Araştırma Makalesi
BibTex RIS Kaynak Göster

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

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

Öz

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

Kaynakça

  • Abbasoğlu, B. (2020). Ortaokul öğrencilerinin akademik başarılarının eğitsel veri madenciliği yöntemleri ile tahmini. Veri Bilimi, 3(1), 1–10. https://dergipark.org.tr/tr/download/article-file/1198399
  • Adeyokun, B. O., Adeyanju, E. O., & Onyenania, G. O. (2020). Influence of entertainment media, cognitive styles and demographic variables on students’ reading habits in yaba college of technology secondary school, Yaba, Lagos. Information Impact: Journal of Information and Knowledge Management, 10(2). https://doi.org/10.4314/iijikm.v10i2.3
  • Ahmed, A. A. M., Deo, R. C., Ghahramani, A., Raj, N., Feng, Q., Yin, Z., & Yang, L. (2021). LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stochastic Environmental Research and Risk Assessment, 35(9). https://doi.org/10.1007/s00477-021-01969-3
  • Akkoyunlu, B., & Kurbanoğlu, S. (2003). Öğretmen Adaylarının bilgi okuryazarlığıv bilgisayar öz-yeterlik algıları üzerine bir çalışma. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 24(24), 1–10. https://dergipark.org.tr/tr/pub/hunefd/issue/7812/102529
  • Aksu, G., & Güzeller, C. O. (2016). Classification of PISA 2012 mathematical literacy scores using decision-tree method: turkey sampling. TED Eğitim ve Bilim, 41(185). https://doi.org/10.15390/EB.2016.4766
  • 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
  • Anastasiou, D., Sideridis, G. D., & Keller, C. E. (2020). The relationships of Socioeconomic factors and special education with reading outcomes across PISA countries. Exceptionality, 28(4), 279–293. https://doi.org/10.1080/09362835.2018.1531759
  • Arıkan, S., Özer, F., Şeker, V., & Ertaş, G. (2020). The Importance of sample weights and plausible values in large-scale assessments. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 522–539. https://doi.org/10.21031/epod.602765
  • Arslan, A. (2020). Ortaokul öğrencilerinin matematiksel bilişüstü farkındalıklarının çeşitli değişkenler açısından belirlenmesi. Turkish Journal of Educational Studies, 7(2), 150–169. https://dergipark.org.tr/tr/download/article-file/1108983
  • 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
  • Bana, A. (2020). Students’ Perception of using the ınternet to develop reading habits. Journal of English Teaching, 6(1), 60–70. https://doi.org/10.33541/jet.v6i1.46
  • Bezek Güre, Ö., Kayri, M., & Erdoğan, F. (2020). Analysis of factors effecting pısa 2015 mathematics literacy via educational data mining. TED Eğitim ve Bilim, 45(202), 393–415. https://doi.org/10.15390/EB.2020.8477
  • Breiman, L. (2001). Random forests. Machine Learning volume, 45, 5–32. https://doi.org/10.1023/A:1010933404324
  • Büyükkıdık, S., Bakırarar, B., & Bulut, O. (2018). Comparing the performance of data mining methods in classifying successful students with scientific literacy in PISA 2015. The 6th International Congress on Measurement and Evaluation in Education and Psychology, 68–75. https://doi.org/10.7939/R3KW5812Q
  • 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
  • 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
  • Çoban, Ö. (2020). Relationships between students’ socioeconomic status, parental support, students’ hindering, teachers’ hindering and students’ literacy scores: PISA 2018. World Journal of Education, 10(4), 45–59. https://doi.org/10.5430/wje.v10n4p45
  • Cobb, D., & Couch, D. (2021). Locating inclusion within the OECD’s assessment of global competence: An inclusive future through PISA 2018? Policy Futures in Education. https://doi.org/10.1177/14782103211006636
  • Çocuk Vakfı. (2006). Türkiye’nin okuma alışkanlığı karnesi. https://cocukvakfi.org.tr/wp-content/dosya/raporlar/13_okuma_aliskanligi_karnesi2006.pdf
  • Consulting, K., & Trust, N. L. (2013). Youth literacy and employability commission: the report of the all-party parliamentary literacy group. https://cdn.literacytrust.org.uk/media/documents/2013_01_01_free_other_-_Youth_Literacy_and_Employability_Commission_final_report.pdf
  • Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4). https://doi.org/10.1016/j.dss.2010.06.003
  • 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
  • Ding, H., & Homer, M. (2020). Interpreting mathematics performance in PISA: Taking account of reading performance. International Journal of Educational Research, 102. https://doi.org/10.1016/j.ijer.2020.101566
  • Dong, X., & Hu, J. (2019). An Exploration of ımpact factors ınfluencing students’ reading literacy in singapore with machine learning approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • 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), 33. https://doi.org/10.1186/s12859-021-03974-3
  • Engel, L. C., Rutkowski, D., & Thompson, G. (2019). Toward an international measure of global competence? A critical look at the PISA 2018 framework. Globalisation, Societies and Education, 17(2). https://doi.org/10.1080/14767724.2019.1642183
