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PISA 2018 Türkiye Örnekleminde Okuma Becerisini Etkileyen Değişkenlerin Boruta Algoritması ile Belirlenmesi

Yıl 2024, Cilt: 57 Sayı: 2, 655 - 701, 25.07.2024
https://doi.org/10.30964/auebfd.1254457

Öz

Bu çalışmanın amacı öğrencilerin okuma beceri düzeylerine göre sınıflandırması üzerinde etkisi olan değişkenlerin belirlenmesidir. Bu amaçla yüksek ve düşük okuma becerisine sahip olarak belirlenen sınıflandırma gruplarını etkiyen değişkenler tespit edilmiştir. Çok sayıda değişkene sahip olan çalışmalarda hangi değişkenin daha etkin olduğunu tespit etmek için kullanılan değişken (öznitelik) seçim işlemi verilerin boyutlarının azaltılmasını ve ilgisiz değişkenlerin çıkarılmasını sağlar. Bu çalışmada Boruta algoritması kullanılarak okul türü, kariyer beklentisi, sosyo-ekonomik durum, BİT’e olan ilgi ve aşinalık, üst biliş stratejileri gibi değişkenlerin öğrencilerin okuma becerilerinde öne çıktığı belirlenmiştir.

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Determination of Variables Affecting Reading Skills Using the Boruta Algorithm in a Turkish Sample from the PISA 2018

Yıl 2024, Cilt: 57 Sayı: 2, 655 - 701, 25.07.2024
https://doi.org/10.30964/auebfd.1254457

Öz

The objective of this study was to identify the variables that influence the classification of students based on their reading proficiency levels. To achieve this, the variables that affect the classification of the groups with high and low reading skills were determined. In studies with several variables, the process of selecting the most effective variable (attribute) reduces the size of the data and eliminates irrelevant variables. The Boruta algorithm was used in this study to determine the variables that most effectively affect students’ reading skills. These variables include school type, career expectations, socioeconomic status, interest in and familiarity with Information and Communication Technology. and metacognitive strategies.

Kaynakça

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  • Unpingco, J. (2019). Machine learning. Python for Probability, Statistics, and Machine Learning (2. baskı, s. 384). Springer International Publishing. https://doi.org/10.1007/978-3-030-18545-9
  • Vázquez-Cano, E., De la Calle-Cabrera, A. M., Hervás-Gómez, C., & López-Meneses, E. (2020). Socio-family context and ıts ınfluence on students’ PISA reading performance scores: evidence from three countries in three continents. Educational Sciences: Theory & Practice, 20(2). https://doi.org/10.12738/jestp.2020.2.004
  • Vázquez-Cano, E., Gómez-Galán, J., Infante-Moro, A., & López-Meneses, E. (2020). Incidence of a non-sustainability use of technology on students’ reading performance in PISA. Sustainability, 12(2), 749. https://doi.org/10.3390/su12020749
  • Venkata, M. D., ve Lingamgunta, S. (2020). Breast cancer multi modality ımage analysis using pheneotype features by SVM. Journal of Science and Technology, 5(1), 52–60. http://jst.org.in/wp-content/uploads/2020/03/7.-Breast-Cancer-Multi-Modality-Image-Analysis-Using-Pheneotype-features-by-SVM.pdf
  • West, M., & Chew, H. E. (2014). Reading in the mobile era: a study of mobile reading in developing countries. R. Krau (Ed). UNESCO Institute for Lifelong Learning. https://unesdoc.unesco.org/ark:/48223/pf0000227436_eng
  • Xiao, Y., Liu, Y., & Hu, J. (2019). Regression analysis of ICT impact factors on early adolescents’ reading proficiency in five high-performing countries. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01646
  • Xu, X., Gu, H., Wang, Y., Wang, J., & Qin, P. (2019). Autoencoder based feature selection method for classification of anticancer drug response. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.00233
  • Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353–363. https://doi.org/10.1016/j.snb.2015.02.025
  • Yilmaz, A., Fer, S., Kelecioglu, H., Doğan, N., Yazıcı, N., Özyalçın Oskay, Ö., Yetkin Özdemin, İ. E., ve Batı, K. (2020). PISA ve Türkiye (2000 - 2018). http://www.egitim.hacettepe.edu.tr/belge/pisaveturkiye.pdf
  • Zaffar, M., Hashmani, M. A., & Savita, K. S. (2017, Kasım). Performance analysis of feature selection algorithm for educational data mining. 2017 IEEE Conference on Big Data and Analytics (ICBDA). https://doi.org/10.1109/ICBDAA.2017.8284099
Toplam 106 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Alan Eğitimleri
Bölüm Araştırma Makalesi
Yazarlar

Sanem Şehribanoğlu 0000-0002-3099-7599

Erken Görünüm Tarihi 11 Mayıs 2024
Yayımlanma Tarihi 25 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 57 Sayı: 2

Kaynak Göster

APA Şehribanoğlu, S. (2024). PISA 2018 Türkiye Örnekleminde Okuma Becerisini Etkileyen Değişkenlerin Boruta Algoritması ile Belirlenmesi. Ankara University Journal of Faculty of Educational Sciences (JFES), 57(2), 655-701. https://doi.org/10.30964/auebfd.1254457

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