KOVİD-19 Salgını Sırasında Öğrencilerin Öğrenme Alışkanlıklarının Schur Ayrıştırma Tabanlı Dalgacık Aşırı Öğrenme Makineleri ile Tahmini
Abstract
KOVİD-19 pandemisi 2020 yılında meydana gelen en kötü olaylardan biri olarak lanse edilmektedir. Pandemi sürecinde hayatımızda birçok olumsuzluklar meydana geldi. Yaşam tarzımızı değiştirmek zorunda kaldık. Pandeminin en çok etkilediği alanlardan biri de eğitim olmuştur. Yüz yüze eğitim yerine uzaktan eğitim hayatımızın bir parçası oldu ve eğitim materyallerinin hızlı bir şekilde sayısallaştırılması sağlandı. Öğrencilerini eğitim alışkanlıklarının değişmesi maalesef kaçınılmaz oldu. Bu çalışma, yeni bir makine öğrenmesi yöntemi ile pandemi döneminde öğrencilerin öğrenme alışkanlıklarının tahmini ile ilgilidir. Daha spesifik olarak çalışmada, yeni bir aşırı öğrenme makinesi yaklaşımı geliştirilmiş ve bu yöntem pandemi döneminde öğrencilerin öğrenme alışkanlıklarının tahmininde kullanılmıştır. Geleneksel aşırı öğrenme makinesinde çıkış katmanı ağırlıklarının hesaplanması için Moore-Penrose matris tersi operatörü kullanılırken bu çalışmada Schur ayrıştırma kullanılarak çıkış katmanı ağırlıklarının hesaplanması gerçekleştirilmiştir. Ayrıca, aktivasyon fonksiyonu olarak da dalgacık fonksiyonu kullanılmıştır. Deneysel çalışmalarda kullanılan veri seti, Hanoi'deki 420 ortaokul öğrencisinin KOVİD-19 nedeniyle okul kapanışlarının ilk iki haftasında öğrenme alışkanlıklarına dikkate alınarak toplanmıştır. 5-kat çapraz geçerlilik testi kullanılarak gerçekleştirilen deneylerde, %87.41 oranında doğru tahmin yapıldığı görülmüştür.
Keywords
Aşırı öğrenme makinesi dalgacık çekirdek öğrencilerin öğrenme alışkanlıkları Schur ayrıştırma
References
- [1] World Health Organization. IHR Emergency Committee on Novel Coronavirus (2019-nCoV) (2020).
- [2] Narayanan, L., Pandit, M., Basu, S., Karmakar, A., Bidhan, V., Kumar, H., & Brar, K. (2020). Impact of lockdown due to COVID-19 outbreak: Lifestyle changes and Public Health Concerns in India.
- [3] Trung, T., Hoang, A. D., Nguyen, T. T., Dinh, V. H., Nguyen, Y. C., & Pham, H. H. (2020). Dataset of Vietnamese student's learning habits during COVID-19. Data in Brief, 105682.
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- [5] Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry research, 112934.
- [6] Odriozola-González, P., Planchuelo-Gómez, Á., Irurtia, M. J., & de Luis-García, R. (2020). Psychological effects of the COVID-19 outbreak and lockdown among students and workers of a Spanish university. Psychiatry Research, 113108.
- [7] Bitan, D. T., Grossman-Giron, A., Bloch, Y., Mayer, Y., Shiffman, N., & Mendlovic, S. (2020). Fear of COVID-19 scale: Psychometric characteristics, reliability and validity in the Israeli population. Psychiatry Research, 113100.
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- [9] Kaparounaki, C. K., Patsali, M. E., Mousa, D. P. V., Papadopoulou, E. V., Papadopoulou, K. K., & Fountoulakis, K. N. (2020). University students’ mental health amidst the COVID-19 quarantine in Greece. Psychiatry Research, 113111.
- [10] Alcin, Omer Faruk, Abdulkadir Sengur, and Melih Cevdet Ince. "Forward-backward pursuit based sparse extreme learning machine." Journal of The Faculty of Engineering and Architecture of Gazi University 30.1 (2015): 111-117.
