Research Article
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Year 2023, , 181 - 196, 31.03.2023
https://doi.org/10.33200/ijcer.1192590

Abstract

References

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  • Efklides, A. & Petkaki, C. (2005). Effects of mood on students' metacognitive experiences. Learning and Instruction, 15(5), 415-431. https://doi.org/10.1016/j.learninstruc.2005.07.010
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Reviewing the Factors Affecting PISA Reading Skills by Using Random Forest and MARS Methods

Year 2023, , 181 - 196, 31.03.2023
https://doi.org/10.33200/ijcer.1192590

Abstract

The research aims to determine the factors affecting PISA 2018 reading skills using Random Forest and MARS methods and to compare their prediction abilities. This study used the information from 5713 students, 2838 (49.7%) male and 2875 (50.3%) female in the PISA 2018 Turkey. The analysis shows the MARS method performed better than the Random Forest method. The most significant factor affecting reading skills in Turkey is “the number of books in the house” in both methods. The variables the MARS method finds significant are “students' perception of difficulty, motivation for reading skills, father’s educational status, reading pleasure, bullying experience of the student, mother's educational status, attitude towards school, classical artifacts at home, supplementary school books at home, competition at school, competitive power, cooperation perception at school, reading frequency, self-efficacy, poetry books at home, anxiety about reading skills and teacher support.” However, the other variables had no relation to prediction. This study is expected to serve as an example of data mining application in educational research

References

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  • Açıkgöz, T. (2017). Bullying and attitude towards secondary school students: Sample of Kartepe district (Unpublished master’s thesis). Sakarya University.
  • Akman, M., Genç, Y. & Ankaralı, H. (2011). Random Forests Methods and an Application in Health Science. Turkiye Klinikleri J Biostat, 3(1):36-48.
  • Aksu G. & Güzeller C. O. (2016). Classification of PISA 2012 Mathematical Literacy Scores Using Decision-Tree Method: Turkey Sampling. EDUCATION AND SCIENCE, 41(185),101-122. http://dx.doi.org/10.15390/EB.2016.4766
  • Aksu, G. & Doğan, N. (2018). Comparison of Learning Methods Used in Data Mining Under Different Conditions Ankara University Journal of Faculty of Educational Sciences, 51(3), 71-100. https://doi.org/10.30964/auebfd.464262
  • Anıl, D. (2009). Factors Effecting Science Achievement of Science Students in Programme for International Students’ Achievement (PISA) in Turkey. Education and Science, 34(152), 87-100. http://egitimvebilim.ted.org.tr/index.php/EB/article/view/594/74
  • Arabameri, A., Pradhan, B., Pourghasemi, H. R., Rezaei, K. & Kerle, N. (2018). Spatial modelling of gully erosion using GIS and R programing: A comparison among three data mining algorithms. Applied sciences, 8(8), 1369. https://doi.org/10.3390/app8081369
  • Arıcı, Ö. & Altıntaş, Ö. (2014). An Investigation of the PISA 2009 Reading Literacy in Terms of Socio-Economical Backgrounds and Receiving Pre-School Education “Turkey Example”. Ankara University, Journal of Faculty of Educational Sciences, 47(1), 423-448.
  • Bayraktar, V.H. (2015). Student motivation in classroom management and factors that affect motivation. Turkish Studies, 10(3), 1079-1100. http://dx.doi.org/10.7827/TurkishStudies.7788
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  • Bing, M. N. (1999 ). Hypercompetitiveness in academia: Achieving criterion-related validity from item context specificity . Journal of Personality Assessment, 73(1) , 80 – 99. https://doi.org/10.1207/S15327752JPA730106
  • Bozkurt, B. Ü. (2016). A report on reading instruction in Turkey: implications from PISA scale. Abant Journal of İzzet Baysal University Faculty of Education, 16 (4), 1673-1686.
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–-32.
  • Chang, Y. C. & Bangsri, A. (2020). Thai Students’ Perceived Teacher Support on Their Reading Ability: Mediating Effects of Self-Efficacy and Sense of School Belonging. International Journal of Educational Methodology, 6(2), 435 - 446.
  • Chen, W., Pourghasemi, H. R. & Naghibi, S. A. (2018). Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bulletin of Engineering Geology and the Environment, 77(2), 611-629. https://doi.org/10.1007/s10064-017-1004-9
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  • Cutler, D.R., T.C. Edwards, K.H. Beard, A. Cutler, K.T., Hess, J.C. Gibson & J.J. Lawler., (2007). Random forests for classification in ecology. Ecology, 88 (11), 783-2792. https://doi.org/10.1890/07-0539.1
  • Deichmann, J.,Eshghi, A., Haughton, D., Sayek, S. & Teebagy, N. (2002). Application of multiple adaptive regression splines (MARS) in direct response modeling. Journal of Interactive Marketing, 16(4), 15-27. https://doi.org/10.1002/dir.10040
  • Efklides, A. & Petkaki, C. (2005). Effects of mood on students' metacognitive experiences. Learning and Instruction, 15(5), 415-431. https://doi.org/10.1016/j.learninstruc.2005.07.010
  • Erdoğan, E. & Acar Güvendir, M. (2019). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi University Journal of Social Sciences, 20(Özel Sayı),1-31 https://doi.org/10.17494/ogusbd.548530
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There are 81 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Özlem Bezek Güre 0000-0002-5272-4639

Hikmet Şevgin 0000-0002-9727-5865

Murat Kayri 0000-0002-5933-6444

Publication Date March 31, 2023
Published in Issue Year 2023

Cite

APA Bezek Güre, Ö., Şevgin, H., & Kayri, M. (2023). Reviewing the Factors Affecting PISA Reading Skills by Using Random Forest and MARS Methods. International Journal of Contemporary Educational Research, 10(1), 181-196. https://doi.org/10.33200/ijcer.1192590

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