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Yıl 2025, Cilt: 14 Sayı: 5, 2058 - 2086, 31.12.2025
https://doi.org/10.15869/itobiad.1544751

Öz

Kaynakça

  • Amiama-Espaillat, C., & Mayor-Ruiz, C. (2017). Lectura digital en la competencia lectora: La influencia en la Generación Z de la República Dominicana. Comunicar. Media Education Research Journal, 15(52), 105–113. https://doi.org/https://doi.org/10.3916/C52-2017-10
  • Anand, N., Sehgal, R., Anand, S., & Kaushik, A. (2021). Feature selection on educational data using Boruta algorithm. International Journal of Computational Intelligence Studies, 10(1). https://doi.org/10.1504/IJCISTUDIES.2021.113826
  • Antzaka, A., Lallier, M., Meyer, S., Diard, J., Carreiras, M., & Valdois, S. (2017). Enhancing reading performance through action video games: the role of visual attention span. Scientific Reports, 7(1), 14563. https://doi.org/10.1038/s41598-017-15119-9
  • Areepattamannil, S., & Santos, I. M. (2019). Adolescent students’ perceived information and communication technology (ICT) competence and autonomy: Examining links to dispositions toward science in 42 countries. Computers in Human Behavior, 98, 50–58. https://doi.org/10.1016/j.chb.2019.04.005
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 117–194. https://doi.org/10.1016/j.compedu.2017.05.007
  • Asiloğulları, A. (2020). The Evaluation of The Relationship Between High School Student’s Life-Long Learning Tendencies and Questioning The Meaning and The Purpose of The Life Behaviour [Bartın University]. https://acikerisim.bartin.edu.tr/bitstream/handle/11772/6474/Azize Asioğulları .pdf?sequence=1&isAllowed=y
  • Aslam, N. M., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An Improved Early Student’s Academic Performance Prediction Using Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), 108. https://doi.org/10.3991/ijet.v16i12.20699
  • Atli, A., & Gür, S. H. (2019). High Schools Students’ Career Choices and Factors Affecting Their Choices. Turkish Psychological Counseling and Guidance Association, 2(1), 32–53. https://dergipark.org.tr/tr/download/article-file/750893
  • Avvisati, F. (2020). The measure of socio-economic status in PISA: a review and some suggested improvements. Large-scale Assessments in Education, 8(1). https://doi.org/10.1186/s40536-020-00086-x
  • Bölükbaş, S., & Gür, B. S. (2020). Tracking and inequality: The results from Turkey. International Journal of Educational Development, 78, 102262. https://doi.org/10.1016/j.ijedudev.2020.102262 Çağlayan, E. (2021). Analysis of Studies Conducted on Education for Disadvantaged Groups and Romani Citizens in Turkey. Journal of Roma Language and Culture Research Institute, 2(1), 1–15. https://dergipark.org.tr/en/pub/raedergisi/issue/62748/905175
  • Çelik, K., & Yurdakul, A. (2020). Investigation of PISA 2015 Reading Ability Achievement of Turkish Students in Terms of Student and School Level Variables. International Journal of Assessment Tools in Education, 7(1), 30–42. https://doi.org/10.21449/ijate.589280
  • CGOIK (2018). On Birinci Kalkınma Planı (2019-2023) Çocuk Çalışma Grubu Raporu. https://www.sbb.gov.tr/wp-content/uploads/2020/04/Cocuk_ve_GenclikOzelIhtisasKomisyonuCocukCalismaGrubuRaporu.pdf
  • Chmielewski, A. K., Dumont, H., & Trautwein, U. (2013). Tracking Effects Depend on Tracking Type. American Educational Research Journal, 50(5), 925–957. https://doi.org/10.3102/0002831213489843 Choi, S., & Lee, S. W. (2020). Enhancing Teacher Self-Efficacy in Multicultural Classrooms and School Climate: The Role of Professional Development in Multicultural Education in the United States and South Korea. AERA Open, 6(4), 233285842097357. https://doi.org/10.1177/2332858420973574
  • Chung, H., Park, S., Kim, J.-I., & Kim, A. (2021). Exploring Variables Affecting Adolescents’ Reading Literacy and Life Satisfaction: PISA 2018 International Comparison of Korea and Finland. Journal of Curriculum and Evaluation, 24(1). https://doi.org/10.29221/jce.2021.24.1.123
  • Çolakoğlu, M. H. (2018). Teachers’ Views and Recommendations About PISA 2015 Results. Journal of Research in Informal Environments, 3(1), 46–66. https://dergipark.org.tr/en/pub/jrinen/issue/39907/373468
  • Çoşkun, K. (2020). Piety and Social Values: An Empiric Study on Imam Hatip High School Students (The Sample of Ankara). Turkish Journal of Religious Studies, 20(1), 213–240. http://marife.org/tr/download/article-file/1094993
  • Dalane, K., & Marcotte, D. E. (2022). The Segregation of Students by Income in Public Schools. Educational Researcher, 51(4), 245–254. https://doi.org/10.3102/0013189X221081853
  • Demir, M. F., & Baloğlu, N. (2020). Relationship Between Metacognition Skills and Academic Procrastination Behaviors of the High School Students. Ahi Evran University Institute of Social Sciences Journal Institute, 6(1), 242–259. https://doi.org/10.31592/aeusbed.640030
  • Depren, S. K., & Depren, Ö. (2021). Cross-Cultural Comparisons of the Factors Influencing the High Reading Achievement in Turkey and China: Evidence from PISA 2018. The Asia-Pacific Education Researcher. https://doi.org/doi.org/10.1007/s40299-021-00584-8
  • Dong, X., & Hu, J. (2019). An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • Dubbeld, A., de Hoog, N., den Brok, P., & de Laat, M. (2019). Teachers’ multicultural attitudes and perceptions of school policy and school climate in relation to burnout. Intercultural Education, 30(6), 599–617. https://doi.org/10.1080/14675986.2018.1538042
  • Emdadi, A., & Eslahchi, C. (2021). Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model. BMC Bioinformatics, 22(1), 22–33. https://doi.org/10.1186/s12859-021-03974-3
  • Erdoğan, E., & Acar Güvendir, M. (2019). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 20, 1–31. https://dergipark.org.tr/tr/download/article-file/686572
  • ERG, (2019). PISA 2018 ne diyor? ERG. https://www.egitimreformugirisimi.org/pisa-2018-ne-diyor/
  • Ertem, H. Y. (2021). Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables. Participatory Educational Research, 8(1), 248–264. https://doi.org/10.17275/per.21.14.8.1
  • Fetler, M. E. (1991). Pitfalls of Using SAT Results to Compare Schools. American Educational Research Journal, 28(2), 481–491. https://doi.org/10.3102/00028312028002481
  • Fırat, T., & Koyuncu, İ. (2020). Investigating Reading Literacy in PISA 2018 Assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Florence, F. O., Adesola, O. A., Hameed, B. A., & Adewumi, O. M. (2017). A Survey on the Reading Habits among Colleges of Education Students in the Information Age. Journal of Education and Practice, 8(8), 106–110.
  • Gamazo, A., & Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167
  • Gubbels, J., Swart, N. M., & Groen, M. A. (2020). Everything in moderation: ICT and reading performance of Dutch 15-year-olds. Large-scale Assessments in Education, 8(1), 1. https://doi.org/10.1186/s40536-020-0079-0
  • Güloğlu, F., & Özay Köse, E. (2020). The Analysis of Multiple Intelligence Types of Social Sciences and Science High School Students in Terms of Different Variables. İInonu University Journal of the Graduate School of Education, 7(13), 1–17. https://doi.org/10.29129/inujgse.570417
  • Guyon, I., & Elisseef, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseef, A. (2006). An Introduction to Feature Extraction. Içinde I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer.
  • Hu, J., & Yu, R. (2021). The effects of ICT-based social media on adolescents’ digital reading performance: A longitudinal study of PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Computers & Education, 175, 104342. https://doi.org/10.1016/j.compedu.2021.104342
  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021
  • Kamar, K. Y. (2020). Relationship between Reading Habits and Students’ Academic Performances of Secondary Schools in Sokoto State, Nigeria. International Journal of Research and Innovation in Social Science, 4(2), 242–245.
  • Karatay, H., Külah, E., & Kaya, S. (2020). Methods, techniques and models for developing reading habit. Research in Reading and Writing Instruction, 8(1), 89–107. https://doi.org/10.35233/oyea.707967
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). Determining Variables That Predict Reading Comprehension Success by Data Mining in PISA 2018. Manisa Celal Bayar University Journal of Social Sciences, 19(4), 241–258. https://doi.org/10.18026/cbayarsos.959609
  • Kaunang, F. J., & Rotikan, R. (2018, Ekim). Students’ Academic Performance Prediction using Data Mining. 2018 Third International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/IAC.2018.8780547
  • Koğar, E. Y. (2021). An Investigation of the Mediating Role of Various Variables in the Effect of Both Gender and Economic, Social and Cultural Status on Reading Literacy. International Journal of Progressive Education, 17(1), 376–391. https://doi.org/10.29329/ijpe.2021.329.24
  • Kong, Y., Seo, Y. S., & Zhai, L. (2022). ICT and Digital Reading Achievement: A Cross-national Comparison using PISA 2018 Data. International Journal of Educational Research, 111, 101912. https://doi.org/10.1016/j.ijer.2021.101912
  • Kösterelioğlu, İ., Çelen, Ü., Kösterelioğlu, M. A., & Ahıska, R. (2019). Success purpose tendencies of high school students. Journal of Human Sciences, 16(2), 662–678. https://doi.org/10.14687/jhs.v16i2.5603
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11). https://doi.org/10.18637/jss.v036.i11
  • Lan, Y.-C., Lo, Y.-L., & Hsu, Y.-S. (2014). The Effects of Meta-Cognitive Instruction on Students’ Reading Comprehension in Computerized Reading Contexts: A Quantitative Meta-Analysis. Educational Technology & Society, 17(4), 186–202.
  • Li, Z., & Qiu, Z. (2018). How does family background affect children’s educational achievement? Evidence from Contemporary China. The Journal of Chinese Sociology, 5(1). https://doi.org/10.1186/s40711-018-0083-8
  • Liu, H., Cao, H., Song, E., Ma, G., Xu, X., Jin, R., Jin, Y., & Hung, C. C. (2019). A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Physica Medica, 63(December 2018), 112–121. https://doi.org/10.1016/j.ejmp.2019.06.003
  • Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68. https://doi.org/10.1016/j.stueduc.2021.100982
  • Mani, K., & Kalpana, P. (2017). An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge. International Journal of Information Technology and Computer Science, 9(7), 61–68. https://doi.org/10.5815/ijitcs.2017.07.07
  • Marks, G. N., & O’Connell, M. (2021). Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Review of Education, 9(3). https://doi.org/10.1002/rev3.3293
  • Marôco, J. (2021). What makes a good reader? Worldwide insights from PIRLS 2016. Reading and Writing, 34(1), 231–272. https://doi.org/10.1007/s11145-020-10068-8
  • MEB. (2015). Introduction of Schools Affiliated to the General Directorate of Secondary Education. http://ogm.meb.gov.tr/meb_iys_dosyalar/2015_05/07092423_ogmokultanitim.pdf
  • MEB. (2018a). Mutlu Çocuklar Güçlü Türkiye 2023 Eğitim Vizyonu. https://2023vizyonu.meb.gov.tr/doc/2023_EGITIM_VIZYONU.pdf
  • MEB. (2018b). Secondary Education Institutions Weekly Course Schedule. https://ttkb.meb.gov.tr/meb_iys_dosyalar/2018_02/21173451_ort_ogrtm_hdc_2018.pdf
  • Mehmood, A., On, B.-W., Lee, I., & Choi, G. (2017). Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning. Symmetry, 9(1), 11. https://doi.org/10.3390/sym9010011
  • MERAM (2017). Types of Secondary Education Institutions. https://meramram.meb.k12.tr/meb_iys_dosyalar/42/26/175064/dosyalar/2017_02/15104831_lsetrler.pdf
  • Mushtaq, S., Soroya, S. H., & Mahmood, K. (2020). Reading habits of generation Z students in Pakistan: Is it time to re-examine school library services? Information Development, 026666692096564. https://doi.org/10.1177/0266666920965642
  • Navarro-Martinez, O., & Peña-Acuña, B. (2022). Technology Usage and Academic Performance in the Pisa 2018 Report. Journal of New Approaches in Educational Research, 11(1), 130. https://doi.org/10.7821/naer.2022.1.735
  • OECD. (2016). PISA 2018 Draft Analytical Frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2019a). PISA 2018 Assessment and Analytical Framework. Içinde OECD iLibrary. OECD.
  • OECD. (2019b). PISA Database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2021a). Country Note for Germany: 21st Readers. Içinde 21st-century readers: Developing literacy skills in a digital world. https://www.oecd.org/pisa/PISA2018_Reading_GERMANY.pdf
  • OECD. (2021b). 21st-Century Readers. OECD. https://doi.org/10.1787/a83d84cb-en
  • Oriogu, C. D., Subair, R. E., Oriogu-Ogbuiyi, D. C., & Ogbuiyi, S. U. (2017). Effect of Reading Habits on the Academic Performance of Students: A Case Study of the Students of Afe Babalola University, Ado-Ekiti, Ekiti State. Teacher Education and Curriculum Studies, 2(5), 74. https://doi.org/10.11648/j.tecs.20170205.13
  • Özbey Demir, Ö. (2020). What Do PISA Results Say About Education Inequality in Turkey? Critical Reviews in Educational Sciences, 1(2), 85–98. https://doi.org/10.22596/cresjournal.0102.85.98
  • Özdemir, Ş., & Karateke, T. (2018). Students’ Reasons for Preferring Imam Preachers Schools (The Sample Of Elazığ). Ondokuz Mayıs University Review of the Faculty of Divinity, 45, 5–33. https://dergipark.org.tr/en/download/article-file/604133
  • Özdemir, S., & Şerbetçi, H. N. (2018). Elementary School Fourth Graders’ Attitudes toward Reading (Bartin Sample). Elementary Education Online, 17(4), 2110–2123. https://doi.org/i 10.17051/ilkonline.2019.506973
  • Özer, M. (2020a). The Paradigm Shift in Vocational Education and Training in Turkey. Gazi Eğitim Fakültesi Dergisi, 40(2), 357–384. https://dergipark.org.tr/tr/download/article-file/1255073
  • Özer, M. (2020b). What Does PISA Tell Us About Performance of Education Systems? Bartın University Journal of Faculty of Education, 9(2), 217–228. https://doi.org/10.14686/buefad.697153
  • Özer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education. Palgrave Communications, 6(1), 34. https://doi.org/10.1057/s41599-020-0409-4
  • Öztürk, Z., & Göksoy, S. (2022). Vocational and Technical Anatolian High School Students’ Opinions for School Alienation and Their Attitudes for Vocational Education. Milli Eğitim Dergisi, 51(234), 1357–1380. https://doi.org/10.37669 milliegitim.852826
  • Pejic, A., & Molcer, P. S. (2019). Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data. 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 313–318. https://doi.org/10.1109/CogInfoCom47531.2019.9089942
  • Qu, J., Ren, K., & Shi, X. (2021). Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting. Water Resources Management, 35(3), 1029–1045. https://doi.org/10.1007/s11269-021-02770-1
  • Reyhanlıoğlu, Ç., & Tiryaki, İ. (2021). An Overview of the Assessment and Evaluation Practices Carried Out In Turkey. Uluslararası Türk Eğitim Bilimleri Dergisi, 9(16), 70–93. https://doi.org/10.46778/goputeb.766689
  • Schiepe-Tiska, A. (2019). School Tracks as Differential Learning Environments Moderate the Relationship Between Teaching Quality and Multidimensional Learning Goals in Mathematics. Frontiers in Education, 4, 1–13. https://doi.org/10.3389/feduc.2019.00004
  • Sciffer, M. G., Perry, L. B., & McConney, A. (2022). Does school socioeconomic composition matter more in some countries than others, and if so, why? Comparative Education, 58(1), 37–51. https://doi.org/10.1080/03050068.2021.2013045
  • Severa, M., & Ceylan, E. (2021). What is school for? Understanding Structural Inequalities through the Experiences of High School Students. Başkent University Journal of Education, 8(1), 196–206.
  • Sevilla, M. P., & Polesel, J. (2022). Vocational education and social inequalities in within- and between-school curriculum tracking. Compare: A Journal of Comparative and International Education, 52(4), 581–599. https://doi.org/10.1080/03057925.2020.1798214
  • Shrestha, S., & Pokharel, M. (2021). Educational data mining in moodle data. International Journal of Informatics and Communication Technology (IJ-ICT), 10(1), 9. https://doi.org/10.11591/ijict.v10i1.pp9-18
  • Sikora, J., & Pokropek, A. (2006). Gendered Career Expectations of Students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random Forest Analysis of Factors Influencing the Students’ Reading Literacy Levels: Using PISA 2018 Korea Data. Asian Journal of Education, 21(1–4), 191–215. https://doi.org/10.15753/aje.2020.03.21.1.191
  • Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970. https://doi.org/10.1007/s10639-019-10089-z
  • Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4
  • Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic Status and School Type as Predictors of Academic Achievement. Journal of Economy Culture and Society, 61(1), 41–64. https://doi.org/10.26650/jecs2020-0034
  • Tat, O., Koyuncu, İ., & Gelbal, S. (2019). The Influence of Using Plausible Values and Survey Weights on Multiple Regression and Hierarchical Linear Model Parameters. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 235–248. https://doi.org/10.21031/epod.486999
  • TEDMEM. (2020). 2019 Eğitim Değerlendirme Raporu (Emin Karip (ed.)). TEDMEM. https://tedmem.org/storage/publications/February2023/f2JxDgyafag6BquTgyYr.pdf
  • Tse, S. K., Xiao, X., & Lam, W. (2013). The influences of gender, reading ability, independent reading, and context on reading attitude. Written Language & Literacy, 16(2), 241–271. https://doi.org/10.1075/wll.16.2.05tse
  • Uğuz, E., Şahin, S., & Yılmaz, R. (2021). The Use of Educational Data Mining in the Evaluation of PISA 2018 Scores of Science. Journal of Information and Communication Technologies. https://doi.org/10.53694/bited.887425
  • 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
  • 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
  • Yazıcı, T., & Kartal, O. Y. (2020). Investigation of High School Students’ Approaches to Learning. Nevşehir Hacı Bektaş Veli University Journal of ISS, 10(2), 625–641. https://doi.org/10.30783/nevsosbilen.783211
  • Yonca, Z. D. (2018). Factors Affecting Finland’s Success in PISA and Comparison with Turkey. Uluslararası Eğitim Bilimleri Dergisi, 14(5), 136–146. https://dergipark.org.tr/tr/download/article-file/563495