  • 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
  • Fabunmi, F., & Folorunso, O. (2010). Poor reading culture: A barrier to students’ patronage of libraries selected secondary school in ado local government area of Ekiti-State, Nigeria. African Research Review, 4(2). https://doi.org/10.4314/afrrev.v4i2.58357
  • Fırat, T., & Koyuncu, İ. (2020). Investigating reading literacy in pısa 2018 assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Flores-Mendoza, C., Ardila, R., Gallegos, M., & Reategui-Colareta, N. (2021). General ıntelligence and socioeconomic status as strong predictors of student performance in latin american schools: evidence from PISAitems. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.632289
  • Gajwani, J., & Chakraborty, P. (2021). Students’ performance prediction using feature selection and supervised machine learning algorithms. Kacprzyk, J., & P. Warsaw (Ed.), International Conference on Innovative Computing and Communications (ss. 37–354). Springer Nature Singapore. https://doi.org/10.1007/978-981-15-5113-0_25
  • 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
  • Geske, A., & Ozola, A. (2008). Factors ınfluencing reading literacy at the primary school level. Problems of Education in the 21st Century, 6(71–77). http://www.scientiasocialis.lt/pec/node/files/pdf/Geske.pdf
  • 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
  • Guyon, I., & Elisseef, A. (2003). An ıntroduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseeff, A. (2006). An ıntroduction to feature extraction. I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_1
  • Han, H., Jahed Armaghani, D., Tarinejad, R., Zhou, J., & Tahir, M. M. (2020). Random Forest and Bayesian Network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites. Natural Resources Research, 29(2), 655–667. https://doi.org/10.1007/s11053-019-09611-4
  • Han, Z., He, Q., & von Davier, M. (2019). Predictive feature generation and selection using process data from PISA Interactive problem-solving items: An application of Random Forests. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02461
  • Henry, L. A. (2006). Searching for an answer: The critical role of new literacies while reading on the internet. The Reading Teacher, 59(7), 614–627. https://doi.org/10.1598/RT.59.7.1
  • Hootsuite. (2021). We are social 2021 Türkiye raporu. https://datareportal.com/reports/digital-2021-turkey
  • Hu, J., Dong, X., & Peng, Y. (2021). Discovery of the key contextual factors relevant to the reading performance of elementary school students from 61 countries/regions: insight from a machine learning-based approach. Reading and Writing. https://doi.org/10.1007/s11145-021-10176-z
  • 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
  • Iman, A. N., & Ahmad, T. (2020, Şubat). Improving ıntrusion detection system by estimating parameters of Random Forest in Boruta. 2020 International Conference on Smart Technology and Applications (ICoSTA). https://doi.org/10.1109/ICoSTA48221.2020.1570609975
  • Jalota, C., & Agrawal, R. (2019). Analysis of educational data mining using classification. Machine Learning, Big Data, Cloud and Parallel Computing, 243–247. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8862214&tag=1
  • Jalota, C., & Agrawal, R. (2021). Feature Selection algorithms and student academic performance: A study. International Conference on Innovative Computing and Communications, 317–328. https://doi.org/10.1007/978-981-15-5113-0_23
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). PISA 2018’de Okuduğunu anlama başarısını yordayan değişkenlerin veri madenciliği ile belirlenmesi. 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
  • Keskin, H. K. (2014). Programme for ınternational student assessment (PISA) reading competencies: A study of the factors in academic reading. The Anthropologist, 18(1), 171–181. https://doi.org/10.1080/09720073.2014.11891533
  • Khorramdel, L., Pokropek, A., Joo, S.-H., Kirsch, I., ve Halderman, L. (2020). Examining gender DIF and gender differences in the PISA 2018 reading literacy scale: A partial invariance approach. Psychological Test and Assessment Modeling, 60(2), 179–231. https://www.researchgate.net/publication/342344680_Examining_gender_DIF_and_gender_differences_in_the_PISA_2018_reading_literacy_scale_A_partial_invariance_approach
  • 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
  • Koyuncu, İ., & Gelbal, S. (2020). Comparison of data mining classification algorithms on educational data under different conditions. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 11(4), 325–345. https://doi.org/10.21031/epod.696664
  • Kursa, M. B. (2020). Package ‘Boruta’. https://cran.r-project.org/web/packages/Boruta/Boruta.pdf
  • 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
