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

Abstract

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.

Keywords

• makine öğrenmesi,
• derin öğrenme,
• covid-19
• karar verme,
• eğitim yapıları.

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

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.

Keywords

• Machine learning
• Deep learning
• Covid-19
• Making decision
• Educational structures

References

1. Alsubaie, M. A. (2022). Distance education and the social literacy of elementary school students during the COVID-19 pandemic. Heliyon, 8(7). https://doi.org/10.1016/j.heliyon.2022.e09811
2. American Institute of Architects. (2020, July 31). Re-occupancy assessment tool V3.0. American Institute of Architects. Retrieved July 11, 2021, from https://content.aia.org/sites/default/files/2020 08/ReOccupancy_Assessment_Tool_v3.pdf
3. Amir, L. R., Tanti, I., Maharani, D. A., Wimardhani, Y. S., Julia, V., Sulijaya, B., & Puspitawati, R. (2020). Student perspective of classroom and distance learning during COVID-19 pandemic in the undergraduate dental study program Universitas Indonesia. BMC medical education, 20(1), 392. https://doi.org/10.1186/s12909-020-02312-0
4. Amirzadeh, M., Sobhaninia, S., Buckman, S. T., & Sharifi, A. (2023). Towards building resilient cities to pandemics: A review of COVID-19 literature. Sustainable cities and society, 89, 104326. https://doi.org/10.1016/j.scs.2022.104326
5. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
6. Dehghani, A. A., Movahedi, N., Ghorbani, K., & Eslamian, S. (2023). Decision tree algorithms. In Handbook of hydroinformatics (pp. 171-187). Elsevier. https://doi.org/10.1016/B978-0-12-821285-1.00004-X
7. DeKay, M., & Brown, G. Z. (2013). Sun, wind, and light: architectural design strategies. John Wiley & Sons. Diker, F., & Erkan, İ. (2022). Fuzzy logic method in the design of elementary school classrooms. Architectural Engineering and Design Management, 18(5), 739-758. https://doi.org/10.1080/17452007.2021.1910925
8. Gaisie, E., Oppong-Yeboah, N. Y., & Cobbinah, P. B. (2022). Geographies of infections: Built environment and COVID-19 pandemic in metropolitan Melbourne. Sustainable cities and society, 81, 103838. https://doi.org/10.1016/j.scs.2022.103838