Yıl 2025, Cilt: 14 Sayı: 5, 2058 - 2086, 31.12.2025
https://doi.org/10.15869/itobiad.1544751

Öz

Kaynakça

  • Amiama-Espaillat, C., & Mayor-Ruiz, C. (2017). Lectura digital en la competencia lectora: La influencia en la Generación Z de la República Dominicana. Comunicar. Media Education Research Journal, 15(52), 105–113. https://doi.org/https://doi.org/10.3916/C52-2017-10
  • Anand, N., Sehgal, R., Anand, S., & Kaushik, A. (2021). Feature selection on educational data using Boruta algorithm. International Journal of Computational Intelligence Studies, 10(1). https://doi.org/10.1504/IJCISTUDIES.2021.113826
  • Antzaka, A., Lallier, M., Meyer, S., Diard, J., Carreiras, M., & Valdois, S. (2017). Enhancing reading performance through action video games: the role of visual attention span. Scientific Reports, 7(1), 14563. https://doi.org/10.1038/s41598-017-15119-9
  • Areepattamannil, S., & Santos, I. M. (2019). Adolescent students’ perceived information and communication technology (ICT) competence and autonomy: Examining links to dispositions toward science in 42 countries. Computers in Human Behavior, 98, 50–58. https://doi.org/10.1016/j.chb.2019.04.005
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 117–194. https://doi.org/10.1016/j.compedu.2017.05.007
  • Asiloğulları, A. (2020). The Evaluation of The Relationship Between High School Student’s Life-Long Learning Tendencies and Questioning The Meaning and The Purpose of The Life Behaviour [Bartın University]. https://acikerisim.bartin.edu.tr/bitstream/handle/11772/6474/Azize Asioğulları .pdf?sequence=1&isAllowed=y
  • Aslam, N. M., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An Improved Early Student’s Academic Performance Prediction Using Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), 108. https://doi.org/10.3991/ijet.v16i12.20699
  • Atli, A., & Gür, S. H. (2019). High Schools Students’ Career Choices and Factors Affecting Their Choices. Turkish Psychological Counseling and Guidance Association, 2(1), 32–53. https://dergipark.org.tr/tr/download/article-file/750893
  • Avvisati, F. (2020). The measure of socio-economic status in PISA: a review and some suggested improvements. Large-scale Assessments in Education, 8(1). https://doi.org/10.1186/s40536-020-00086-x
  • Bölükbaş, S., & Gür, B. S. (2020). Tracking and inequality: The results from Turkey. International Journal of Educational Development, 78, 102262. https://doi.org/10.1016/j.ijedudev.2020.102262 Çağlayan, E. (2021). Analysis of Studies Conducted on Education for Disadvantaged Groups and Romani Citizens in Turkey. Journal of Roma Language and Culture Research Institute, 2(1), 1–15. https://dergipark.org.tr/en/pub/raedergisi/issue/62748/905175
  • Çelik, K., & Yurdakul, A. (2020). Investigation of PISA 2015 Reading Ability Achievement of Turkish Students in Terms of Student and School Level Variables. International Journal of Assessment Tools in Education, 7(1), 30–42. https://doi.org/10.21449/ijate.589280
  • CGOIK (2018). On Birinci Kalkınma Planı (2019-2023) Çocuk Çalışma Grubu Raporu. https://www.sbb.gov.tr/wp-content/uploads/2020/04/Cocuk_ve_GenclikOzelIhtisasKomisyonuCocukCalismaGrubuRaporu.pdf
  • Chmielewski, A. K., Dumont, H., & Trautwein, U. (2013). Tracking Effects Depend on Tracking Type. American Educational Research Journal, 50(5), 925–957. https://doi.org/10.3102/0002831213489843 Choi, S., & Lee, S. W. (2020). Enhancing Teacher Self-Efficacy in Multicultural Classrooms and School Climate: The Role of Professional Development in Multicultural Education in the United States and South Korea. AERA Open, 6(4), 233285842097357. https://doi.org/10.1177/2332858420973574
  • Chung, H., Park, S., Kim, J.-I., & Kim, A. (2021). Exploring Variables Affecting Adolescents’ Reading Literacy and Life Satisfaction: PISA 2018 International Comparison of Korea and Finland. Journal of Curriculum and Evaluation, 24(1). https://doi.org/10.29221/jce.2021.24.1.123
  • Çolakoğlu, M. H. (2018). Teachers’ Views and Recommendations About PISA 2015 Results. Journal of Research in Informal Environments, 3(1), 46–66. https://dergipark.org.tr/en/pub/jrinen/issue/39907/373468
  • Çoşkun, K. (2020). Piety and Social Values: An Empiric Study on Imam Hatip High School Students (The Sample of Ankara). Turkish Journal of Religious Studies, 20(1), 213–240. http://marife.org/tr/download/article-file/1094993
  • Dalane, K., & Marcotte, D. E. (2022). The Segregation of Students by Income in Public Schools. Educational Researcher, 51(4), 245–254. https://doi.org/10.3102/0013189X221081853
  • Demir, M. F., & Baloğlu, N. (2020). Relationship Between Metacognition Skills and Academic Procrastination Behaviors of the High School Students. Ahi Evran University Institute of Social Sciences Journal Institute, 6(1), 242–259. https://doi.org/10.31592/aeusbed.640030
  • Depren, S. K., & Depren, Ö. (2021). Cross-Cultural Comparisons of the Factors Influencing the High Reading Achievement in Turkey and China: Evidence from PISA 2018. The Asia-Pacific Education Researcher. https://doi.org/doi.org/10.1007/s40299-021-00584-8
  • Dong, X., & Hu, J. (2019). An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • Dubbeld, A., de Hoog, N., den Brok, P., & de Laat, M. (2019). Teachers’ multicultural attitudes and perceptions of school policy and school climate in relation to burnout. Intercultural Education, 30(6), 599–617. https://doi.org/10.1080/14675986.2018.1538042
  • Emdadi, A., & Eslahchi, C. (2021). Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model. BMC Bioinformatics, 22(1), 22–33. https://doi.org/10.1186/s12859-021-03974-3
  • Erdoğan, E., & Acar Güvendir, M. (2019). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 20, 1–31. https://dergipark.org.tr/tr/download/article-file/686572
  • ERG, (2019). PISA 2018 ne diyor? ERG. https://www.egitimreformugirisimi.org/pisa-2018-ne-diyor/
  • Ertem, H. Y. (2021). Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables. Participatory Educational Research, 8(1), 248–264. https://doi.org/10.17275/per.21.14.8.1
  • Fetler, M. E. (1991). Pitfalls of Using SAT Results to Compare Schools. American Educational Research Journal, 28(2), 481–491. https://doi.org/10.3102/00028312028002481
  • Fırat, T., & Koyuncu, İ. (2020). Investigating Reading Literacy in PISA 2018 Assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Florence, F. O., Adesola, O. A., Hameed, B. A., & Adewumi, O. M. (2017). A Survey on the Reading Habits among Colleges of Education Students in the Information Age. Journal of Education and Practice, 8(8), 106–110.
  • Gamazo, A., & Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167
  • Gubbels, J., Swart, N. M., & Groen, M. A. (2020). Everything in moderation: ICT and reading performance of Dutch 15-year-olds. Large-scale Assessments in Education, 8(1), 1. https://doi.org/10.1186/s40536-020-0079-0
  • Güloğlu, F., & Özay Köse, E. (2020). The Analysis of Multiple Intelligence Types of Social Sciences and Science High School Students in Terms of Different Variables. İInonu University Journal of the Graduate School of Education, 7(13), 1–17. https://doi.org/10.29129/inujgse.570417
  • Guyon, I., & Elisseef, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseef, A. (2006). An Introduction to Feature Extraction. Içinde I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer.
  • Hu, J., & Yu, R. (2021). The effects of ICT-based social media on adolescents’ digital reading performance: A longitudinal study of PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Computers & Education, 175, 104342. https://doi.org/10.1016/j.compedu.2021.104342
  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021
  • Kamar, K. Y. (2020). Relationship between Reading Habits and Students’ Academic Performances of Secondary Schools in Sokoto State, Nigeria. International Journal of Research and Innovation in Social Science, 4(2), 242–245.
  • Karatay, H., Külah, E., & Kaya, S. (2020). Methods, techniques and models for developing reading habit. Research in Reading and Writing Instruction, 8(1), 89–107. https://doi.org/10.35233/oyea.707967
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). Determining Variables That Predict Reading Comprehension Success by Data Mining in PISA 2018. Manisa Celal Bayar University Journal of Social Sciences, 19(4), 241–258. https://doi.org/10.18026/cbayarsos.959609
  • Kaunang, F. J., & Rotikan, R. (2018, Ekim). Students’ Academic Performance Prediction using Data Mining. 2018 Third International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/IAC.2018.8780547
  • Koğar, E. Y. (2021). An Investigation of the Mediating Role of Various Variables in the Effect of Both Gender and Economic, Social and Cultural Status on Reading Literacy. International Journal of Progressive Education, 17(1), 376–391. https://doi.org/10.29329/ijpe.2021.329.24
  • Kong, Y., Seo, Y. S., & Zhai, L. (2022). ICT and Digital Reading Achievement: A Cross-national Comparison using PISA 2018 Data. International Journal of Educational Research, 111, 101912. https://doi.org/10.1016/j.ijer.2021.101912
  • Kösterelioğlu, İ., Çelen, Ü., Kösterelioğlu, M. A., & Ahıska, R. (2019). Success purpose tendencies of high school students. Journal of Human Sciences, 16(2), 662–678. https://doi.org/10.14687/jhs.v16i2.5603
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11). https://doi.org/10.18637/jss.v036.i11
  • Lan, Y.-C., Lo, Y.-L., & Hsu, Y.-S. (2014). The Effects of Meta-Cognitive Instruction on Students’ Reading Comprehension in Computerized Reading Contexts: A Quantitative Meta-Analysis. Educational Technology & Society, 17(4), 186–202.
  • Li, Z., & Qiu, Z. (2018). How does family background affect children’s educational achievement? Evidence from Contemporary China. The Journal of Chinese Sociology, 5(1). https://doi.org/10.1186/s40711-018-0083-8
  • Liu, H., Cao, H., Song, E., Ma, G., Xu, X., Jin, R., Jin, Y., & Hung, C. C. (2019). A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Physica Medica, 63(December 2018), 112–121. https://doi.org/10.1016/j.ejmp.2019.06.003
  • Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68. https://doi.org/10.1016/j.stueduc.2021.100982
  • Mani, K., & Kalpana, P. (2017). An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge. International Journal of Information Technology and Computer Science, 9(7), 61–68. https://doi.org/10.5815/ijitcs.2017.07.07
  • Marks, G. N., & O’Connell, M. (2021). Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Review of Education, 9(3). https://doi.org/10.1002/rev3.3293
  • Marôco, J. (2021). What makes a good reader? Worldwide insights from PIRLS 2016. Reading and Writing, 34(1), 231–272. https://doi.org/10.1007/s11145-020-10068-8
  • MEB. (2015). Introduction of Schools Affiliated to the General Directorate of Secondary Education. http://ogm.meb.gov.tr/meb_iys_dosyalar/2015_05/07092423_ogmokultanitim.pdf
  • MEB. (2018a). Mutlu Çocuklar Güçlü Türkiye 2023 Eğitim Vizyonu. https://2023vizyonu.meb.gov.tr/doc/2023_EGITIM_VIZYONU.pdf
  • MEB. (2018b). Secondary Education Institutions Weekly Course Schedule. https://ttkb.meb.gov.tr/meb_iys_dosyalar/2018_02/21173451_ort_ogrtm_hdc_2018.pdf
  • Mehmood, A., On, B.-W., Lee, I., & Choi, G. (2017). Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning. Symmetry, 9(1), 11. https://doi.org/10.3390/sym9010011
  • MERAM (2017). Types of Secondary Education Institutions. https://meramram.meb.k12.tr/meb_iys_dosyalar/42/26/175064/dosyalar/2017_02/15104831_lsetrler.pdf
  • Mushtaq, S., Soroya, S. H., & Mahmood, K. (2020). Reading habits of generation Z students in Pakistan: Is it time to re-examine school library services? Information Development, 026666692096564. https://doi.org/10.1177/0266666920965642
  • Navarro-Martinez, O., & Peña-Acuña, B. (2022). Technology Usage and Academic Performance in the Pisa 2018 Report. Journal of New Approaches in Educational Research, 11(1), 130. https://doi.org/10.7821/naer.2022.1.735
  • OECD. (2016). PISA 2018 Draft Analytical Frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2019a). PISA 2018 Assessment and Analytical Framework. Içinde OECD iLibrary. OECD.
  • OECD. (2019b). PISA Database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2021a). Country Note for Germany: 21st Readers. Içinde 21st-century readers: Developing literacy skills in a digital world. https://www.oecd.org/pisa/PISA2018_Reading_GERMANY.pdf
  • OECD. (2021b). 21st-Century Readers. OECD. https://doi.org/10.1787/a83d84cb-en
  • Oriogu, C. D., Subair, R. E., Oriogu-Ogbuiyi, D. C., & Ogbuiyi, S. U. (2017). Effect of Reading Habits on the Academic Performance of Students: A Case Study of the Students of Afe Babalola University, Ado-Ekiti, Ekiti State. Teacher Education and Curriculum Studies, 2(5), 74. https://doi.org/10.11648/j.tecs.20170205.13
  • Özbey Demir, Ö. (2020). What Do PISA Results Say About Education Inequality in Turkey? Critical Reviews in Educational Sciences, 1(2), 85–98. https://doi.org/10.22596/cresjournal.0102.85.98
  • Özdemir, Ş., & Karateke, T. (2018). Students’ Reasons for Preferring Imam Preachers Schools (The Sample Of Elazığ). Ondokuz Mayıs University Review of the Faculty of Divinity, 45, 5–33. https://dergipark.org.tr/en/download/article-file/604133
  • Özdemir, S., & Şerbetçi, H. N. (2018). Elementary School Fourth Graders’ Attitudes toward Reading (Bartin Sample). Elementary Education Online, 17(4), 2110–2123. https://doi.org/i 10.17051/ilkonline.2019.506973
  • Özer, M. (2020a). The Paradigm Shift in Vocational Education and Training in Turkey. Gazi Eğitim Fakültesi Dergisi, 40(2), 357–384. https://dergipark.org.tr/tr/download/article-file/1255073
  • Özer, M. (2020b). What Does PISA Tell Us About Performance of Education Systems? Bartın University Journal of Faculty of Education, 9(2), 217–228. https://doi.org/10.14686/buefad.697153
  • Özer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education. Palgrave Communications, 6(1), 34. https://doi.org/10.1057/s41599-020-0409-4
  • Öztürk, Z., & Göksoy, S. (2022). Vocational and Technical Anatolian High School Students’ Opinions for School Alienation and Their Attitudes for Vocational Education. Milli Eğitim Dergisi, 51(234), 1357–1380. https://doi.org/10.37669 milliegitim.852826
  • Pejic, A., & Molcer, P. S. (2019). Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data. 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 313–318. https://doi.org/10.1109/CogInfoCom47531.2019.9089942
  • Qu, J., Ren, K., & Shi, X. (2021). Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting. Water Resources Management, 35(3), 1029–1045. https://doi.org/10.1007/s11269-021-02770-1
  • Reyhanlıoğlu, Ç., & Tiryaki, İ. (2021). An Overview of the Assessment and Evaluation Practices Carried Out In Turkey. Uluslararası Türk Eğitim Bilimleri Dergisi, 9(16), 70–93. https://doi.org/10.46778/goputeb.766689
  • Schiepe-Tiska, A. (2019). School Tracks as Differential Learning Environments Moderate the Relationship Between Teaching Quality and Multidimensional Learning Goals in Mathematics. Frontiers in Education, 4, 1–13. https://doi.org/10.3389/feduc.2019.00004
  • Sciffer, M. G., Perry, L. B., & McConney, A. (2022). Does school socioeconomic composition matter more in some countries than others, and if so, why? Comparative Education, 58(1), 37–51. https://doi.org/10.1080/03050068.2021.2013045
  • Severa, M., & Ceylan, E. (2021). What is school for? Understanding Structural Inequalities through the Experiences of High School Students. Başkent University Journal of Education, 8(1), 196–206.
  • Sevilla, M. P., & Polesel, J. (2022). Vocational education and social inequalities in within- and between-school curriculum tracking. Compare: A Journal of Comparative and International Education, 52(4), 581–599. https://doi.org/10.1080/03057925.2020.1798214
  • Shrestha, S., & Pokharel, M. (2021). Educational data mining in moodle data. International Journal of Informatics and Communication Technology (IJ-ICT), 10(1), 9. https://doi.org/10.11591/ijict.v10i1.pp9-18
  • Sikora, J., & Pokropek, A. (2006). Gendered Career Expectations of Students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random Forest Analysis of Factors Influencing the Students’ Reading Literacy Levels: Using PISA 2018 Korea Data. Asian Journal of Education, 21(1–4), 191–215. https://doi.org/10.15753/aje.2020.03.21.1.191
  • Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970. https://doi.org/10.1007/s10639-019-10089-z
  • Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4
  • Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic Status and School Type as Predictors of Academic Achievement. Journal of Economy Culture and Society, 61(1), 41–64. https://doi.org/10.26650/jecs2020-0034
  • Tat, O., Koyuncu, İ., & Gelbal, S. (2019). The Influence of Using Plausible Values and Survey Weights on Multiple Regression and Hierarchical Linear Model Parameters. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 235–248. https://doi.org/10.21031/epod.486999
  • TEDMEM. (2020). 2019 Eğitim Değerlendirme Raporu (Emin Karip (ed.)). TEDMEM. https://tedmem.org/storage/publications/February2023/f2JxDgyafag6BquTgyYr.pdf
  • Tse, S. K., Xiao, X., & Lam, W. (2013). The influences of gender, reading ability, independent reading, and context on reading attitude. Written Language & Literacy, 16(2), 241–271. https://doi.org/10.1075/wll.16.2.05tse
  • Uğuz, E., Şahin, S., & Yılmaz, R. (2021). The Use of Educational Data Mining in the Evaluation of PISA 2018 Scores of Science. Journal of Information and Communication Technologies. https://doi.org/10.53694/bited.887425
  • 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
  • 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
  • Yazıcı, T., & Kartal, O. Y. (2020). Investigation of High School Students’ Approaches to Learning. Nevşehir Hacı Bektaş Veli University Journal of ISS, 10(2), 625–641. https://doi.org/10.30783/nevsosbilen.783211
  • Yonca, Z. D. (2018). Factors Affecting Finland’s Success in PISA and Comparison with Turkey. Uluslararası Eğitim Bilimleri Dergisi, 14(5), 136–146. https://dergipark.org.tr/tr/download/article-file/563495