  • Lahouar, A., & Slama, J. B. H. (2015). Random forests model for one day ahead load forecasting. IREC2015 The Sixth International Renewable Energy Congress, 1–6. https://doi.org/10.1109/IREC.2015.7110975
  • Le, T.-T.-H., Tran, T., Trinh, T.-P.-T., Nguyen, C.-T., Nguyen, T.-P.-T., Vuong, T.-T., Vu, T.-H., Bui, D.-Q., Vuong, H.-M., Hoang, P.-H., Nguyen, M.-H., Ho, M.-T., & Vuong, Q.-H. (2019). Reading habits, socioeconomic conditions, occupational aspiration and academic achievement in Vietnamese junior high school students. Sustainability, 11(18). https://doi.org/10.3390/su11185113
  • Lee, Y.-H., & Wu, J.-Y. (2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences, 22(3), 336–342. https://doi.org/10.1016/j.lindif.2012.01.007
  • Lee, Y. (2018). A study on development of collaborative problem solving prediction system based on deep learning: focusing on ICT factors. Journal of The Korean Association of Information Education, 22(1), 151–158. https://doi.org/10.14352/jkaie.2018.22.1.151
  • Lezhnina, O., & Kismihók, G. (2021). Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. International Journal of Research & Method in Education, 1–20. https://doi.org/10.1080/1743727X.2021.1963226
  • Liaw, A., ve Wiener, M. (2018). Package ‘randomForest’. https://cran.r-project.org/web/packages/randomForest/randomForest.pdf
  • Mahajan, G., ve Saini, B. (2020). Educational data mining: A state-of-the-art survey on tools and techniques used in EDM. International Journal of Computer Applications & Information Technology, 12(1), 310–316. https://ijcait.com/IJCAIT/121/IJCAIT1215GINIKA.pdf
  • Martínez-Abad, F., Gamazo, A., & Rodríguez-Conde, M.-J. (2020). Educational data mining: Identification of factors associated with school effectiveness in PISA assessment. Studies in Educational Evaluation, 66, 100875. https://doi.org/10.1016/j.stueduc.2020.100875
  • Mostafa, T. (2021). Do girls and boys engage with global and intercultural issues differently? https://doi.org/10.1787/9a52e7dd-en
  • Muñoz, I. A., Molina, E. C., Casas, E. E., ve Martín, E. L. (2018). ¿Cuánto oro hay entre la arena? Minería de datos con los resultados de España en PISA 2015. Revista Española de Pedagogía, 76(270), 225–246. https://www.jstor.org/stable/26547069
  • 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
  • Nadaf, A., Eliëns, S., & Miao, X. (2021). Interpretable-machine-learning evidence for ımportance and optimum of learning time. Int. J. Inf. Educ. Technol., Online First, 1–6. https://doi.org/10.18178/IJIET
  • Nzeyimana, G., & Bazimaziki, G. (2020). Revisiting the reading culture and information dissemination: Conceptualisation of “a reading nation is an informed nation”. International Journal of English Literature and Social Sciences, 5(3), 590–598. https://doi.org/10.22161/ijels.53.5
  • OECD. (2016). PISA 2018 Draft analytical frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2018). Preparing our youth for an ınclusive and sustainable world. https://www.oecd.org/pisa/Handbook-PISA-2018-Global-Competence.pdf
  • OECD. (2019a). PISA 2018 Assessment and analytical framework. OECD iLibrary. OECD.
  • OECD. (2019b). PISA database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2019c). PISA 2018 results (Volume III). PISA 2018 Results (Volume III) What School Life Means for Students’ Lives (Annex A1.). OECD Publishing. https://doi.org/10.1787/acd78851-en
  • OECD. (2020). Scaling procedures and construct validation of context questionnaire data. PISA 2018 Technical Report (s. 42). OECD Publishing. https://www.oecd.org/pisa/data/pisa2018technicalreport/PISA2018_Technical-Report-Chapter-16-Background-Questionnaires.pdf
  • 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
  • Özkan, U. B. (2020). Öğrencilerde eudaimonianın ve akademik başarının yordayıcısı olarak ekonomik, sosyal ve kültürel düzey. Yaşadıkça Eğitim, 34(2). https://doi.org/10.33308/26674874.2020342208
  • Özkan, U. B. (2021). Interest in Environmental ıssues as a determinant of science literacy: A multinational review with artificial neural network analysis. FIRE: Forum for International Research in Education, 7(1), 115–131. https://doi.org/10.32865/fire202171232
  • Park, J., & Ranasinghe, W. M. D. T. (2021). A study on exploring digital ınformation service method through analysis of PISA 2018 reading literacy assessment framework. Journal of the Korean Society for Library and Information Science, 55(1), 135–159. https://doi.org/10.4275/KSLIS.2021.55.1.135
  • 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
  • Pejic, A., & Stanic Molcer, P. (2018). Relationship mining in PISA CBA 2012 problem solving dataset using association rules. 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), 000549–000554. https://doi.org/10.1109/SACI.2018.8440942
  • Petko, D., Cantieni, A., & Prasse, D. (2017). Perceived quality of educational technology matters. Journal of Educational Computing Research, 54(8), 1070–1091. https://doi.org/10.1177/0735633116649373
