Examining Non-cognitive Factors Predicting Reading Achievement in Turkey: Evidence from PISA 2018

Year 2022, , 450 - 459, 20.09.2022

Pınar Karaman

https://doi.org/10.33200/ijcer.1026655

Cited By: 2

Abstract

The purpose of the study was to investigate how student and teacher-related non-cognitive variables were important factors on the reading performances of Turkish students in PISA 2018. The results of the HLM analysis revealed that economic, social and cultural status (ESCS) as backround variable was considered an effective predictor of student and school reading achievement. Meta-cognitive stratejies were the most influential variables among the students’ non-cognitive variables. Besides, most of the teacher-related non-cognitive factors had significant impacts on reading achievement even after controlling all student related and backround variables. Teachers’ instructional behaviours such as adaptive instruction and teacher-directed instruction have much more influence on reading performance than other teacher behaviors. The results suggeted that fostering soft skills are essential for both students and teachers.

Keywords

Reading achievement, Hierarchical linear models, Meta-cognitive strategies, Adaptive instruction, Teacher-directed instruction

References

• Aksu, G., & Guzeller, C. O. (2016). Classification of PISA 2012 mathematical literacy scores using Decision-Tree Method: Turkey sampling. Egitim Bilim, 41, 101–122. http://dx.doi.org/10.15390/EB.2016.4766
• Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students' learning strategies and motivation processes. Journal of Educational Psychology, 80(3), 260–267. https://doi.org/10.1037/0022-0663.80.3.260
• Bergman Nutley, S., & Söderqvist, S. (2017). How is working memory training likely to influence academic performance? Current evidence and methodological considerations. Frontiers in Psychology, 8, 69.
• Carrell, P. L., Gajdusek, L., & Wise, T. (1998). Metacognition and EFL/ESL reading. Instructional science, 26(1), 97-112.
• Chapman, J. W., Tunmer, W. E., & Prochnow, J. E. (2000). Early reading-related skills and performance, reading self-concept, and the development of academic self-concept: A longitudinal study. Journal of Educational Psychology, 92(4), 703–708. https://doi.org/10.1037/0022-0663.92.4.703
• Cunha, F., & Heckman, J. J. (2008). Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation. Journal of Human Resources, 43, 738–782.
• Çalışkanel, G. (2013). The Relationship between working memory, English (L2) and academic achievement in12-14 year-old Turkish students: the effect of age and gender (Master's thesis).
• 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, 1-11.
• Dincer, M. A. & Uysal, G. (2010). The determinants of student achievement in Turkey. International Journal of Educational Development, 30(6), 592-598.
• Ersan, O., & Rodriguez, M. C. (2020). Socioeconomic status and beyond: a multilevel analysis of TIMSS mathematics achievement given student and school context in Turkey. Large-scale Assessments in Education, 8(1), 1-32.
• Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., & Beechum, N. O. (2012). Teaching Adolescents to Become Learners: The Role of Noncognitive Factors in Shaping School Performance--A Critical Literature Review. Consortium on Chicago School Research., Chicago, IL.
• Furnham, A., Zhang, J., & Chamorro-Premuzic, T. (2006). The relationship between psychometric and self-estimated intelligence, creativity, personality and academic achievement. Imagination, Cognition and Personality, 25(2), 119–145. https://doi.org/10.2190/530V-3M9U-7UQ8-FMBG
• 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, 3365.
• Gabrieli, C., Ansel, D. & Krachman, S. B. (2015). Ready to be Counted: The Research Case for Education Policy Action on Non-cognitive Skills. Boston: Transforming Education.
• 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, 575167.
• Goetz, T., Frenzel, A. C., Hall, N. C., & Pekrun, R. (2008). Antecedents of academic emotions: Testing the internal/external frame of reference model for academic enjoyment. Contemporary Educational Psychology, 33(1), 9-33.
• Gutman, L. M., & Schoon, I. (2013). The impact of non-cognitive skills on outcomes for young people. London, England: Education Empowerment Foundation.
• Hannon, B. (2016). General and non-general intelligence factors simultaneously influence SAT, SAT-V, and SAT-M performance. Intelligence, 59, 51–63
• Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. http://dx.doi.org/10.4324/9780203887332.
• He, J., Barrera-Pedemonte, F., Buchholz, J. (2019). Cross-cultural comparability of noncognitive constructs in TIMSS and PISA. Assessment in Education: Principles, Policy & Practice, 26(4), 369-385.
• Heckman, J., J. Stixrud, S. Urzua (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24(3), 411-482, http://dx.doi.org/10.1086/504455.
• Khine, M. S., & Areepattamannil, S. (2016). Non-cognitive skills and factors in educational attainment. Cham: Sense Publishers. doi: 10.1007/978-94-6300- 591-3