Yıl 2025, Cilt: 14 Sayı: 5, 2058 - 2086, 31.12.2025
https://doi.org/10.15869/itobiad.1544751

Öz

Kaynakça

  • Amiama-Espaillat, C., & Mayor-Ruiz, C. (2017). Lectura digital en la competencia lectora: La influencia en la Generación Z de la República Dominicana. Comunicar. Media Education Research Journal, 15(52), 105–113. https://doi.org/https://doi.org/10.3916/C52-2017-10
  • Anand, N., Sehgal, R., Anand, S., & Kaushik, A. (2021). Feature selection on educational data using Boruta algorithm. International Journal of Computational Intelligence Studies, 10(1). https://doi.org/10.1504/IJCISTUDIES.2021.113826
  • Antzaka, A., Lallier, M., Meyer, S., Diard, J., Carreiras, M., & Valdois, S. (2017). Enhancing reading performance through action video games: the role of visual attention span. Scientific Reports, 7(1), 14563. https://doi.org/10.1038/s41598-017-15119-9
  • Areepattamannil, S., & Santos, I. M. (2019). Adolescent students’ perceived information and communication technology (ICT) competence and autonomy: Examining links to dispositions toward science in 42 countries. Computers in Human Behavior, 98, 50–58. https://doi.org/10.1016/j.chb.2019.04.005
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 117–194. https://doi.org/10.1016/j.compedu.2017.05.007
  • Asiloğulları, A. (2020). The Evaluation of The Relationship Between High School Student’s Life-Long Learning Tendencies and Questioning The Meaning and The Purpose of The Life Behaviour [Bartın University]. https://acikerisim.bartin.edu.tr/bitstream/handle/11772/6474/Azize Asioğulları .pdf?sequence=1&isAllowed=y
  • Aslam, N. M., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An Improved Early Student’s Academic Performance Prediction Using Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), 108. https://doi.org/10.3991/ijet.v16i12.20699
  • Atli, A., & Gür, S. H. (2019). High Schools Students’ Career Choices and Factors Affecting Their Choices. Turkish Psychological Counseling and Guidance Association, 2(1), 32–53. https://dergipark.org.tr/tr/download/article-file/750893
  • Avvisati, F. (2020). The measure of socio-economic status in PISA: a review and some suggested improvements. Large-scale Assessments in Education, 8(1). https://doi.org/10.1186/s40536-020-00086-x
  • Bölükbaş, S., & Gür, B. S. (2020). Tracking and inequality: The results from Turkey. International Journal of Educational Development, 78, 102262. https://doi.org/10.1016/j.ijedudev.2020.102262 Çağlayan, E. (2021). Analysis of Studies Conducted on Education for Disadvantaged Groups and Romani Citizens in Turkey. Journal of Roma Language and Culture Research Institute, 2(1), 1–15. https://dergipark.org.tr/en/pub/raedergisi/issue/62748/905175
  • Çelik, K., & Yurdakul, A. (2020). Investigation of PISA 2015 Reading Ability Achievement of Turkish Students in Terms of Student and School Level Variables. International Journal of Assessment Tools in Education, 7(1), 30–42. https://doi.org/10.21449/ijate.589280
  • CGOIK (2018). On Birinci Kalkınma Planı (2019-2023) Çocuk Çalışma Grubu Raporu. https://www.sbb.gov.tr/wp-content/uploads/2020/04/Cocuk_ve_GenclikOzelIhtisasKomisyonuCocukCalismaGrubuRaporu.pdf
  • Chmielewski, A. K., Dumont, H., & Trautwein, U. (2013). Tracking Effects Depend on Tracking Type. American Educational Research Journal, 50(5), 925–957. https://doi.org/10.3102/0002831213489843 Choi, S., & Lee, S. W. (2020). Enhancing Teacher Self-Efficacy in Multicultural Classrooms and School Climate: The Role of Professional Development in Multicultural Education in the United States and South Korea. AERA Open, 6(4), 233285842097357. https://doi.org/10.1177/2332858420973574
  • Chung, H., Park, S., Kim, J.-I., & Kim, A. (2021). Exploring Variables Affecting Adolescents’ Reading Literacy and Life Satisfaction: PISA 2018 International Comparison of Korea and Finland. Journal of Curriculum and Evaluation, 24(1). https://doi.org/10.29221/jce.2021.24.1.123
  • Çolakoğlu, M. H. (2018). Teachers’ Views and Recommendations About PISA 2015 Results. Journal of Research in Informal Environments, 3(1), 46–66. https://dergipark.org.tr/en/pub/jrinen/issue/39907/373468
  • Çoşkun, K. (2020). Piety and Social Values: An Empiric Study on Imam Hatip High School Students (The Sample of Ankara). Turkish Journal of Religious Studies, 20(1), 213–240. http://marife.org/tr/download/article-file/1094993
  • Dalane, K., & Marcotte, D. E. (2022). The Segregation of Students by Income in Public Schools. Educational Researcher, 51(4), 245–254. https://doi.org/10.3102/0013189X221081853
  • Demir, M. F., & Baloğlu, N. (2020). Relationship Between Metacognition Skills and Academic Procrastination Behaviors of the High School Students. Ahi Evran University Institute of Social Sciences Journal Institute, 6(1), 242–259. https://doi.org/10.31592/aeusbed.640030
  • Depren, S. K., & Depren, Ö. (2021). Cross-Cultural Comparisons of the Factors Influencing the High Reading Achievement in Turkey and China: Evidence from PISA 2018. The Asia-Pacific Education Researcher. https://doi.org/doi.org/10.1007/s40299-021-00584-8
  • Dong, X., & Hu, J. (2019). An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • Dubbeld, A., de Hoog, N., den Brok, P., & de Laat, M. (2019). Teachers’ multicultural attitudes and perceptions of school policy and school climate in relation to burnout. Intercultural Education, 30(6), 599–617. https://doi.org/10.1080/14675986.2018.1538042
  • Emdadi, A., & Eslahchi, C. (2021). Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model. BMC Bioinformatics, 22(1), 22–33. https://doi.org/10.1186/s12859-021-03974-3
  • Erdoğan, E., & Acar Güvendir, M. (2019). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 20, 1–31. https://dergipark.org.tr/tr/download/article-file/686572
  • ERG, (2019). PISA 2018 ne diyor? ERG. https://www.egitimreformugirisimi.org/pisa-2018-ne-diyor/
  • Ertem, H. Y. (2021). Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables. Participatory Educational Research, 8(1), 248–264. https://doi.org/10.17275/per.21.14.8.1
  • Fetler, M. E. (1991). Pitfalls of Using SAT Results to Compare Schools. American Educational Research Journal, 28(2), 481–491. https://doi.org/10.3102/00028312028002481
  • Fırat, T., & Koyuncu, İ. (2020). Investigating Reading Literacy in PISA 2018 Assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Florence, F. O., Adesola, O. A., Hameed, B. A., & Adewumi, O. M. (2017). A Survey on the Reading Habits among Colleges of Education Students in the Information Age. Journal of Education and Practice, 8(8), 106–110.
  • Gamazo, A., & Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167
  • Gubbels, J., Swart, N. M., & Groen, M. A. (2020). Everything in moderation: ICT and reading performance of Dutch 15-year-olds. Large-scale Assessments in Education, 8(1), 1. https://doi.org/10.1186/s40536-020-0079-0
  • Güloğlu, F., & Özay Köse, E. (2020). The Analysis of Multiple Intelligence Types of Social Sciences and Science High School Students in Terms of Different Variables. İInonu University Journal of the Graduate School of Education, 7(13), 1–17. https://doi.org/10.29129/inujgse.570417
  • Guyon, I., & Elisseef, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseef, A. (2006). An Introduction to Feature Extraction. Içinde I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer.
  • Hu, J., & Yu, R. (2021). The effects of ICT-based social media on adolescents’ digital reading performance: A longitudinal study of PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Computers & Education, 175, 104342. https://doi.org/10.1016/j.compedu.2021.104342
  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021
  • Kamar, K. Y. (2020). Relationship between Reading Habits and Students’ Academic Performances of Secondary Schools in Sokoto State, Nigeria. International Journal of Research and Innovation in Social Science, 4(2), 242–245.
  • Karatay, H., Külah, E., & Kaya, S. (2020). Methods, techniques and models for developing reading habit. Research in Reading and Writing Instruction, 8(1), 89–107. https://doi.org/10.35233/oyea.707967
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). Determining Variables That Predict Reading Comprehension Success by Data Mining in PISA 2018. Manisa Celal Bayar University Journal of Social Sciences, 19(4), 241–258. https://doi.org/10.18026/cbayarsos.959609
  • Kaunang, F. J., & Rotikan, R. (2018, Ekim). Students’ Academic Performance Prediction using Data Mining. 2018 Third International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/IAC.2018.8780547
  • Koğar, E. Y. (2021). An Investigation of the Mediating Role of Various Variables in the Effect of Both Gender and Economic, Social and Cultural Status on Reading Literacy. International Journal of Progressive Education, 17(1), 376–391. https://doi.org/10.29329/ijpe.2021.329.24
  • Kong, Y., Seo, Y. S., & Zhai, L. (2022). ICT and Digital Reading Achievement: A Cross-national Comparison using PISA 2018 Data. International Journal of Educational Research, 111, 101912. https://doi.org/10.1016/j.ijer.2021.101912
  • Kösterelioğlu, İ., Çelen, Ü., Kösterelioğlu, M. A., & Ahıska, R. (2019). Success purpose tendencies of high school students. Journal of Human Sciences, 16(2), 662–678. https://doi.org/10.14687/jhs.v16i2.5603
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11). https://doi.org/10.18637/jss.v036.i11
  • Lan, Y.-C., Lo, Y.-L., & Hsu, Y.-S. (2014). The Effects of Meta-Cognitive Instruction on Students’ Reading Comprehension in Computerized Reading Contexts: A Quantitative Meta-Analysis. Educational Technology & Society, 17(4), 186–202.
  • Li, Z., & Qiu, Z. (2018). How does family background affect children’s educational achievement? Evidence from Contemporary China. The Journal of Chinese Sociology, 5(1). https://doi.org/10.1186/s40711-018-0083-8
  • Liu, H., Cao, H., Song, E., Ma, G., Xu, X., Jin, R., Jin, Y., & Hung, C. C. (2019). A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Physica Medica, 63(December 2018), 112–121. https://doi.org/10.1016/j.ejmp.2019.06.003
  • Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68. https://doi.org/10.1016/j.stueduc.2021.100982
  • Mani, K., & Kalpana, P. (2017). An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge. International Journal of Information Technology and Computer Science, 9(7), 61–68. https://doi.org/10.5815/ijitcs.2017.07.07
  • Marks, G. N., & O’Connell, M. (2021). Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Review of Education, 9(3). https://doi.org/10.1002/rev3.3293
  • Marôco, J. (2021). What makes a good reader? Worldwide insights from PIRLS 2016. Reading and Writing, 34(1), 231–272. https://doi.org/10.1007/s11145-020-10068-8
  • MEB. (2015). Introduction of Schools Affiliated to the General Directorate of Secondary Education. http://ogm.meb.gov.tr/meb_iys_dosyalar/2015_05/07092423_ogmokultanitim.pdf
  • MEB. (2018a). Mutlu Çocuklar Güçlü Türkiye 2023 Eğitim Vizyonu. https://2023vizyonu.meb.gov.tr/doc/2023_EGITIM_VIZYONU.pdf
  • MEB. (2018b). Secondary Education Institutions Weekly Course Schedule. https://ttkb.meb.gov.tr/meb_iys_dosyalar/2018_02/21173451_ort_ogrtm_hdc_2018.pdf
  • Mehmood, A., On, B.-W., Lee, I., & Choi, G. (2017). Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning. Symmetry, 9(1), 11. https://doi.org/10.3390/sym9010011
  • MERAM (2017). Types of Secondary Education Institutions. https://meramram.meb.k12.tr/meb_iys_dosyalar/42/26/175064/dosyalar/2017_02/15104831_lsetrler.pdf
  • Mushtaq, S., Soroya, S. H., & Mahmood, K. (2020). Reading habits of generation Z students in Pakistan: Is it time to re-examine school library services? Information Development, 026666692096564. https://doi.org/10.1177/0266666920965642
  • Navarro-Martinez, O., & Peña-Acuña, B. (2022). Technology Usage and Academic Performance in the Pisa 2018 Report. Journal of New Approaches in Educational Research, 11(1), 130. https://doi.org/10.7821/naer.2022.1.735
  • OECD. (2016). PISA 2018 Draft Analytical Frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2019a). PISA 2018 Assessment and Analytical Framework. Içinde OECD iLibrary. OECD.
  • OECD. (2019b). PISA Database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2021a). Country Note for Germany: 21st Readers. Içinde 21st-century readers: Developing literacy skills in a digital world. https://www.oecd.org/pisa/PISA2018_Reading_GERMANY.pdf
  • OECD. (2021b). 21st-Century Readers. OECD. https://doi.org/10.1787/a83d84cb-en
  • Oriogu, C. D., Subair, R. E., Oriogu-Ogbuiyi, D. C., & Ogbuiyi, S. U. (2017). Effect of Reading Habits on the Academic Performance of Students: A Case Study of the Students of Afe Babalola University, Ado-Ekiti, Ekiti State. Teacher Education and Curriculum Studies, 2(5), 74. https://doi.org/10.11648/j.tecs.20170205.13
  • Özbey Demir, Ö. (2020). What Do PISA Results Say About Education Inequality in Turkey? Critical Reviews in Educational Sciences, 1(2), 85–98. https://doi.org/10.22596/cresjournal.0102.85.98
  • Özdemir, Ş., & Karateke, T. (2018). Students’ Reasons for Preferring Imam Preachers Schools (The Sample Of Elazığ). Ondokuz Mayıs University Review of the Faculty of Divinity, 45, 5–33. https://dergipark.org.tr/en/download/article-file/604133
  • Özdemir, S., & Şerbetçi, H. N. (2018). Elementary School Fourth Graders’ Attitudes toward Reading (Bartin Sample). Elementary Education Online, 17(4), 2110–2123. https://doi.org/i 10.17051/ilkonline.2019.506973
  • Özer, M. (2020a). The Paradigm Shift in Vocational Education and Training in Turkey. Gazi Eğitim Fakültesi Dergisi, 40(2), 357–384. https://dergipark.org.tr/tr/download/article-file/1255073
  • Özer, M. (2020b). What Does PISA Tell Us About Performance of Education Systems? Bartın University Journal of Faculty of Education, 9(2), 217–228. https://doi.org/10.14686/buefad.697153
  • Özer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education. Palgrave Communications, 6(1), 34. https://doi.org/10.1057/s41599-020-0409-4
  • Öztürk, Z., & Göksoy, S. (2022). Vocational and Technical Anatolian High School Students’ Opinions for School Alienation and Their Attitudes for Vocational Education. Milli Eğitim Dergisi, 51(234), 1357–1380. https://doi.org/10.37669 milliegitim.852826
  • Pejic, A., & Molcer, P. S. (2019). Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data. 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 313–318. https://doi.org/10.1109/CogInfoCom47531.2019.9089942
  • Qu, J., Ren, K., & Shi, X. (2021). Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting. Water Resources Management, 35(3), 1029–1045. https://doi.org/10.1007/s11269-021-02770-1
  • Reyhanlıoğlu, Ç., & Tiryaki, İ. (2021). An Overview of the Assessment and Evaluation Practices Carried Out In Turkey. Uluslararası Türk Eğitim Bilimleri Dergisi, 9(16), 70–93. https://doi.org/10.46778/goputeb.766689
  • Schiepe-Tiska, A. (2019). School Tracks as Differential Learning Environments Moderate the Relationship Between Teaching Quality and Multidimensional Learning Goals in Mathematics. Frontiers in Education, 4, 1–13. https://doi.org/10.3389/feduc.2019.00004
  • Sciffer, M. G., Perry, L. B., & McConney, A. (2022). Does school socioeconomic composition matter more in some countries than others, and if so, why? Comparative Education, 58(1), 37–51. https://doi.org/10.1080/03050068.2021.2013045
  • Severa, M., & Ceylan, E. (2021). What is school for? Understanding Structural Inequalities through the Experiences of High School Students. Başkent University Journal of Education, 8(1), 196–206.
  • Sevilla, M. P., & Polesel, J. (2022). Vocational education and social inequalities in within- and between-school curriculum tracking. Compare: A Journal of Comparative and International Education, 52(4), 581–599. https://doi.org/10.1080/03057925.2020.1798214
  • Shrestha, S., & Pokharel, M. (2021). Educational data mining in moodle data. International Journal of Informatics and Communication Technology (IJ-ICT), 10(1), 9. https://doi.org/10.11591/ijict.v10i1.pp9-18
  • Sikora, J., & Pokropek, A. (2006). Gendered Career Expectations of Students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random Forest Analysis of Factors Influencing the Students’ Reading Literacy Levels: Using PISA 2018 Korea Data. Asian Journal of Education, 21(1–4), 191–215. https://doi.org/10.15753/aje.2020.03.21.1.191
  • Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970. https://doi.org/10.1007/s10639-019-10089-z
  • Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4
  • Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic Status and School Type as Predictors of Academic Achievement. Journal of Economy Culture and Society, 61(1), 41–64. https://doi.org/10.26650/jecs2020-0034
  • Tat, O., Koyuncu, İ., & Gelbal, S. (2019). The Influence of Using Plausible Values and Survey Weights on Multiple Regression and Hierarchical Linear Model Parameters. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 235–248. https://doi.org/10.21031/epod.486999
  • TEDMEM. (2020). 2019 Eğitim Değerlendirme Raporu (Emin Karip (ed.)). TEDMEM. https://tedmem.org/storage/publications/February2023/f2JxDgyafag6BquTgyYr.pdf
  • Tse, S. K., Xiao, X., & Lam, W. (2013). The influences of gender, reading ability, independent reading, and context on reading attitude. Written Language & Literacy, 16(2), 241–271. https://doi.org/10.1075/wll.16.2.05tse
  • Uğuz, E., Şahin, S., & Yılmaz, R. (2021). The Use of Educational Data Mining in the Evaluation of PISA 2018 Scores of Science. Journal of Information and Communication Technologies. https://doi.org/10.53694/bited.887425
  • 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
  • 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
  • Yazıcı, T., & Kartal, O. Y. (2020). Investigation of High School Students’ Approaches to Learning. Nevşehir Hacı Bektaş Veli University Journal of ISS, 10(2), 625–641. https://doi.org/10.30783/nevsosbilen.783211
  • Yonca, Z. D. (2018). Factors Affecting Finland’s Success in PISA and Comparison with Turkey. Uluslararası Eğitim Bilimleri Dergisi, 14(5), 136–146. https://dergipark.org.tr/tr/download/article-file/563495

