COVID-19-related school closures caused unprecedented and prolonged disruption to daily life, education, and social and physical activities. This disruption in the life course affected the well-being of students from different age groups. This study proposed analyzing student well-being and determining the most influential factors that affected student well-being during the COVID-19 pandemic. With this aim, we adopted a cross-sectional study designed to analyze the student data from the Responses to Educational Disruption Survey (REDS) collected between December 2020 and July 2021 from a large sample of grade 8 or equivalent students from eight countries (n = 20,720), including Burkina Faso, Denmark, Ethiopia, Kenya, the Russian Federation, Slovenia, the United Arab Emirates, and Uzbekistan. We first estimated a well-being IRT score for each student in the REDS student database. Then, we used 10 data-mining approaches to determine the most influential factors that affected the well-being of students during the COVID-19 outbreak. Overall, 178 factors were analyzed. The results indicated that the most influential factors on student well-being were multifarious. The most influential variables on student well-being were students’ worries about contracting COVID-19 at school, their learning progress during the COVID-19 disruption, their motivation to learn when school reopened, and their excitement to reunite with friends after the COVID-19 disruption.
Öz: Finansal piyasaların ana çıktısı bir zaman serisi problemidir ve zaman serileri doğaları gereği gürültülü, durağan olmayan ve karmaşık bir yapı sergilemektedirler. Bu karmaşık yapı sebebiyle zaman serilerinin gelecekteki davranışlarını öngörme süreci araştırmacılar açısından hayli zorlu bir çalışma alanı olmaktadır. Bu çalışmada BIST 100 endeksi günlük getiri yönünün tahmin edilmesinde kapsamlı bir öznitelik mühendisliği işlemi uygulanmış ve farklı makine öğrenmesi algoritmaları kullanılarak modellemeler gerçekleştirilmiştir. Modellere girdi olarak alınacak öznitelikler, serinin özetleyici istatistiklerine, örnekleme dağılımının ek karakteristiklerine ve serinin lineer olmayan/karmaşık yapısını yansıtan gözlenen dinamiklerine bağlı olarak çıkartılmış ve dışsal değişken kullanmadan da sınıflandırma performanslarının oldukça yüksek olduğu gösterilmiştir. Ayrıca farklı eğitim-test oranları kullanılarak tahminlerin dayanıklılığı araştırılmıştır.
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