  • Pont, B., & Werquin, P. (2001). How old are new skills? OECD Observer, 225, 15–17. https://www.oecd-ilibrary.org/docserver/observer-v2001-2-en.pdf?expires=1623701762&id=id&accname=guest&checksum=B8E4146276B70DF42D8B1CBB2B2A091C
  • Rojas-Torres, L., Ordóñez, G., & Calvo, K. (2021). Teacher and Student practices associated with performance in the PISA reading literacy evaluation. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.658973
  • Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1355
  • Sağlam, Z., Pekyürek, M. F., & Yilmaz, R. (2020). PISA 2018 araştırmasına etki eden duygusal faktörlerin veri madenciliği yöntemleri ile incelenmesi. Bilgi ve İletişim Teknolojileri Dergisi, 2(2), 113–148. https://dergipark.org.tr/tr/pub/bited/issue/58421/749242
  • Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational data mining: Student Performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C), 54–59. https://www.researchgate.net/publication/332369964_Educational_Data_Mining_Student_Performance_Prediction_in_Academic
  • Sikora, J., & Pokropek, A. (2006). Gendered career expectations of students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Sokkhey, P., Navy, S., Tong, L., & Takeo, O. (2020). Multi-models of educational data mining for predicting student performance in mathematics: a case study on high schools in Cambodia. IEIE Transactions on Smart Processing & Computing, 9(3), 217–229. https://doi.org/10.5573/IEIESPC.2020.9.3.217
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random forest analysis of factors ınfluencing 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
  • Suna, E. H., Tanberkan, H., & Özer, M. (2020). Changes in literacy of students in turkey by years and school types: Performance of students in PISA applications. Journal of Measurement and Evaluation in Education and Psychology, 11(1), 76–98. https://doi.org/10.21031/epod.702191
  • Suna, E., Tanberkan, H., Taş, E., Eroğlu, E., & Ümare, A. (2019). PISA 2018 Türkiye ön raporu. http://www.meb.gov.tr/meb_iys_dosyalar/2019_12/03105347_PISA_2018_Turkiye_On_Raporu.pdf
  • 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. (2019). Küresel yeterlik. https://tedmem.org/mem-notlari/degerlendirme/kuresel-yeterlik
  • Torppa, M., Eklund, K., Sulkunen, S., Niemi, P., & Ahonen, T. (2018). Why do boys and girls perform differently on PISA Reading in Finland? The effects of reading fluency, achievement behaviour, leisure reading and homework activity. Journal of Research in Reading, 41(1), 122–139. https://doi.org/10.1111/1467-9817.12103
  • UNESCO. (2013). Second global report on adult learning and education (GRALE 2). UNESCO Institute for Lifelong Learning. http://www.unesco.org/new/fileadmin/MULTIMEDIA/FIELD/Santiago/pdf/GRALE2-Literacy-Chapter.pdf
  • Unpingco, J. (2019). Machine learning. Python for Probability, Statistics, and Machine Learning (2. baskı, s. 384). Springer International Publishing. https://doi.org/10.1007/978-3-030-18545-9
  • Vázquez-Cano, E., De la Calle-Cabrera, A. M., Hervás-Gómez, C., & López-Meneses, E. (2020). Socio-family context and ıts ınfluence on students’ PISA reading performance scores: evidence from three countries in three continents. Educational Sciences: Theory & Practice, 20(2). https://doi.org/10.12738/jestp.2020.2.004
  • Vázquez-Cano, E., Gómez-Galán, J., Infante-Moro, A., & López-Meneses, E. (2020). Incidence of a non-sustainability use of technology on students’ reading performance in PISA. Sustainability, 12(2), 749. https://doi.org/10.3390/su12020749
  • Venkata, M. D., ve Lingamgunta, S. (2020). Breast cancer multi modality ımage analysis using pheneotype features by SVM. Journal of Science and Technology, 5(1), 52–60. http://jst.org.in/wp-content/uploads/2020/03/7.-Breast-Cancer-Multi-Modality-Image-Analysis-Using-Pheneotype-features-by-SVM.pdf
  • West, M., & Chew, H. E. (2014). Reading in the mobile era: a study of mobile reading in developing countries. R. Krau (Ed). UNESCO Institute for Lifelong Learning. https://unesdoc.unesco.org/ark:/48223/pf0000227436_eng
  • Xiao, Y., Liu, Y., & Hu, J. (2019). Regression analysis of ICT impact factors on early adolescents’ reading proficiency in five high-performing countries. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01646
  • Xu, X., Gu, H., Wang, Y., Wang, J., & Qin, P. (2019). Autoencoder based feature selection method for classification of anticancer drug response. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.00233
  • Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353–363. https://doi.org/10.1016/j.snb.2015.02.025
  • Yilmaz, A., Fer, S., Kelecioglu, H., Doğan, N., Yazıcı, N., Özyalçın Oskay, Ö., Yetkin Özdemin, İ. E., ve Batı, K. (2020). PISA ve Türkiye (2000 - 2018). http://www.egitim.hacettepe.edu.tr/belge/pisaveturkiye.pdf
  • Zaffar, M., Hashmani, M. A., & Savita, K. S. (2017, Kasım). Performance analysis of feature selection algorithm for educational data mining. 2017 IEEE Conference on Big Data and Analytics (ICBDA). https://doi.org/10.1109/ICBDAA.2017.8284099