Türkiye’de Okul Türlerindeki Farklılaşmanın PISA 2018 Okuma Beceri Düzeylerine Göre Değerlendirilmesi

Yıl 2025, Cilt: 14 Sayı: 5, 2058 - 2086, 31.12.2025
https://doi.org/10.15869/itobiad.1544751

Öz

Türk eğitim sisteminde farklı eğitim anlayışlarına ve niteliklerine sahip okul türleri arasındaki farklılıkların incelendiği bu çalışmada, PISA 2018 Türkiye örnekleminde okuma beceri düzeyleri dikkate alınarak yüksek ve düşük okuma becerisine göre sınıflandırma gruplarını etkileyen değişkenler temel alınmıştır. Bu çalışmada, EDM kapsamındaki öznitelik seçim yöntemlerinden biri olan Boruta algoritması yardımıyla, Türkiye'de PISA sınavına giren farklı müfredatlara sahip altı okul türü dikkate alınarak öğrencilerin okuma becerilerini etkileyen önemli değişkenlerin belirlenmesi amaçlanmıştır. PISA sonuçlarına göre fen liseleri, genel PISA başarısı ve matematik, fen ve okuma alanlarında diğer okul türlerine göre daha yüksek bir başarı düzeyine sahiptir. PISA sınav sonuçlarına göre eğitim çıktılarındaki farklılıklar bu çalışmayla daha da belirginleşmiştir.. Eğitim eşitsizliği ortaya çıkarılmış ve okullar arasındaki farklılıklara dikkat çekilmiştir. Mesleki Teknik Anadolu liseleri ülkenin donanımlı ara eleman ihtiyacını karşılayan ve ülkenin ekonomik gelişiminde önemli bir yere sahiptir. Bu nedenle öğrencilerin dezavantajlı durumlarının giderilmesi müfredatlarında bir revizyona gidilmesinin iyi olacağı düşünülmektedir. Ebeveynlerin bazıları çocuklarının dini eğitim almaları için Anadolu imam hatip liselerini tercih etme eğilimindeyken bu okullar çocukların gelecekteki mesleki beklentilerini karşılamamaktadır. Bu ailelere inançlı bireyler olmak düşüncesi ile mesleki beklenti arasındaki farkın net olarak anlatılmasının özellikle gençlerin gelecekleri açısından daha faydalı olacağı düşünülmektedir. Sosyoekonomik durumun performans üzerinde etkili olduğu ancak meta bilişsel strateji eğitimleri alan öğrencilerde bu yetersizliklerin ortadan kalktığı belirlenmiştir.