• Klem, A. M., & Connell, J. P. (2004). Relationships matter: Linking teacher support to student engagement and achievement. Journal of school health, 74(7), 262-273.
• Klieme, E. (2016). TIMSS 2015 and PISA 2015: How are they related on the country level? DIPF Working Paper. Retriweved from https://www.dipf.de/de/forschung/publikationen/pdf-publikationen/Klieme_TIMSS2015andPISA2015.pdf
• Lee, J., & Shute, V. J. (2010). Personal and social-contextual factors in K–12 academic performance: An integrative perspective on student learning. Educational psychologist, 45(3), 185-202.
• Lingard, B., Martino, W., & Rezai-Rashti, G. (2013). Testing regimes, accountabilities and education policy: commensurate global and national developments. J. Educ. Policy 28, 539–556. doi: 10.1080/02680939.2013.820042
• Ma, L., Luo, H., & Xiao, L. (2021). Perceived teacher support, self-concept, enjoyment and achievement in reading: A multilevel mediation model based on PISA 2018. Learning and Individual Differences, 85, 101947.
• Malhotra, S. (2020). Psychometric Intelligence and Academic Achievement, A Comparative Analysis of Elementary Schools. EDUTEC: Journal of Education And Technology, 3(2), 83-95.
• Mullis, I. V. S. & Martin, M. O. (Eds.). (2013). TIMSS 2015 assessment frameworks. Boston College, TIMSS & PIRLS International Study Center.
• Organisation for Economic Co-operation and Development (2016a). Help manual for the IDB analyzer (SAS macros). Hamburg, Germany. Retrieved from http://www.iea.nl/data
• Organisation for Economic Co-operation and Development OECD. (2016b). PISA 2015 results (volume II): Policies and practices for successful schools, PISA. Paris: OECD Publishing. doi:10.1787/9789264267510-en
• Organisation for Economic Co-operation and Development OECD (2017). “Chapter 16 scaling procedures and construct validation of context questionnaire data” in PISA 2015 technical report, Paris: OECD.
• Organisation for Economic Co-operation and Development OECD (2019a). PISA 2018 Assessment and Analytical Framework. Paris: OECD Publishing. doi: 10.1787/b25efab8-en
• Organisation for Economic Co-operation and Development (OECD) (2019b). PISA 2018 results (Volume II): Where all students can succeed.
• Organisation for Economic Co-operation and Development OECD. (2019c). PISA 2018 Results (Volume III): What School Life Means for Students' Lives. OECD.
• Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18(4), 315-341.
• Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child development, 88(5), 1653-1670.
• Popham, W. J. (2000). Modern educational measurement: Practical guidelines for educational leaders. Pearson College Division.
• Qi, X. (2021). Effects of self-regulated learning on student’s reading literacy: Evidence from Shanghai. Frontiers in Psychology, 11, 3590.
• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). CA: Sage publications.
• Raudenbush, S. W., Bryk, A. S., Cheong, Y. F. & Congdon, R. (2019). HLM 8 for Windows. [Computer software]. Skokie, IL: Scientifc Software International, Inc.
• Raudenbush, S. W., & Sampson, R. (1999). Assessing direct and indirect effects in multilevel designs with latent variables. Sociological Methods & Research, 28(2), 123-153.
• Smits, J., & Gündüz Hoşgör, A. (2006). Effects of family background characteristics on educational participation in Turkey. International Journal of Educational Development, 26(5), 545–560
• Swanson, H. L., & Alloway, T. P. (2012). Working memory, learning, and academic achievement. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA educational psychology handbook, Vol. 1. Theories, constructs, and critical issues (pp. 327–366). American Psychological Association. https://doi.org/10.1037/13273-012
• Şen, H. Ş. (2009). The relationship between the use of metacognitive strategies and reading comprehension. Procedia-Social and Behavioral Sciences, 1(1), 2301-2305.
• Tabak, H. & Çalık, T. (2020). Evaluation of an educational reform in the context of equal opportunities in Turkey: Policy recommendations with evidence from PISA. International Journal of Contemporary Educational Research, 7(1), 321-334. DOI: https://doi.org/10.33200/ijcer.685893
• Tyler, R.W. (2000) A rationale for program evaluation. In D.L. Stufflebeam, G.F. Madaus, and T. Kelleghan (Eds.), Evaluation models: Viewpoints on educational and human service evaluation (2nd ed.). (pp. 87-96). Boston: Kluwer Academic Publishers
• Wanzer, D., Postlewaite, E., & Zargarpour, N. (2019). Relationships among noncognitive factors and academic performance: Testing the University of Chicago Consortium on School Research model. AERA Open, 5(4), 1-20
• Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary educational psychology, 25(1), 68-81.
• Wu, M. (2010). Comparing the similarities and differences of PISA 2003 and TIMSS (OECD Education Working Papers, No. 32). Paris: OECD Publishing
• Yıldırım, S. (2012). Teacher support, motivation, learning strategy use, and achievement: A multilevel mediation model. The Journal of Experimental Education, 80(2), 150-172.