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

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

Öz

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

Kaynakça

  • Abbasoğlu, B. (2020). Ortaokul öğrencilerinin akademik başarılarının eğitsel veri madenciliği yöntemleri ile tahmini. Veri Bilimi, 3(1), 1–10. https://dergipark.org.tr/tr/download/article-file/1198399
  • Adeyokun, B. O., Adeyanju, E. O., & Onyenania, G. O. (2020). Influence of entertainment media, cognitive styles and demographic variables on students’ reading habits in yaba college of technology secondary school, Yaba, Lagos. Information Impact: Journal of Information and Knowledge Management, 10(2). https://doi.org/10.4314/iijikm.v10i2.3
  • Ahmed, A. A. M., Deo, R. C., Ghahramani, A., Raj, N., Feng, Q., Yin, Z., & Yang, L. (2021). LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stochastic Environmental Research and Risk Assessment, 35(9). https://doi.org/10.1007/s00477-021-01969-3
  • Akkoyunlu, B., & Kurbanoğlu, S. (2003). Öğretmen Adaylarının bilgi okuryazarlığıv bilgisayar öz-yeterlik algıları üzerine bir çalışma. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 24(24), 1–10. https://dergipark.org.tr/tr/pub/hunefd/issue/7812/102529
  • Aksu, G., & Güzeller, C. O. (2016). Classification of PISA 2012 mathematical literacy scores using decision-tree method: turkey sampling. TED Eğitim ve Bilim, 41(185). https://doi.org/10.15390/EB.2016.4766
  • 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
  • Anastasiou, D., Sideridis, G. D., & Keller, C. E. (2020). The relationships of Socioeconomic factors and special education with reading outcomes across PISA countries. Exceptionality, 28(4), 279–293. https://doi.org/10.1080/09362835.2018.1531759
  • Arıkan, S., Özer, F., Şeker, V., & Ertaş, G. (2020). The Importance of sample weights and plausible values in large-scale assessments. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 522–539. https://doi.org/10.21031/epod.602765
  • Arslan, A. (2020). Ortaokul öğrencilerinin matematiksel bilişüstü farkındalıklarının çeşitli değişkenler açısından belirlenmesi. Turkish Journal of Educational Studies, 7(2), 150–169. https://dergipark.org.tr/tr/download/article-file/1108983
  • 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
  • Bana, A. (2020). Students’ Perception of using the ınternet to develop reading habits. Journal of English Teaching, 6(1), 60–70. https://doi.org/10.33541/jet.v6i1.46
  • Bezek Güre, Ö., Kayri, M., & Erdoğan, F. (2020). Analysis of factors effecting pısa 2015 mathematics literacy via educational data mining. TED Eğitim ve Bilim, 45(202), 393–415. https://doi.org/10.15390/EB.2020.8477
  • Breiman, L. (2001). Random forests. Machine Learning volume, 45, 5–32. https://doi.org/10.1023/A:1010933404324
  • Büyükkıdık, S., Bakırarar, B., & Bulut, O. (2018). Comparing the performance of data mining methods in classifying successful students with scientific literacy in PISA 2015. The 6th International Congress on Measurement and Evaluation in Education and Psychology, 68–75. https://doi.org/10.7939/R3KW5812Q
  • 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
  • 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
  • Çoban, Ö. (2020). Relationships between students’ socioeconomic status, parental support, students’ hindering, teachers’ hindering and students’ literacy scores: PISA 2018. World Journal of Education, 10(4), 45–59. https://doi.org/10.5430/wje.v10n4p45
  • Cobb, D., & Couch, D. (2021). Locating inclusion within the OECD’s assessment of global competence: An inclusive future through PISA 2018? Policy Futures in Education. https://doi.org/10.1177/14782103211006636
  • Çocuk Vakfı. (2006). Türkiye’nin okuma alışkanlığı karnesi. https://cocukvakfi.org.tr/wp-content/dosya/raporlar/13_okuma_aliskanligi_karnesi2006.pdf
  • Consulting, K., & Trust, N. L. (2013). Youth literacy and employability commission: the report of the all-party parliamentary literacy group. https://cdn.literacytrust.org.uk/media/documents/2013_01_01_free_other_-_Youth_Literacy_and_Employability_Commission_final_report.pdf
  • Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4). https://doi.org/10.1016/j.dss.2010.06.003
  • 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
  • Ding, H., & Homer, M. (2020). Interpreting mathematics performance in PISA: Taking account of reading performance. International Journal of Educational Research, 102. https://doi.org/10.1016/j.ijer.2020.101566
  • Dong, X., & Hu, J. (2019). An Exploration of ımpact factors ınfluencing students’ reading literacy in singapore with machine learning approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52
  • 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), 33. https://doi.org/10.1186/s12859-021-03974-3
  • Engel, L. C., Rutkowski, D., & Thompson, G. (2019). Toward an international measure of global competence? A critical look at the PISA 2018 framework. Globalisation, Societies and Education, 17(2). https://doi.org/10.1080/14767724.2019.1642183
  • 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
