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Mathematics low achievement in Greece: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data
ARTICLE

## Anastasios Karakolidis, Vasiliki Pitsia, Anastassios Emvalotis, University of Ioannina

Themes in Science and Technology Education Volume 9, Number 1, ISSN 1792-8788 Publisher: Themes in Science and Technology Education

## Abstract

The main aim of the present study was to carry out an in-depth examination of mathematics underperformance in Greece. By applying a binary multilevel model to the PISA 2012 data, this study investigated the factors which were linked to low achievement in mathematics. The multilevel analysis revealed that students’ gender, immigration status, self-constructs about mathematics, pre-primary education attendance as well as individual and school mean socioeconomic status (SES) were statistically significant predictors of student low achievement.It was also found that school accounted for a large proportion of the differences between low achievers and non-low achievers, with the final model explaining a great part of these differences. By successfully addressing the research questions, this study has demonstrated evidence that could help educators and policy makers to tackle the massive problem of mathematics underperformance not only in Greece, but also in other countries with similar educational systems.

## Citation

Karakolidis, A., Pitsia, V. & Emvalotis, A. (2016). Mathematics low achievement in Greece: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data. Themes in Science and Technology Education, 9(1), 3-24. Retrieved November 28, 2022 from https://www.learntechlib.org/p/173602/.