Kaynakça

  • Amiama-Espaillat, C., & Mayor-Ruiz, C. (2017). Lectura digital en la competencia lectora: La influencia en la Generación Z de la República Dominicana. Comunicar. Media Education Research Journal, 15(52), 105–113. https://doi.org/https://doi.org/10.3916/C52-2017-10
  • Anand, N., Sehgal, R., Anand, S., & Kaushik, A. (2021). Feature selection on educational data using Boruta algorithm. International Journal of Computational Intelligence Studies, 10(1). https://doi.org/10.1504/IJCISTUDIES.2021.113826
  • Antzaka, A., Lallier, M., Meyer, S., Diard, J., Carreiras, M., & Valdois, S. (2017). Enhancing reading performance through action video games: the role of visual attention span. Scientific Reports, 7(1), 14563. https://doi.org/10.1038/s41598-017-15119-9
  • Areepattamannil, S., & Santos, I. M. (2019). Adolescent students’ perceived information and communication technology (ICT) competence and autonomy: Examining links to dispositions toward science in 42 countries. Computers in Human Behavior, 98, 50–58. https://doi.org/10.1016/j.chb.2019.04.005
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 117–194. https://doi.org/10.1016/j.compedu.2017.05.007
  • Asiloğulları, A. (2020). The Evaluation of The Relationship Between High School Student’s Life-Long Learning Tendencies and Questioning The Meaning and The Purpose of The Life Behaviour [Bartın University]. https://acikerisim.bartin.edu.tr/bitstream/handle/11772/6474/Azize Asioğulları .pdf?sequence=1&isAllowed=y
  • Aslam, N. M., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An Improved Early Student’s Academic Performance Prediction Using Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), 108. https://doi.org/10.3991/ijet.v16i12.20699
  • Atli, A., & Gür, S. H. (2019). High Schools Students’ Career Choices and Factors Affecting Their Choices. Turkish Psychological Counseling and Guidance Association, 2(1), 32–53. https://dergipark.org.tr/tr/download/article-file/750893
  • Avvisati, F. (2020). The measure of socio-economic status in PISA: a review and some suggested improvements. Large-scale Assessments in Education, 8(1). https://doi.org/10.1186/s40536-020-00086-x
  • Bölükbaş, S., & Gür, B. S. (2020). Tracking and inequality: The results from Turkey. International Journal of Educational Development, 78, 102262. https://doi.org/10.1016/j.ijedudev.2020.102262 Çağlayan, E. (2021). Analysis of Studies Conducted on Education for Disadvantaged Groups and Romani Citizens in Turkey. Journal of Roma Language and Culture Research Institute, 2(1), 1–15. https://dergipark.org.tr/en/pub/raedergisi/issue/62748/905175
  • Çelik, K., & Yurdakul, A. (2020). Investigation of PISA 2015 Reading Ability Achievement of Turkish Students in Terms of Student and School Level Variables. International Journal of Assessment Tools in Education, 7(1), 30–42. https://doi.org/10.21449/ijate.589280
  • CGOIK (2018). On Birinci Kalkınma Planı (2019-2023) Çocuk Çalışma Grubu Raporu. https://www.sbb.gov.tr/wp-content/uploads/2020/04/Cocuk_ve_GenclikOzelIhtisasKomisyonuCocukCalismaGrubuRaporu.pdf
  • Chmielewski, A. K., Dumont, H., & Trautwein, U. (2013). Tracking Effects Depend on Tracking Type. American Educational Research Journal, 50(5), 925–957. https://doi.org/10.3102/0002831213489843 Choi, S., & Lee, S. W. (2020). Enhancing Teacher Self-Efficacy in Multicultural Classrooms and School Climate: The Role of Professional Development in Multicultural Education in the United States and South Korea. AERA Open, 6(4), 233285842097357. https://doi.org/10.1177/2332858420973574
  • Chung, H., Park, S., Kim, J.-I., & Kim, A. (2021). Exploring Variables Affecting Adolescents’ Reading Literacy and Life Satisfaction: PISA 2018 International Comparison of Korea and Finland. Journal of Curriculum and Evaluation, 24(1). https://doi.org/10.29221/jce.2021.24.1.123
  • Çolakoğlu, M. H. (2018). Teachers’ Views and Recommendations About PISA 2015 Results. Journal of Research in Informal Environments, 3(1), 46–66. https://dergipark.org.tr/en/pub/jrinen/issue/39907/373468
  • Çoşkun, K. (2020). Piety and Social Values: An Empiric Study on Imam Hatip High School Students (The Sample of Ankara). Turkish Journal of Religious Studies, 20(1), 213–240. http://marife.org/tr/download/article-file/1094993
  • Dalane, K., & Marcotte, D. E. (2022). The Segregation of Students by Income in Public Schools. Educational Researcher, 51(4), 245–254. https://doi.org/10.3102/0013189X221081853
  • Demir, M. F., & Baloğlu, N. (2020). Relationship Between Metacognition Skills and Academic Procrastination Behaviors of the High School Students. Ahi Evran University Institute of Social Sciences Journal Institute, 6(1), 242–259. https://doi.org/10.31592/aeusbed.640030
  • Depren, S. K., & Depren, Ö. (2021). Cross-Cultural Comparisons of the Factors Influencing the High Reading Achievement in Turkey and China: Evidence from PISA 2018. The Asia-Pacific Education Researcher. https://doi.org/doi.org/10.1007/s40299-021-00584-8
  • Dong, X., & Hu, J. (2019). An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • Dubbeld, A., de Hoog, N., den Brok, P., & de Laat, M. (2019). Teachers’ multicultural attitudes and perceptions of school policy and school climate in relation to burnout. Intercultural Education, 30(6), 599–617. https://doi.org/10.1080/14675986.2018.1538042
  • Emdadi, A., & Eslahchi, C. (2021). Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model. BMC Bioinformatics, 22(1), 22–33. https://doi.org/10.1186/s12859-021-03974-3
  • Erdoğan, E., & Acar Güvendir, M. (2019). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 20, 1–31. https://dergipark.org.tr/tr/download/article-file/686572
  • ERG, (2019). PISA 2018 ne diyor? ERG. https://www.egitimreformugirisimi.org/pisa-2018-ne-diyor/
  • Ertem, H. Y. (2021). Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables. Participatory Educational Research, 8(1), 248–264. https://doi.org/10.17275/per.21.14.8.1
  • Fetler, M. E. (1991). Pitfalls of Using SAT Results to Compare Schools. American Educational Research Journal, 28(2), 481–491. https://doi.org/10.3102/00028312028002481
  • Fırat, T., & Koyuncu, İ. (2020). Investigating Reading Literacy in PISA 2018 Assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Florence, F. O., Adesola, O. A., Hameed, B. A., & Adewumi, O. M. (2017). A Survey on the Reading Habits among Colleges of Education Students in the Information Age. Journal of Education and Practice, 8(8), 106–110.
  • Gamazo, A., & Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167
  • Gubbels, J., Swart, N. M., & Groen, M. A. (2020). Everything in moderation: ICT and reading performance of Dutch 15-year-olds. Large-scale Assessments in Education, 8(1), 1. https://doi.org/10.1186/s40536-020-0079-0
  • Güloğlu, F., & Özay Köse, E. (2020). The Analysis of Multiple Intelligence Types of Social Sciences and Science High School Students in Terms of Different Variables. İInonu University Journal of the Graduate School of Education, 7(13), 1–17. https://doi.org/10.29129/inujgse.570417
  • Guyon, I., & Elisseef, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseef, A. (2006). An Introduction to Feature Extraction. Içinde I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer.
  • Hu, J., & Yu, R. (2021). The effects of ICT-based social media on adolescents’ digital reading performance: A longitudinal study of PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Computers & Education, 175, 104342. https://doi.org/10.1016/j.compedu.2021.104342
  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021
  • Kamar, K. Y. (2020). Relationship between Reading Habits and Students’ Academic Performances of Secondary Schools in Sokoto State, Nigeria. International Journal of Research and Innovation in Social Science, 4(2), 242–245.
  • Karatay, H., Külah, E., & Kaya, S. (2020). Methods, techniques and models for developing reading habit. Research in Reading and Writing Instruction, 8(1), 89–107. https://doi.org/10.35233/oyea.707967
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). Determining Variables That Predict Reading Comprehension Success by Data Mining in PISA 2018. Manisa Celal Bayar University Journal of Social Sciences, 19(4), 241–258. https://doi.org/10.18026/cbayarsos.959609
  • Kaunang, F. J., & Rotikan, R. (2018, Ekim). Students’ Academic Performance Prediction using Data Mining. 2018 Third International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/IAC.2018.8780547
  • Koğar, E. Y. (2021). An Investigation of the Mediating Role of Various Variables in the Effect of Both Gender and Economic, Social and Cultural Status on Reading Literacy. International Journal of Progressive Education, 17(1), 376–391. https://doi.org/10.29329/ijpe.2021.329.24
  • Kong, Y., Seo, Y. S., & Zhai, L. (2022). ICT and Digital Reading Achievement: A Cross-national Comparison using PISA 2018 Data. International Journal of Educational Research, 111, 101912. https://doi.org/10.1016/j.ijer.2021.101912
  • Kösterelioğlu, İ., Çelen, Ü., Kösterelioğlu, M. A., & Ahıska, R. (2019). Success purpose tendencies of high school students. Journal of Human Sciences, 16(2), 662–678. https://doi.org/10.14687/jhs.v16i2.5603
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11). https://doi.org/10.18637/jss.v036.i11
  • Lan, Y.-C., Lo, Y.-L., & Hsu, Y.-S. (2014). The Effects of Meta-Cognitive Instruction on Students’ Reading Comprehension in Computerized Reading Contexts: A Quantitative Meta-Analysis. Educational Technology & Society, 17(4), 186–202.
  • Li, Z., & Qiu, Z. (2018). How does family background affect children’s educational achievement? Evidence from Contemporary China. The Journal of Chinese Sociology, 5(1). https://doi.org/10.1186/s40711-018-0083-8
  • Liu, H., Cao, H., Song, E., Ma, G., Xu, X., Jin, R., Jin, Y., & Hung, C. C. (2019). A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Physica Medica, 63(December 2018), 112–121. https://doi.org/10.1016/j.ejmp.2019.06.003
  • Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68. https://doi.org/10.1016/j.stueduc.2021.100982
  • Mani, K., & Kalpana, P. (2017). An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge. International Journal of Information Technology and Computer Science, 9(7), 61–68. https://doi.org/10.5815/ijitcs.2017.07.07
  • Marks, G. N., & O’Connell, M. (2021). Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Review of Education, 9(3). https://doi.org/10.1002/rev3.3293
  • Marôco, J. (2021). What makes a good reader? Worldwide insights from PIRLS 2016. Reading and Writing, 34(1), 231–272. https://doi.org/10.1007/s11145-020-10068-8
  • MEB. (2015). Introduction of Schools Affiliated to the General Directorate of Secondary Education. http://ogm.meb.gov.tr/meb_iys_dosyalar/2015_05/07092423_ogmokultanitim.pdf
  • MEB. (2018a). Mutlu Çocuklar Güçlü Türkiye 2023 Eğitim Vizyonu. https://2023vizyonu.meb.gov.tr/doc/2023_EGITIM_VIZYONU.pdf
  • MEB. (2018b). Secondary Education Institutions Weekly Course Schedule. https://ttkb.meb.gov.tr/meb_iys_dosyalar/2018_02/21173451_ort_ogrtm_hdc_2018.pdf
  • Mehmood, A., On, B.-W., Lee, I., & Choi, G. (2017). Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning. Symmetry, 9(1), 11. https://doi.org/10.3390/sym9010011
  • MERAM (2017). Types of Secondary Education Institutions. https://meramram.meb.k12.tr/meb_iys_dosyalar/42/26/175064/dosyalar/2017_02/15104831_lsetrler.pdf
  • Mushtaq, S., Soroya, S. H., & Mahmood, K. (2020). Reading habits of generation Z students in Pakistan: Is it time to re-examine school library services? Information Development, 026666692096564. https://doi.org/10.1177/0266666920965642
  • Navarro-Martinez, O., & Peña-Acuña, B. (2022). Technology Usage and Academic Performance in the Pisa 2018 Report. Journal of New Approaches in Educational Research, 11(1), 130. https://doi.org/10.7821/naer.2022.1.735
  • OECD. (2016). PISA 2018 Draft Analytical Frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2019a). PISA 2018 Assessment and Analytical Framework. Içinde OECD iLibrary. OECD.
  • OECD. (2019b). PISA Database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2021a). Country Note for Germany: 21st Readers. Içinde 21st-century readers: Developing literacy skills in a digital world. https://www.oecd.org/pisa/PISA2018_Reading_GERMANY.pdf
  • OECD. (2021b). 21st-Century Readers. OECD. https://doi.org/10.1787/a83d84cb-en
  • Oriogu, C. D., Subair, R. E., Oriogu-Ogbuiyi, D. C., & Ogbuiyi, S. U. (2017). Effect of Reading Habits on the Academic Performance of Students: A Case Study of the Students of Afe Babalola University, Ado-Ekiti, Ekiti State. Teacher Education and Curriculum Studies, 2(5), 74. https://doi.org/10.11648/j.tecs.20170205.13
  • Özbey Demir, Ö. (2020). What Do PISA Results Say About Education Inequality in Turkey? Critical Reviews in Educational Sciences, 1(2), 85–98. https://doi.org/10.22596/cresjournal.0102.85.98
  • Özdemir, Ş., & Karateke, T. (2018). Students’ Reasons for Preferring Imam Preachers Schools (The Sample Of Elazığ). Ondokuz Mayıs University Review of the Faculty of Divinity, 45, 5–33. https://dergipark.org.tr/en/download/article-file/604133
  • Özdemir, S., & Şerbetçi, H. N. (2018). Elementary School Fourth Graders’ Attitudes toward Reading (Bartin Sample). Elementary Education Online, 17(4), 2110–2123. https://doi.org/i 10.17051/ilkonline.2019.506973
  • Özer, M. (2020a). The Paradigm Shift in Vocational Education and Training in Turkey. Gazi Eğitim Fakültesi Dergisi, 40(2), 357–384. https://dergipark.org.tr/tr/download/article-file/1255073
  • Özer, M. (2020b). What Does PISA Tell Us About Performance of Education Systems? Bartın University Journal of Faculty of Education, 9(2), 217–228. https://doi.org/10.14686/buefad.697153
  • Özer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education. Palgrave Communications, 6(1), 34. https://doi.org/10.1057/s41599-020-0409-4
  • Öztürk, Z., & Göksoy, S. (2022). Vocational and Technical Anatolian High School Students’ Opinions for School Alienation and Their Attitudes for Vocational Education. Milli Eğitim Dergisi, 51(234), 1357–1380. https://doi.org/10.37669 milliegitim.852826
  • Pejic, A., & Molcer, P. S. (2019). Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data. 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 313–318. https://doi.org/10.1109/CogInfoCom47531.2019.9089942
  • Qu, J., Ren, K., & Shi, X. (2021). Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting. Water Resources Management, 35(3), 1029–1045. https://doi.org/10.1007/s11269-021-02770-1
  • Reyhanlıoğlu, Ç., & Tiryaki, İ. (2021). An Overview of the Assessment and Evaluation Practices Carried Out In Turkey. Uluslararası Türk Eğitim Bilimleri Dergisi, 9(16), 70–93. https://doi.org/10.46778/goputeb.766689
  • Schiepe-Tiska, A. (2019). School Tracks as Differential Learning Environments Moderate the Relationship Between Teaching Quality and Multidimensional Learning Goals in Mathematics. Frontiers in Education, 4, 1–13. https://doi.org/10.3389/feduc.2019.00004
  • Sciffer, M. G., Perry, L. B., & McConney, A. (2022). Does school socioeconomic composition matter more in some countries than others, and if so, why? Comparative Education, 58(1), 37–51. https://doi.org/10.1080/03050068.2021.2013045
  • Severa, M., & Ceylan, E. (2021). What is school for? Understanding Structural Inequalities through the Experiences of High School Students. Başkent University Journal of Education, 8(1), 196–206.
  • Sevilla, M. P., & Polesel, J. (2022). Vocational education and social inequalities in within- and between-school curriculum tracking. Compare: A Journal of Comparative and International Education, 52(4), 581–599. https://doi.org/10.1080/03057925.2020.1798214
  • Shrestha, S., & Pokharel, M. (2021). Educational data mining in moodle data. International Journal of Informatics and Communication Technology (IJ-ICT), 10(1), 9. https://doi.org/10.11591/ijict.v10i1.pp9-18
  • Sikora, J., & Pokropek, A. (2006). Gendered Career Expectations of Students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random Forest Analysis of Factors Influencing the Students’ Reading Literacy Levels: Using PISA 2018 Korea Data. Asian Journal of Education, 21(1–4), 191–215. https://doi.org/10.15753/aje.2020.03.21.1.191
  • Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970. https://doi.org/10.1007/s10639-019-10089-z
  • Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4
  • Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic Status and School Type as Predictors of Academic Achievement. Journal of Economy Culture and Society, 61(1), 41–64. https://doi.org/10.26650/jecs2020-0034
  • Tat, O., Koyuncu, İ., & Gelbal, S. (2019). The Influence of Using Plausible Values and Survey Weights on Multiple Regression and Hierarchical Linear Model Parameters. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 235–248. https://doi.org/10.21031/epod.486999
  • TEDMEM. (2020). 2019 Eğitim Değerlendirme Raporu (Emin Karip (ed.)). TEDMEM. https://tedmem.org/storage/publications/February2023/f2JxDgyafag6BquTgyYr.pdf
  • Tse, S. K., Xiao, X., & Lam, W. (2013). The influences of gender, reading ability, independent reading, and context on reading attitude. Written Language & Literacy, 16(2), 241–271. https://doi.org/10.1075/wll.16.2.05tse
  • Uğuz, E., Şahin, S., & Yılmaz, R. (2021). The Use of Educational Data Mining in the Evaluation of PISA 2018 Scores of Science. Journal of Information and Communication Technologies. https://doi.org/10.53694/bited.887425
  • 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
  • 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
  • Yazıcı, T., & Kartal, O. Y. (2020). Investigation of High School Students’ Approaches to Learning. Nevşehir Hacı Bektaş Veli University Journal of ISS, 10(2), 625–641. https://doi.org/10.30783/nevsosbilen.783211
  • Yonca, Z. D. (2018). Factors Affecting Finland’s Success in PISA and Comparison with Turkey. Uluslararası Eğitim Bilimleri Dergisi, 14(5), 136–146. https://dergipark.org.tr/tr/download/article-file/563495