- [11] Alcin, O. F., Sengur, A., Ghofrani, S., & Ince, M. C. (2014). GA-SELM: Greedy algorithms for sparse extreme learning machine. Measurement, 55, 126-132.
Prediction of students' learning habits with Schur decomposition based wavelet extreme learning machines during the COVID-19 pandemic
Abstract
KThe COVID-19 pandemic is touted as one of the worst events to occur in 2020. Many negativities occurred in our lives during the pandemic. We had to change our lifestyle. Education has been one of the areas most affected by the pandemic. Distance education has become a part of our life instead of face-to-face education and the educational materials have been digitized rapidly. Unfortunately, it was inevitable that the educational habits of the students changed. This study is about the prediction of students' learning habits during the pandemic with a new machine learning method. More specifically, in the study, a new extreme learning machine approach was developed and this method was used to predict students' learning habits during the pandemic. While the Moore-Penrose matrix inverse operator is used to calculate the output layer weights in the traditional extreme learning machine, in this study, the output layer weights are calculated using the Schur decomposition. Also, wavelet kernel is used as activation function. The data set used in the experimental studies was collected by considering the learning habits of 420 middle school students in Hanoi during the first two weeks of school closures due to COVID-19. In the experiments performed using the 5-fold cross validation test, it was observed that 87.41% of the correct predictions were obtained.
References
- [1] World Health Organization. IHR Emergency Committee on Novel Coronavirus (2019-nCoV) (2020).
- [2] Narayanan, L., Pandit, M., Basu, S., Karmakar, A., Bidhan, V., Kumar, H., & Brar, K. (2020). Impact of lockdown due to COVID-19 outbreak: Lifestyle changes and Public Health Concerns in India.
- [3] Trung, T., Hoang, A. D., Nguyen, T. T., Dinh, V. H., Nguyen, Y. C., & Pham, H. H. (2020). Dataset of Vietnamese student's learning habits during COVID-19. Data in Brief, 105682.
- [4] Riad, A., Huang, Y., Zheng, L., & Elavsky, S. (2020). COVID-19 Induced Anxiety and Protective Behaviors During COVID-19 Outbreak: Scale Development and Validation. medRxiv.
- [5] Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry research, 112934.
- [6] Odriozola-González, P., Planchuelo-Gómez, Á., Irurtia, M. J., & de Luis-García, R. (2020). Psychological effects of the COVID-19 outbreak and lockdown among students and workers of a Spanish university. Psychiatry Research, 113108.
- [7] Bitan, D. T., Grossman-Giron, A., Bloch, Y., Mayer, Y., Shiffman, N., & Mendlovic, S. (2020). Fear of COVID-19 scale: Psychometric characteristics, reliability and validity in the Israeli population. Psychiatry Research, 113100.
- [8] Zhang, Y., Zhang, H., Ma, X., & Di, Q. (2020). Mental Health Problems during the COVID-19 Pandemics and the Mitigation Effects of Exercise: A Longitudinal Study of College Students in China. International Journal of Environmental Research and Public Health, 17(10), 3722.
- [9] Kaparounaki, C. K., Patsali, M. E., Mousa, D. P. V., Papadopoulou, E. V., Papadopoulou, K. K., & Fountoulakis, K. N. (2020). University students’ mental health amidst the COVID-19 quarantine in Greece. Psychiatry Research, 113111.
- [10] Alcin, Omer Faruk, Abdulkadir Sengur, and Melih Cevdet Ince. "Forward-backward pursuit based sparse extreme learning machine." Journal of The Faculty of Engineering and Architecture of Gazi University 30.1 (2015): 111-117.
- [11] Alcin, O. F., Sengur, A., Ghofrani, S., & Ince, M. C. (2014). GA-SELM: Greedy algorithms for sparse extreme learning machine. Measurement, 55, 126-132.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | June 30, 2021 |
| Submission Date | February 18, 2021 |
| Acceptance Date | May 25, 2021 |
| Published in Issue | Year 2021 Volume: 7 Issue: 1 |