  • Fabunmi, F., & Folorunso, O. (2010). Poor reading culture: A barrier to students’ patronage of libraries selected secondary school in ado local government area of Ekiti-State, Nigeria. African Research Review, 4(2). https://doi.org/10.4314/afrrev.v4i2.58357
  • Fırat, T., & Koyuncu, İ. (2020). Investigating reading literacy in pısa 2018 assessment. lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
  • Flores-Mendoza, C., Ardila, R., Gallegos, M., & Reategui-Colareta, N. (2021). General ıntelligence and socioeconomic status as strong predictors of student performance in latin american schools: evidence from PISAitems. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.632289
  • Gajwani, J., & Chakraborty, P. (2021). Students’ performance prediction using feature selection and supervised machine learning algorithms. Kacprzyk, J., & P. Warsaw (Ed.), International Conference on Innovative Computing and Communications (ss. 37–354). Springer Nature Singapore. https://doi.org/10.1007/978-981-15-5113-0_25
  • 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
  • Geske, A., & Ozola, A. (2008). Factors ınfluencing reading literacy at the primary school level. Problems of Education in the 21st Century, 6(71–77). http://www.scientiasocialis.lt/pec/node/files/pdf/Geske.pdf
  • 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
  • Guyon, I., & Elisseef, A. (2003). An ıntroduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
  • Guyon, I., & Elisseeff, A. (2006). An ıntroduction to feature extraction. I. Guyon, S. Gunn, M. Nikravesh, & L. A. Zadeh (Ed.), Feature Extraction (ss. 1–25). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_1
  • Han, H., Jahed Armaghani, D., Tarinejad, R., Zhou, J., & Tahir, M. M. (2020). Random Forest and Bayesian Network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites. Natural Resources Research, 29(2), 655–667. https://doi.org/10.1007/s11053-019-09611-4
  • Han, Z., He, Q., & von Davier, M. (2019). Predictive feature generation and selection using process data from PISA Interactive problem-solving items: An application of Random Forests. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02461
  • Henry, L. A. (2006). Searching for an answer: The critical role of new literacies while reading on the internet. The Reading Teacher, 59(7), 614–627. https://doi.org/10.1598/RT.59.7.1
  • Hootsuite. (2021). We are social 2021 Türkiye raporu. https://datareportal.com/reports/digital-2021-turkey
  • Hu, J., Dong, X., & Peng, Y. (2021). Discovery of the key contextual factors relevant to the reading performance of elementary school students from 61 countries/regions: insight from a machine learning-based approach. Reading and Writing. https://doi.org/10.1007/s11145-021-10176-z
  • 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
  • Iman, A. N., & Ahmad, T. (2020, Şubat). Improving ıntrusion detection system by estimating parameters of Random Forest in Boruta. 2020 International Conference on Smart Technology and Applications (ICoSTA). https://doi.org/10.1109/ICoSTA48221.2020.1570609975
  • Jalota, C., & Agrawal, R. (2019). Analysis of educational data mining using classification. Machine Learning, Big Data, Cloud and Parallel Computing, 243–247. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8862214&tag=1
  • Jalota, C., & Agrawal, R. (2021). Feature Selection algorithms and student academic performance: A study. International Conference on Innovative Computing and Communications, 317–328. https://doi.org/10.1007/978-981-15-5113-0_23
  • Kasap, Y., Doğan, N., & Koçak, C. (2021). PISA 2018’de Okuduğunu anlama başarısını yordayan değişkenlerin veri madenciliği ile belirlenmesi. 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
  • Keskin, H. K. (2014). Programme for ınternational student assessment (PISA) reading competencies: A study of the factors in academic reading. The Anthropologist, 18(1), 171–181. https://doi.org/10.1080/09720073.2014.11891533
  • Khorramdel, L., Pokropek, A., Joo, S.-H., Kirsch, I., ve Halderman, L. (2020). Examining gender DIF and gender differences in the PISA 2018 reading literacy scale: A partial invariance approach. Psychological Test and Assessment Modeling, 60(2), 179–231. https://www.researchgate.net/publication/342344680_Examining_gender_DIF_and_gender_differences_in_the_PISA_2018_reading_literacy_scale_A_partial_invariance_approach
  • 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
  • Koyuncu, İ., & Gelbal, S. (2020). Comparison of data mining classification algorithms on educational data under different conditions. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 11(4), 325–345. https://doi.org/10.21031/epod.696664
  • Kursa, M. B. (2020). Package ‘Boruta’. https://cran.r-project.org/web/packages/Boruta/Boruta.pdf
  • 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
  • Lahouar, A., & Slama, J. B. H. (2015). Random forests model for one day ahead load forecasting. IREC2015 The Sixth International Renewable Energy Congress, 1–6. https://doi.org/10.1109/IREC.2015.7110975
  • Le, T.-T.-H., Tran, T., Trinh, T.-P.-T., Nguyen, C.-T., Nguyen, T.-P.-T., Vuong, T.-T., Vu, T.-H., Bui, D.-Q., Vuong, H.-M., Hoang, P.-H., Nguyen, M.-H., Ho, M.-T., & Vuong, Q.-H. (2019). Reading habits, socioeconomic conditions, occupational aspiration and academic achievement in Vietnamese junior high school students. Sustainability, 11(18). https://doi.org/10.3390/su11185113