Differentiation in School Types in Turkey Evaluation of PISA 2018 Based on Reading Skill Levels

Yıl 2025, Cilt: 14 Sayı: 5, 2058 - 2086, 31.12.2025
https://doi.org/10.15869/itobiad.1544751

Öz

This study examines the differences between school types with varying educational cultures and qualities within the Turkish education system. It is based on variables affecting the classification of groups according to high and low reading skills, with consideration of reading skill levels in the PISA 2018 Turkey sample. In this study, with the help of the Boruta algorithm, one of the feature selection methods within the scope of Educational Data Mining, the objective was to determine the important variables that affect student reading skills by considering six types of school with different curriculum that take the PISA exam in Turkey. The results of the PISA indicate that science high schools demonstrate a higher level of success than other school types in terms of overall PISA success, as well as in the domains of mathematics, science and reading.The disparities in educational outcomeshave become more pronounced as a consequence of this study. The study brought to light the existence of educational inequality and drew attention to the discrepancies between different educational institutions. Vocational and Technical Anatolian high schools play an instrumental role in meeting the country's need for well-trained intermediate personnel, thus contributing to the country's economic development. It is therefore proposed that the curriculum be revised with a view to eliminate the disadvantages faced by students. While some parents opt for Anatolian Imam Hatip high schools in order to provide their children with religious education, these schools are not aligned with the future professional aspirations of their pupils. It is thought that elucidating the distinction between the notion of being a devout individual and the professional expectations of the future workforce would prove more beneficial, particularly in terms of the long-term prospects of the younger generation. It has been established that socioeconomic status exerts an influence on performance; However, these deficiencies are eradicated in students who receive metacognitive strategy training.

