, derin sinir ağları modelinin bu tahmin probleminde diğer gözetimli öğrenme yöntemlerine kıyasla daha uygun bir yaklaşım olduğu sonucuna varılmıştır.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bu çalışmada, yüz yüze eğitime geçiş sürecinde mevcut sınıflarda kaç öğrencinin bulunması gerektiğini tahmin etmek amacıyla farklı modeller geliştirilmiştir.
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Temel Bulgular & Veri:Covid-19 virüsünün bulaşma riskini belirleyen faktör yalnızca fiziksel mesafe değil, aynı zamanda maruz kalma süresidir.
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Sonuç & Katkı:Elde edilen modellerin değerlendirilmesi sonucunda, derin sinir ağları modelinin bu tahmin probleminde diğer gözetimli öğrenme yöntemlerine kıyasla daha uygun bir yaklaşım olduğu sonucuna varılmıştır.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:makine öğrenmesi · derin öğrenme · covid-19

Özet

Bu çalışmada, yüz yüze eğitime geçiş sürecinde mevcut sınıflarda kaç öğrencinin bulunması gerektiğini tahmin etmek amacıyla farklı modeller geliştirilmiştir. Covid-19 virüsünün bulaşma riskini belirleyen faktör yalnızca fiziksel mesafe değil, aynı zamanda maruz kalma süresidir. Bu doğrultuda, mevcut ilkokul sınıflarının ve pencere boyutlarının kullanılarak sınıfların fiziksel boyutlar açısından verimliliğini değerlendirmek için Bulanık Mantık yöntemiyle bir model oluşturulmuştur. Geliştirilen model doğrultusunda elde edilen veriler kullanılarak çeşitli modeller geliştirilmiştir. Elde edilen modellerin değerlendirilmesi sonucunda, derin sinir ağları modelinin bu tahmin probleminde diğer gözetimli öğrenme yöntemlerine kıyasla daha uygun bir yaklaşım olduğu sonucuna varılmıştır. Geliştirilen modelin, eğitim yapılarında gerekli önlemlerin alınması ve virüsün yayılmasını önlemeye yönelik düzenlemelerin yapılması için hazırlanan yönergelere katkı sağlaması beklenmektedir. Yalnızca ilkokul sınıfları incelenerek elde edilen verilerle geliştirilen modeller, farklı eğitim seviyelerindeki sınıfların incelenmesiyle elde edilecek verilerle de uygulanabilir.

Abstract

In this paper, different models have been developed to estimate how many students should be in the existing classrooms to be less affected and protected from the Covid19 virus during transition to face-to-face education. The factor that determines the risk of transmission of the Covid 19 virus is not only physical distance, but the duration of exposure. In this direction, model has been created by Fuzzy Logic method to evaluate the efficiency of classrooms in terms of physical sizes using the classroom and window sizes of existing primary schools. Various models have been developed by using the data obtained in line with the developed model. After the evaluation of the obtained models, it was concluded that deep neural networks model can be accepted as a more suitable approach for this estimation problem than other supervised learning methods. It is expected that the developed model will help the guidelines prepared for taking necessary precautions in educational structures and making arrangements to prevent the transmission of the virus. Developed with the data obtained by examining only the primary school classrooms, developed models can also be applied with the data to be obtained by examining the classrooms of different levels.

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Bu Makaleye Atıf Yap

Diker, F., Erkan, İ. (2025). Yüz Yüze Eğitime Geçiş Sürecinde Güvenli Sınıf Kapasitelerinin Belirlenmesine Yönelik Veri Odaklı Bir Yaklaşım. Journal of Computational Design, Cilt: 6 Sayı: 2, 281–316. https://doi.org/10.53710/jcode.1661952

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