  • Lee, Y.-H., & Wu, J.-Y. (2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences, 22(3), 336–342. https://doi.org/10.1016/j.lindif.2012.01.007
  • Lee, Y. (2018). A study on development of collaborative problem solving prediction system based on deep learning: focusing on ICT factors. Journal of The Korean Association of Information Education, 22(1), 151–158. https://doi.org/10.14352/jkaie.2018.22.1.151
  • Lezhnina, O., & Kismihók, G. (2021). Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. International Journal of Research & Method in Education, 1–20. https://doi.org/10.1080/1743727X.2021.1963226
  • Liaw, A., ve Wiener, M. (2018). Package ‘randomForest’. https://cran.r-project.org/web/packages/randomForest/randomForest.pdf
  • Mahajan, G., ve Saini, B. (2020). Educational data mining: A state-of-the-art survey on tools and techniques used in EDM. International Journal of Computer Applications & Information Technology, 12(1), 310–316. https://ijcait.com/IJCAIT/121/IJCAIT1215GINIKA.pdf
  • Martínez-Abad, F., Gamazo, A., & Rodríguez-Conde, M.-J. (2020). Educational data mining: Identification of factors associated with school effectiveness in PISA assessment. Studies in Educational Evaluation, 66, 100875. https://doi.org/10.1016/j.stueduc.2020.100875
  • Mostafa, T. (2021). Do girls and boys engage with global and intercultural issues differently? https://doi.org/10.1787/9a52e7dd-en
  • Muñoz, I. A., Molina, E. C., Casas, E. E., ve Martín, E. L. (2018). ¿Cuánto oro hay entre la arena? Minería de datos con los resultados de España en PISA 2015. Revista Española de Pedagogía, 76(270), 225–246. https://www.jstor.org/stable/26547069
  • 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
  • Nadaf, A., Eliëns, S., & Miao, X. (2021). Interpretable-machine-learning evidence for ımportance and optimum of learning time. Int. J. Inf. Educ. Technol., Online First, 1–6. https://doi.org/10.18178/IJIET
  • Nzeyimana, G., & Bazimaziki, G. (2020). Revisiting the reading culture and information dissemination: Conceptualisation of “a reading nation is an informed nation”. International Journal of English Literature and Social Sciences, 5(3), 590–598. https://doi.org/10.22161/ijels.53.5
  • OECD. (2016). PISA 2018 Draft analytical frameworks. https://www.oecd.org/pisa/pisaproducts/PISA-2018-draft-frameworks.pdf
  • OECD. (2018). Preparing our youth for an ınclusive and sustainable world. https://www.oecd.org/pisa/Handbook-PISA-2018-Global-Competence.pdf
  • OECD. (2019a). PISA 2018 Assessment and analytical framework. OECD iLibrary. OECD.
  • OECD. (2019b). PISA database. OECD. https://www.oecd.org/pisa/data/
  • OECD. (2019c). PISA 2018 results (Volume III). PISA 2018 Results (Volume III) What School Life Means for Students’ Lives (Annex A1.). OECD Publishing. https://doi.org/10.1787/acd78851-en
  • OECD. (2020). Scaling procedures and construct validation of context questionnaire data. PISA 2018 Technical Report (s. 42). OECD Publishing. https://www.oecd.org/pisa/data/pisa2018technicalreport/PISA2018_Technical-Report-Chapter-16-Background-Questionnaires.pdf
  • 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
  • Özkan, U. B. (2020). Öğrencilerde eudaimonianın ve akademik başarının yordayıcısı olarak ekonomik, sosyal ve kültürel düzey. Yaşadıkça Eğitim, 34(2). https://doi.org/10.33308/26674874.2020342208
  • Özkan, U. B. (2021). Interest in Environmental ıssues as a determinant of science literacy: A multinational review with artificial neural network analysis. FIRE: Forum for International Research in Education, 7(1), 115–131. https://doi.org/10.32865/fire202171232
  • Park, J., & Ranasinghe, W. M. D. T. (2021). A study on exploring digital ınformation service method through analysis of PISA 2018 reading literacy assessment framework. Journal of the Korean Society for Library and Information Science, 55(1), 135–159. https://doi.org/10.4275/KSLIS.2021.55.1.135
  • 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
  • Pejic, A., & Stanic Molcer, P. (2018). Relationship mining in PISA CBA 2012 problem solving dataset using association rules. 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), 000549–000554. https://doi.org/10.1109/SACI.2018.8440942
  • Petko, D., Cantieni, A., & Prasse, D. (2017). Perceived quality of educational technology matters. Journal of Educational Computing Research, 54(8), 1070–1091. https://doi.org/10.1177/0735633116649373
  • Pont, B., & Werquin, P. (2001). How old are new skills? OECD Observer, 225, 15–17. https://www.oecd-ilibrary.org/docserver/observer-v2001-2-en.pdf?expires=1623701762&id=id&accname=guest&checksum=B8E4146276B70DF42D8B1CBB2B2A091C
  • Rojas-Torres, L., Ordóñez, G., & Calvo, K. (2021). Teacher and Student practices associated with performance in the PISA reading literacy evaluation. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.658973
  • Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1355
  • Sağlam, Z., Pekyürek, M. F., & Yilmaz, R. (2020). PISA 2018 araştırmasına etki eden duygusal faktörlerin veri madenciliği yöntemleri ile incelenmesi. Bilgi ve İletişim Teknolojileri Dergisi, 2(2), 113–148. https://dergipark.org.tr/tr/pub/bited/issue/58421/749242
  • Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational data mining: Student Performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C), 54–59. https://www.researchgate.net/publication/332369964_Educational_Data_Mining_Student_Performance_Prediction_in_Academic
  • Sikora, J., & Pokropek, A. (2006). Gendered career expectations of students. https://doi.org/http://dx.doi.org/10.1787/5kghw6891gms-en
  • Sokkhey, P., Navy, S., Tong, L., & Takeo, O. (2020). Multi-models of educational data mining for predicting student performance in mathematics: a case study on high schools in Cambodia. IEIE Transactions on Smart Processing & Computing, 9(3), 217–229. https://doi.org/10.5573/IEIESPC.2020.9.3.217
  • Son, Y., Hyunjeong, P., & Park, M. (2020). Random forest analysis of factors ınfluencing 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
  • Suna, E. H., Tanberkan, H., & Özer, M. (2020). Changes in literacy of students in turkey by years and school types: Performance of students in PISA applications. Journal of Measurement and Evaluation in Education and Psychology, 11(1), 76–98. https://doi.org/10.21031/epod.702191
  • Suna, E., Tanberkan, H., Taş, E., Eroğlu, E., & Ümare, A. (2019). PISA 2018 Türkiye ön raporu. http://www.meb.gov.tr/meb_iys_dosyalar/2019_12/03105347_PISA_2018_Turkiye_On_Raporu.pdf
  • 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. (2019). Küresel yeterlik. https://tedmem.org/mem-notlari/degerlendirme/kuresel-yeterlik
  • Torppa, M., Eklund, K., Sulkunen, S., Niemi, P., & Ahonen, T. (2018). Why do boys and girls perform differently on PISA Reading in Finland? The effects of reading fluency, achievement behaviour, leisure reading and homework activity. Journal of Research in Reading, 41(1), 122–139. https://doi.org/10.1111/1467-9817.12103
  • UNESCO. (2013). Second global report on adult learning and education (GRALE 2). UNESCO Institute for Lifelong Learning. http://www.unesco.org/new/fileadmin/MULTIMEDIA/FIELD/Santiago/pdf/GRALE2-Literacy-Chapter.pdf
  • Unpingco, J. (2019). Machine learning. Python for Probability, Statistics, and Machine Learning (2. baskı, s. 384). Springer International Publishing. https://doi.org/10.1007/978-3-030-18545-9
  • Vázquez-Cano, E., De la Calle-Cabrera, A. M., Hervás-Gómez, C., & López-Meneses, E. (2020). Socio-family context and ıts ınfluence on students’ PISA reading performance scores: evidence from three countries in three continents. Educational Sciences: Theory & Practice, 20(2). https://doi.org/10.12738/jestp.2020.2.004
  • Vázquez-Cano, E., Gómez-Galán, J., Infante-Moro, A., & López-Meneses, E. (2020). Incidence of a non-sustainability use of technology on students’ reading performance in PISA. Sustainability, 12(2), 749. https://doi.org/10.3390/su12020749
  • Venkata, M. D., ve Lingamgunta, S. (2020). Breast cancer multi modality ımage analysis using pheneotype features by SVM. Journal of Science and Technology, 5(1), 52–60. http://jst.org.in/wp-content/uploads/2020/03/7.-Breast-Cancer-Multi-Modality-Image-Analysis-Using-Pheneotype-features-by-SVM.pdf
  • West, M., & Chew, H. E. (2014). Reading in the mobile era: a study of mobile reading in developing countries. R. Krau (Ed). UNESCO Institute for Lifelong Learning. https://unesdoc.unesco.org/ark:/48223/pf0000227436_eng
  • Xiao, Y., Liu, Y., & Hu, J. (2019). Regression analysis of ICT impact factors on early adolescents’ reading proficiency in five high-performing countries. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01646
  • Xu, X., Gu, H., Wang, Y., Wang, J., & Qin, P. (2019). Autoencoder based feature selection method for classification of anticancer drug response. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.00233
  • Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353–363. https://doi.org/10.1016/j.snb.2015.02.025
  • Yilmaz, A., Fer, S., Kelecioglu, H., Doğan, N., Yazıcı, N., Özyalçın Oskay, Ö., Yetkin Özdemin, İ. E., ve Batı, K. (2020). PISA ve Türkiye (2000 - 2018). http://www.egitim.hacettepe.edu.tr/belge/pisaveturkiye.pdf
  • Zaffar, M., Hashmani, M. A., & Savita, K. S. (2017, Kasım). Performance analysis of feature selection algorithm for educational data mining. 2017 IEEE Conference on Big Data and Analytics (ICBDA). https://doi.org/10.1109/ICBDAA.2017.8284099
Toplam 106 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Alan Eğitimleri
Bölüm Araştırma Makalesi
Yazarlar

Sanem Şehribanoğlu 0000-0002-3099-7599

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

Kaynak Göster

APA Şehribanoğlu, S. (2024). PISA 2018 Türkiye Örnekleminde Okuma Becerisini Etkileyen Değişkenlerin Boruta Algoritması ile Belirlenmesi. Ankara University Journal of Faculty of Educational Sciences (JFES), 57(2), 655-701. https://doi.org/10.30964/auebfd.1254457
Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi (AÜEBFD), Ankara Üniversitesi Yayınevi'nin kurumsal dergisidir. 

Creative Commons License AUEBFD'nin tüm İçerikleri Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License kuralları çerçevesinde lisanslanmaktadır.

AUEBFD CC BY-NC-ND 4.0 lisansını kullanmaktadır.