Kaynakça

  • Amiama-Espaillat, C., & Mayor-Ruiz, C. (2017). Lectura digital en la competencia lectora: La influencia en la Generación Z de la República Dominicana. Comunicar. Media Education Research Journal, 15(52), 105–113. https://doi.org/https://doi.org/10.3916/C52-2017-10
  • Anand, N., Sehgal, R., Anand, S., & Kaushik, A. (2021). Feature selection on educational data using Boruta algorithm. International Journal of Computational Intelligence Studies, 10(1). https://doi.org/10.1504/IJCISTUDIES.2021.113826
  • Antzaka, A., Lallier, M., Meyer, S., Diard, J., Carreiras, M., & Valdois, S. (2017). Enhancing reading performance through action video games: the role of visual attention span. Scientific Reports, 7(1), 14563. https://doi.org/10.1038/s41598-017-15119-9
  • Areepattamannil, S., & Santos, I. M. (2019). Adolescent students’ perceived information and communication technology (ICT) competence and autonomy: Examining links to dispositions toward science in 42 countries. Computers in Human Behavior, 98, 50–58. https://doi.org/10.1016/j.chb.2019.04.005
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 117–194. https://doi.org/10.1016/j.compedu.2017.05.007
  • Asiloğulları, A. (2020). The Evaluation of The Relationship Between High School Student’s Life-Long Learning Tendencies and Questioning The Meaning and The Purpose of The Life Behaviour [Bartın University]. https://acikerisim.bartin.edu.tr/bitstream/handle/11772/6474/Azize Asioğulları .pdf?sequence=1&isAllowed=y
  • Aslam, N. M., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An Improved Early Student’s Academic Performance Prediction Using Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), 108. https://doi.org/10.3991/ijet.v16i12.20699
  • Atli, A., & Gür, S. H. (2019). High Schools Students’ Career Choices and Factors Affecting Their Choices. Turkish Psychological Counseling and Guidance Association, 2(1), 32–53. https://dergipark.org.tr/tr/download/article-file/750893
  • Avvisati, F. (2020). The measure of socio-economic status in PISA: a review and some suggested improvements. Large-scale Assessments in Education, 8(1). https://doi.org/10.1186/s40536-020-00086-x
  • Bölükbaş, S., & Gür, B. S. (2020). Tracking and inequality: The results from Turkey. International Journal of Educational Development, 78, 102262. https://doi.org/10.1016/j.ijedudev.2020.102262 Çağlayan, E. (2021). Analysis of Studies Conducted on Education for Disadvantaged Groups and Romani Citizens in Turkey. Journal of Roma Language and Culture Research Institute, 2(1), 1–15. https://dergipark.org.tr/en/pub/raedergisi/issue/62748/905175
  • Çelik, K., & Yurdakul, A. (2020). Investigation of PISA 2015 Reading Ability Achievement of Turkish Students in Terms of Student and School Level Variables. International Journal of Assessment Tools in Education, 7(1), 30–42. https://doi.org/10.21449/ijate.589280
  • CGOIK (2018). On Birinci Kalkınma Planı (2019-2023) Çocuk Çalışma Grubu Raporu. https://www.sbb.gov.tr/wp-content/uploads/2020/04/Cocuk_ve_GenclikOzelIhtisasKomisyonuCocukCalismaGrubuRaporu.pdf
  • Chmielewski, A. K., Dumont, H., & Trautwein, U. (2013). Tracking Effects Depend on Tracking Type. American Educational Research Journal, 50(5), 925–957. https://doi.org/10.3102/0002831213489843 Choi, S., & Lee, S. W. (2020). Enhancing Teacher Self-Efficacy in Multicultural Classrooms and School Climate: The Role of Professional Development in Multicultural Education in the United States and South Korea. AERA Open, 6(4), 233285842097357. https://doi.org/10.1177/2332858420973574
  • Chung, H., Park, S., Kim, J.-I., & Kim, A. (2021). Exploring Variables Affecting Adolescents’ Reading Literacy and Life Satisfaction: PISA 2018 International Comparison of Korea and Finland. Journal of Curriculum and Evaluation, 24(1). https://doi.org/10.29221/jce.2021.24.1.123
  • Çolakoğlu, M. H. (2018). Teachers’ Views and Recommendations About PISA 2015 Results. Journal of Research in Informal Environments, 3(1), 46–66. https://dergipark.org.tr/en/pub/jrinen/issue/39907/373468
  • Çoşkun, K. (2020). Piety and Social Values: An Empiric Study on Imam Hatip High School Students (The Sample of Ankara). Turkish Journal of Religious Studies, 20(1), 213–240. http://marife.org/tr/download/article-file/1094993
  • Dalane, K., & Marcotte, D. E. (2022). The Segregation of Students by Income in Public Schools. Educational Researcher, 51(4), 245–254. https://doi.org/10.3102/0013189X221081853
  • Demir, M. F., & Baloğlu, N. (2020). Relationship Between Metacognition Skills and Academic Procrastination Behaviors of the High School Students. Ahi Evran University Institute of Social Sciences Journal Institute, 6(1), 242–259. https://doi.org/10.31592/aeusbed.640030
  • Depren, S. K., & Depren, Ö. (2021). Cross-Cultural Comparisons of the Factors Influencing the High Reading Achievement in Turkey and China: Evidence from PISA 2018. The Asia-Pacific Education Researcher. https://doi.org/doi.org/10.1007/s40299-021-00584-8
  • Dong, X., & Hu, J. (2019). An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • Dubbeld, A., de Hoog, N., den Brok, P., & de Laat, M. (2019). Teachers’ multicultural attitudes and perceptions of school policy and school climate in relation to burnout. Intercultural Education, 30(6), 599–617. https://doi.org/10.1080/14675986.2018.1538042
  • Emdadi, A., & Eslahchi, C. (2021). Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model. BMC Bioinformatics, 22(1), 22–33. https://doi.org/10.1186/s12859-021-03974-3
  • Erdoğan, E., & Acar Güvendir, M. (2019). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 20, 1–31. https://dergipark.org.tr/tr/download/article-file/686572
  • ERG, (2019). PISA 2018 ne diyor? ERG. https://www.egitimreformugirisimi.org/pisa-2018-ne-diyor/
  • Ertem, H. Y. (2021). Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables. Participatory Educational Research, 8(1), 248–264. https://doi.org/10.17275/per.21.14.8.1
  • Fetler, M. E. (1991). Pitfalls of Using SAT Results to Compare Schools. American Educational Research Journal, 28(2), 481–491. https://doi.org/10.3102/00028312028002481
  • Fırat, T., & Koyuncu, İ. (2020). Investigating Reading Literacy in PISA 2018 Assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Florence, F. O., Adesola, O. A., Hameed, B. A., & Adewumi, O. M. (2017). A Survey on the Reading Habits among Colleges of Education Students in the Information Age. Journal of Education and Practice, 8(8), 106–110.
  • Gamazo, A., & Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167
  • Gubbels, J., Swart, N. M., & Groen, M. A. (2020). Everything in moderation: ICT and reading performance of Dutch 15-year-olds. Large-scale Assessments in Education, 8(1), 1. https://doi.org/10.1186/s40536-020-0079-0
  • Güloğlu, F., & Özay Köse, E. (2020). The Analysis of Multiple Intelligence Types of Social Sciences and Science High School Students in Terms of Different Variables. İInonu University Journal of the Graduate School of Education, 7(13), 1–17. https://doi.org/10.29129/inujgse.570417
  • Guyon, I., & Elisseef, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseef, A. (2006). An Introduction to Feature Extraction. Içinde I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer.
  • Hu, J., & Yu, R. (2021). The effects of ICT-based social media on adolescents’ digital reading performance: A longitudinal study of PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Computers & Education, 175, 104342. https://doi.org/10.1016/j.compedu.2021.104342
  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021
  • Kamar, K. Y. (2020). Relationship between Reading Habits and Students’ Academic Performances of Secondary Schools in Sokoto State, Nigeria. International Journal of Research and Innovation in Social Science, 4(2), 242–245.
  • Karatay, H., Külah, E., & Kaya, S. (2020). Methods, techniques and models for developing reading habit. Research in Reading and Writing Instruction, 8(1), 89–107. https://doi.org/10.35233/oyea.707967
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). Determining Variables That Predict Reading Comprehension Success by Data Mining in PISA 2018. Manisa Celal Bayar University Journal of Social Sciences, 19(4), 241–258. https://doi.org/10.18026/cbayarsos.959609
  • Kaunang, F. J., & Rotikan, R. (2018, Ekim). Students’ Academic Performance Prediction using Data Mining. 2018 Third International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/IAC.2018.8780547
  • Koğar, E. Y. (2021). An Investigation of the Mediating Role of Various Variables in the Effect of Both Gender and Economic, Social and Cultural Status on Reading Literacy. International Journal of Progressive Education, 17(1), 376–391. https://doi.org/10.29329/ijpe.2021.329.24
  • Kong, Y., Seo, Y. S., & Zhai, L. (2022). ICT and Digital Reading Achievement: A Cross-national Comparison using PISA 2018 Data. International Journal of Educational Research, 111, 101912. https://doi.org/10.1016/j.ijer.2021.101912
  • Kösterelioğlu, İ., Çelen, Ü., Kösterelioğlu, M. A., & Ahıska, R. (2019). Success purpose tendencies of high school students. Journal of Human Sciences, 16(2), 662–678. https://doi.org/10.14687/jhs.v16i2.5603
  • Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11). https://doi.org/10.18637/jss.v036.i11
  • Lan, Y.-C., Lo, Y.-L., & Hsu, Y.-S. (2014). The Effects of Meta-Cognitive Instruction on Students’ Reading Comprehension in Computerized Reading Contexts: A Quantitative Meta-Analysis. Educational Technology & Society, 17(4), 186–202.