9. Gupta, M. M. (2021). Impact of Coronavirus Disease (COVID-19) pandemic on classroom teaching: Challenges of online classes and solutions. Journal of education and health promotion, 10, 155. https://doi.org/10.4103/jehp.jehp_1104_20
10. Güzelci, O. Z., Şen Bayram, A. K., Alaçam, S., Güzelci, H., Akkuyu, E. I., & Şencan, İ. (2021). Design tactics for enhancing the adaptability of primary and middle schools to the new needs of postpandemic reuse. Archnet- IJAR: International Journal of Architectural Research, 15(1), 148-166. https://doi.org/10.1108/ARCH-10-2020-0237
11. Heiberger, R. M., & Neuwirth, E. (2009). Polynomial regression. In R through excel: a spreadsheet interface for statistics, data analysis, and graphics (pp. 269-284). Springer New York. DOI 10.1007/978-1-4419-0052-4 11
12. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507. DOI: 10.1126/science.1127647
13. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97. DOI: 10.1109/MSP.2012.2205597
14. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., ... & Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5
15. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). Springer.
16. Karadag, I., Güzelci, O. Z., & Alaçam, S. (2023). EDU-AI: A twofold machine learning model to support classroom layout generation. Construction Innovation, 23(4), 898-914. https://doi.org/10.1108/CI-02-2022- 0034
17. Kavuncu, S. K. (2018). Makine öğrenmesi ve derin öğrenme: Nesne tanıma uygulaması [Master’s thesis, Kırıkkale University, Institute of Science and Technology]. YÖK Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi/giris.jsp
18. Keogh‐Brown, M. R., Wren‐Lewis, S., Edmunds, W. J., Beutels, P., & Smith, R. D. (2010). The possible macroeconomic impact on the UK of an influenza pandemic. Health Economics, 19(11), 1345-1360. https://doi.org/10.1002/hec.1554
19. Li, J., & Che, W. (2022). Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities. Sustainable Cities and Society, 83, 103958. https://doi.org/10.1016/j.scs.2022.103958
20. Liu, J., Liao, X., Qian, S., Yuan, J., Wang, F., Liu, Y., ... & Zhang, Z. (2020). Community transmission of severe acute respiratory syndrome coronavirus 2, Shenzhen, China, 2020. Emerging Infectious Diseases, 26(6), 1320. 10.3201/eid2606.200239
21. Liu, Y., Wang, X., Song, C., Chen, J., Shu, H., Wu, M., ... & Pei, T. (2023). Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China. Sustainable Cities and Society, 89, 104314. https://doi.org/10.1016/j.scs.2022.104314
22. Lordan, R., FitzGerald, G. A., & Grosser, T. (2020). Reopening schools during COVID-19. Science, 369(6508), 1146-1146. https://doi.org/10.1126/science.abe5765
23. Mitchell, T. M. (1997). Machine learning. McGraw-Hill. Retrieved from https://www.cs.cmu.edu/~tom/files/MachineLearningTomMitchell. pdf [Date of access: 11.07.2021]
24. Mouratidis, K., & Papagiannakis, A. (2021). COVID-19, internet, and mobility: The rise of telework, telehealth, e-learning, and eshopping. Sustainable Cities and Society, 74, 103182. https://doi.org/10.1016/j.scs.2021.103182
25. Naqa, I., & Murphy, M. J. (2015). What is machine learning? In I. El Naqa, R. Li, M. Murphy (Eds.), Machine Learning in Radiation Oncology (3-11). Springer International Publishing. https://doi.org/10.1007/978-3- 319-18305-3_1
26. Neuwirth, L. S., Jović, S., & Mukherji, B. R. (2021). Reimagining higher education during and post-COVID-19: Challenges and opportunities. Journal of Adult And Continuing Education, 27(2), 141- 156. https://doi.org/10.1177/1477971420947738
27. Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., ... & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International journal of surgery, 78, 185-193. https://doi.org/10.1016/j.ijsu.2020.04.018
28. Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), 210-229. https://doi.org/10.1147/rd.33.0210
29. Suchetana, B., Rajagopalan, B., & Silverstein, J. (2017). Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model. Science of the Total Environment, 598, 249-257. https://doi.org/10.1016/j.scitotenv.2017.03.236
30. United Nations Educational, Scientific and Cultural Organization (UNESCO), (2020). COVID-19 impact on Education. Retrieved June 25, 2021, from https://en.unesco.org/themes/education-emergencies/coronavirusschool-closures
31. Vapnik, V. N. (1997, October). The support vector method. In International conference on artificial neural networks (pp. 261-271). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/BFb0020166
32. World Health Organization (WHO). (2020). Coronavirus disease (COVID-19) advice for the public. Retrieved July 2, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public
33. Yetiş, C., & Kayılı, M. T. (2021). Covid-19 salgını: eğitim yapıları üzerinden yeniden kullanım değerlendirmesi. Journal of Awareness, 6(2), 199-2114. https://doi.org/10.26809/joa.6.2.10
34. Yüksek Öğretim Kurulu (YÖK) (2020). Guide for the Development of Healthy and Clean Environments in Higher Education Institutions. Retrieved July 11, 2021, from https://eski.yok.gov.tr/Documents/Yayinlar/Yayinlarimiz/2020/yuksekogretim-kurumlarinda-saglikli-ve-temiz-ortamlarin-gelistirilmesikilavuzu.pdf
35. Yu, R., Ostwald, M. J., Gu, N., Skates, H., & Feast, S. (2022). Evaluating the effectiveness of online teaching in architecture courses. Architectural Science Review, 65(2), 89-100. https://doi.org/10.1080/00038628.2021.1921689