  • Li, Z., & Qiu, Z. (2018). How does family background affect children’s educational achievement? Evidence from Contemporary China. The Journal of Chinese Sociology, 5(1). https://doi.org/10.1186/s40711-018-0083-8
  • Liu, H., Cao, H., Song, E., Ma, G., Xu, X., Jin, R., Jin, Y., & Hung, C. C. (2019). A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Physica Medica, 63(December 2018), 112–121. https://doi.org/10.1016/j.ejmp.2019.06.003
  • Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68. https://doi.org/10.1016/j.stueduc.2021.100982
  • Mani, K., & Kalpana, P. (2017). An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge. International Journal of Information Technology and Computer Science, 9(7), 61–68. https://doi.org/10.5815/ijitcs.2017.07.07
  • Marks, G. N., & O’Connell, M. (2021). Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Review of Education, 9(3). https://doi.org/10.1002/rev3.3293
  • Marôco, J. (2021). What makes a good reader? Worldwide insights from PIRLS 2016. Reading and Writing, 34(1), 231–272. https://doi.org/10.1007/s11145-020-10068-8
  • MEB. (2015). Introduction of Schools Affiliated to the General Directorate of Secondary Education. http://ogm.meb.gov.tr/meb_iys_dosyalar/2015_05/07092423_ogmokultanitim.pdf
  • MEB. (2018a). Mutlu Çocuklar Güçlü Türkiye 2023 Eğitim Vizyonu. https://2023vizyonu.meb.gov.tr/doc/2023_EGITIM_VIZYONU.pdf
  • MEB. (2018b). Secondary Education Institutions Weekly Course Schedule. https://ttkb.meb.gov.tr/meb_iys_dosyalar/2018_02/21173451_ort_ogrtm_hdc_2018.pdf
  • Mehmood, A., On, B.-W., Lee, I., & Choi, G. (2017). Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning. Symmetry, 9(1), 11. https://doi.org/10.3390/sym9010011
  • MERAM (2017). Types of Secondary Education Institutions. https://meramram.meb.k12.tr/meb_iys_dosyalar/42/26/175064/dosyalar/2017_02/15104831_lsetrler.pdf
  • Mushtaq, S., Soroya, S. H., & Mahmood, K. (2020). Reading habits of generation Z students in Pakistan: Is it time to re-examine school library services? Information Development, 026666692096564. https://doi.org/10.1177/0266666920965642
  • Navarro-Martinez, O., & Peña-Acuña, B. (2022). Technology Usage and Academic Performance in the Pisa 2018 Report. Journal of New Approaches in Educational Research, 11(1), 130. https://doi.org/10.7821/naer.2022.1.735
  • OECD. (2016). PISA 2018 Draft Analytical Frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2019a). PISA 2018 Assessment and Analytical Framework. Içinde OECD iLibrary. OECD.
  • OECD. (2019b). PISA Database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2021a). Country Note for Germany: 21st Readers. Içinde 21st-century readers: Developing literacy skills in a digital world. https://www.oecd.org/pisa/PISA2018_Reading_GERMANY.pdf
  • OECD. (2021b). 21st-Century Readers. OECD. https://doi.org/10.1787/a83d84cb-en
  • Oriogu, C. D., Subair, R. E., Oriogu-Ogbuiyi, D. C., & Ogbuiyi, S. U. (2017). Effect of Reading Habits on the Academic Performance of Students: A Case Study of the Students of Afe Babalola University, Ado-Ekiti, Ekiti State. Teacher Education and Curriculum Studies, 2(5), 74. https://doi.org/10.11648/j.tecs.20170205.13
  • Özbey Demir, Ö. (2020). What Do PISA Results Say About Education Inequality in Turkey? Critical Reviews in Educational Sciences, 1(2), 85–98. https://doi.org/10.22596/cresjournal.0102.85.98
  • Özdemir, Ş., & Karateke, T. (2018). Students’ Reasons for Preferring Imam Preachers Schools (The Sample Of Elazığ). Ondokuz Mayıs University Review of the Faculty of Divinity, 45, 5–33. https://dergipark.org.tr/en/download/article-file/604133
  • Özdemir, S., & Şerbetçi, H. N. (2018). Elementary School Fourth Graders’ Attitudes toward Reading (Bartin Sample). Elementary Education Online, 17(4), 2110–2123. https://doi.org/i 10.17051/ilkonline.2019.506973
  • Özer, M. (2020a). The Paradigm Shift in Vocational Education and Training in Turkey. Gazi Eğitim Fakültesi Dergisi, 40(2), 357–384. https://dergipark.org.tr/tr/download/article-file/1255073
  • Özer, M. (2020b). What Does PISA Tell Us About Performance of Education Systems? Bartın University Journal of Faculty of Education, 9(2), 217–228. https://doi.org/10.14686/buefad.697153
  • Özer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education. Palgrave Communications, 6(1), 34. https://doi.org/10.1057/s41599-020-0409-4
  • Öztürk, Z., & Göksoy, S. (2022). Vocational and Technical Anatolian High School Students’ Opinions for School Alienation and Their Attitudes for Vocational Education. Milli Eğitim Dergisi, 51(234), 1357–1380. https://doi.org/10.37669 milliegitim.852826
  • Pejic, A., & Molcer, P. S. (2019). Predicting the Outcome of a PISA Problem Solving Task Using Strategic Behavior Data. 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 313–318. https://doi.org/10.1109/CogInfoCom47531.2019.9089942
  • Qu, J., Ren, K., & Shi, X. (2021). Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting. Water Resources Management, 35(3), 1029–1045. https://doi.org/10.1007/s11269-021-02770-1
  • Reyhanlıoğlu, Ç., & Tiryaki, İ. (2021). An Overview of the Assessment and Evaluation Practices Carried Out In Turkey. Uluslararası Türk Eğitim Bilimleri Dergisi, 9(16), 70–93. https://doi.org/10.46778/goputeb.766689
  • Schiepe-Tiska, A. (2019). School Tracks as Differential Learning Environments Moderate the Relationship Between Teaching Quality and Multidimensional Learning Goals in Mathematics. Frontiers in Education, 4, 1–13. https://doi.org/10.3389/feduc.2019.00004
  • Sciffer, M. G., Perry, L. B., & McConney, A. (2022). Does school socioeconomic composition matter more in some countries than others, and if so, why? Comparative Education, 58(1), 37–51. https://doi.org/10.1080/03050068.2021.2013045
  • Severa, M., & Ceylan, E. (2021). What is school for? Understanding Structural Inequalities through the Experiences of High School Students. Başkent University Journal of Education, 8(1), 196–206.
  • Sevilla, M. P., & Polesel, J. (2022). Vocational education and social inequalities in within- and between-school curriculum tracking. Compare: A Journal of Comparative and International Education, 52(4), 581–599. https://doi.org/10.1080/03057925.2020.1798214
  • Shrestha, S., & Pokharel, M. (2021). Educational data mining in moodle data. International Journal of Informatics and Communication Technology (IJ-ICT), 10(1), 9. https://doi.org/10.11591/ijict.v10i1.pp9-18
  • Sikora, J., & Pokropek, A. (2006). Gendered Career Expectations of Students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random Forest Analysis of Factors Influencing the Students’ Reading Literacy Levels: Using PISA 2018 Korea Data. Asian Journal of Education, 21(1–4), 191–215. https://doi.org/10.15753/aje.2020.03.21.1.191
  • Srijamdee, K., & Pholphirul, P. (2020). Does ICT familiarity always help promote educational outcomes? Empirical evidence from PISA-Thailand. Education and Information Technologies, 25(4), 2933–2970. https://doi.org/10.1007/s10639-019-10089-z
  • Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4
  • Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic Status and School Type as Predictors of Academic Achievement. Journal of Economy Culture and Society, 61(1), 41–64. https://doi.org/10.26650/jecs2020-0034
  • Tat, O., Koyuncu, İ., & Gelbal, S. (2019). The Influence of Using Plausible Values and Survey Weights on Multiple Regression and Hierarchical Linear Model Parameters. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 235–248. https://doi.org/10.21031/epod.486999
  • TEDMEM. (2020). 2019 Eğitim Değerlendirme Raporu (Emin Karip (ed.)). TEDMEM. https://tedmem.org/storage/publications/February2023/f2JxDgyafag6BquTgyYr.pdf
  • Tse, S. K., Xiao, X., & Lam, W. (2013). The influences of gender, reading ability, independent reading, and context on reading attitude. Written Language & Literacy, 16(2), 241–271. https://doi.org/10.1075/wll.16.2.05tse
  • Uğuz, E., Şahin, S., & Yılmaz, R. (2021). The Use of Educational Data Mining in the Evaluation of PISA 2018 Scores of Science. Journal of Information and Communication Technologies. https://doi.org/10.53694/bited.887425
  • 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
  • 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
  • Yazıcı, T., & Kartal, O. Y. (2020). Investigation of High School Students’ Approaches to Learning. Nevşehir Hacı Bektaş Veli University Journal of ISS, 10(2), 625–641. https://doi.org/10.30783/nevsosbilen.783211
  • Yonca, Z. D. (2018). Factors Affecting Finland’s Success in PISA and Comparison with Turkey. Uluslararası Eğitim Bilimleri Dergisi, 14(5), 136–146. https://dergipark.org.tr/tr/download/article-file/563495
Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Türkçe ve Sosyal Bilimler Eğitimi (Diğer), Eğitim Sosyolojisi
Bölüm Araştırma Makalesi
Yazarlar

Sanem Şehribanoğlu 0000-0002-3099-7599

Gönderilme Tarihi 6 Eylül 2024
Kabul Tarihi 18 Kasım 2025
Erken Görünüm Tarihi 16 Aralık 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 14 Sayı: 5

Kaynak Göster

APA Şehribanoğlu, S. (2025). Differentiation in School Types in Turkey Evaluation of PISA 2018 Based on Reading Skill Levels. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 14(5), 2058-2086. https://doi.org/10.15869/itobiad.1544751
İnsan ve Toplum Bilimleri Araştırmaları Dergisi  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır. 

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