## References

View References & Citations Map- Anderson, J.O., Lin, H.-S., Treagust, D.F., Ross, S.P., & Yore, L.D. (2007). Using large-scale assessment datasets for research in science and mathematics education: Programme for International Student Assessment (PISA). International Journal of Science and Mathematics Education, 5(4), 591–614.
- Areepattamannil, S. (2014). International note: What factors are associated with reading, mathematics, and science literacy of Indian adolescents? A multilevel examination. Journal of Adolescence, 37(4), 367–372.
- Ashcraft, M.H., & Krause, J.A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243–248.
- Ashcraft, M.H., & Moore, A.M. (2009). Mathematics anxiety and the affective drop in performance. Journal of Psychoeducational Assessment, 27(3), 197–205.
- Ashcraft, M.H., & Ridley, K.S. (2005). Math anxiety and its cognitive consequences. In J.I.D. Campbell (Ed.), Handbook of Mathematical Cognition (pp. 315–327). New York, NY: Psychology Press.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.
- Beilock, S.L., Kulp, C.A., Holt, L.E., & Carr, T.H. (2004). More on the fragility of performance: choking under pressure in mathematical problem solving. Journal of Experimental Psychology, 133(4), 584-600.
- Byrnes, J.P., & Miller, D.C. (2007). The relative importance of predictors of math and science achievement: An opportunity-propensity analysis. Contemporary Educational Psychology, 32(4), 599–629.
- Chen, S.K., Yeh, Y.C., Hwang, F.M., & Lin, S.S.J. (2013). The relationship between academic self-concept and achievement: A multicohort-multioccasion study. Learning and Individual Differences, 23(1), 172–178.
- Chiu, M.M. (2010). Effects of inequality, family and school on mathematics achievement: Country and student differences. Social Forces, 88(4), 2645–1676.
- Chiu, M.M., Chow, B.W.Y., & Mcbride-Chang, C. (2007). Universals and specifics in learning strategies: Explaining adolescent mathematics, science, and reading achievement across 34 countries. Learning and Individual Differences, 17(4), 344–365.
- Chiu, M.M., & Klassen, R.M. (2010). Relations of mathematics self-concept and its calibration with mathematics achievement: Cultural differences among fifteen-year-olds in 34 countries. Learning and Instruction, 20(1), 2–17.
- Cohen, L., Manion, L., & Morrison, K. (2011). Research methods in education (7th ed.). London: Routledge. Council of the European Union (2010). Council conclusions on increasing the level of basic skills in the context of European cooperation on schools for the 21st century. Brussels.
- Demir, I., Kiliç, S., & Ünal, H. (2010). Effects of students’ and schools' characteristics on mathematics achievement: Findings from PISA 2006. Procedia-Social and Behavioral Sciences, 2(2), 3099–3103.
- Dimakos, I.C., & Tasiopoulou, K. (2003). Attitudes towards migrants: What do Greek students think about their immigrant classmates? Intercultural Education, 14(3), 307–316.
- Else-Quest, N.M., Hyde, J.S., & Linn, M.C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103–127.
- European Commission (2011). Mathematics education in Europe: Common challenges and national policies. Brussels: Education, Audiovisual and Cultural Executive Agency, Eurydice. European Commission (2013a). EU school report: Some improvement in science and reading, but poor in maths. Brussels.
- European Commission (2014). Study to prepare the commission report on policies for tackling low achievement in basic skills. Luxembourg: Publication Office of the European Union.
- Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). London: SAGE.
- Fryer, R.G., & Levitt, S.D. (2010). An empirical analysis of the gender gap in mathematics. American Economic Journal: Applied Economics, 2(2), 210–240.
- Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press.
- Gilleece, L., Cosgrove, J., & Sofroniou, N. (2010). Equity in mathematics and science outcomes: Characteristics associated with high and low achievement on PISA 2006 in Ireland. International Journal of Science and Mathematics Education, 8, 475–496.
- Glewwe, W.P., Hanushek, A.E., Humpage, D.S., & Ravina, R. (2011). School resources and educational outcomes in developing countries: A review of the literature from 1990 to 2010 (No. 17554). NBER Working Pares Series. Cambridge, MA. A. Karakolidis, V. Pitsia, A. Emvalotis
- Goforth, K., Noltemeyer, A., Patton, J., Bush, K.R., & Bergen, D. (2014). Understanding mathematics achievement: An analysis of the effects of student and family factors. Educational Studies, 40(2), 196–214.
- Guay, F., Marsh, W.H., & Boivin, M. (2003). Academic self-concept and academic achievement: Developmental perspectives on their causal ordering. Journal of Educational Psychology, 95(1), 124–136.
- Guo, G., & Zhao, H. (2000). Multilevel modeling for binary data. Annual Review of Sociology, 26, 441–462.
- Hambrick, A. (2009). Remembering the child: On equity and inclusion in mathematics and science classrooms. Critical issue. North Central Regional Educational Laboratory.
- Hampden-Thompson, G. (2013). Family policy, family structure, and children’s educational achievement. Social Science Research, 42(3), 804–17.
- Hanushek, A.E., & Woessmann, L. (2011). The economics of international differences in educational achievement. In A.E. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the Economics of Education (Vol. 3, pp. 89–
- Hojo, M., & Oshio, T. (2012). What factors determine student performance in East Asia? New evidence from the 2007 Trends in International Mathematics and Science Study. Asian Economic Journal, 26(4), 333–357.
- Hopko, D.R., McNeil, D.W., Gleason, P.J., & Rabalais, A.E. (2002). The emotional stroop paradigm: Performance as a function of stimulus properties and self-reported mathematics anxiety. Cognitive Therapy and Research, 26(2), 157–166.
- Hox, J.J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.
- Hyde, J.S., & Mertz, J.E. (2009). Gender, culture, and mathematics performance. Proceedings of the National Academy of Sciences of the United States of America, 106(22), 8801–8807.
- Kitsantas, A., Cheema, J., & Ware, H.W. (2011). Mathematics achievement: The role of homework and self-efficacy. Journal of Advanced Academics, 22(2), 310–339.
- Koutsogeorgopoulou, V. (2009). Raising education outcome in Greece. OECD Economics Department Working Papers.
- Lau, K. (2009). A Critical Examination of PISA’s Assessment on Scientific Literacy. International Journal of Science and Mathematics Education, 7, 1061–1088.
- Lee, J. (2009). Universals and specifics of math self-concept, math self-efficacy, and math anxiety across 41 PISA 2003 participating countries. Learning and Individual Differences, 19(3), 355–365.
- Lee, J., & Stankov, L. (2013). Higher-order structure of noncognitive constructs and prediction of PISA 2003 mathematics achievement. Learning and Individual Differences, 26, 119–130.
- Lindberg, S.M., Hyde, J.S., Petersen, J.L., & Linn, M.C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136(6), 1123–1135.
- Ma, X., & Xu, J. (2004). The causal ordering of mathematics anxiety and mathematics achievement: A longitudinal panel analysis. Journal of Adolescence, 27(2), 165–179.
- Marsh, W.H., & Craven, R.G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives. Perspectives on Psychological Science, 1(2), 133–163.
- Marsh, W.H., Hau, K.-T., Artelt, C., Baumert, J., & Peschar, J.L. (2009). OECD’s brief self-report measure of educational psychology’s most useful affective constructs: Cross-cultural, psychometric comparisons across 25 countries. International Journal of Testing, 6(4), 311–360.
- Marsh, W.H., Hau, K.-T., & Kong, C.-K. (2002). Multilevel causal ordering of academic self-concept and achievement: Influence of language of instruction (English compared with Chinese) for Hong Kong students. American Educational Research Journal, 39(3), 727–763.
- Marsh, W.H., & Köller, O. (2004). Unification of theoretical models of academic self-concept/achievement relations: Reunification of East and West German school systems after the fall of the Berlin Wall. Contemporary Educational Psychology, 29(3), 264–282.
- Marsh, W.H., Trautwein, U., Ludtke, O., Koller, O., & Baumert, J. (2005). Academic self-concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering. Child Development, 76(2), 397– 416.
- Martin, M.O., Mullis, I.V.S., Foy, P., & Stanco, G.M. (2012). TIMSS 2011 international results in science. Chestnut Hill, MA: TIMSS & PIRLS International Study Center.
- Martins, L., & Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016–1033.
- Meunier, M. (2011). Immigration and student achievement: Evidence from Switzerland. Economics of Education Review, 30(1), 16–38.
- Morony, S., Kleitman, S., Lee, Y.P., & Stankov, L. (2013). Predicting achievement: Confidence vs self-efficacy, anxiety, and self-concept in Confucian and European countries. International Journal of Educational Research, 58, 79–96.
- Mueller, M., Yankelewitz, D., & Maher, C. (2011). Sense making as motivation in doing mathematics: Results from two studies. The Mathematics Educator, 20(2), 33–43.
- Muijs, D. (2012). Advanced quantitative data analysis. In A. Briggs, M. Coleman, & M. Morrison (Eds.), Research Methods in Educational Leadership & Management (3rd ed.). London: SAGE. Mathematics low achievement in Greece: A multilevel analysis of the PISA 2012 data 23
- Mullis, I., Martin, M., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center.
- National Center for Education Statistics (2009). The children born in 2001 at kindergarten entry: First findings from the kindergarten data collections of early childhood longitudinal study, birth cohort (ECLS-B). Washington, DC: US Department of Education.
- National Mathematics Advisory Panel. (2008). Foundations for success: The final report of the national mathematics advisory panel. Washington, DC: U.S. Department of Education.
- Nelson, G., Westhues, A., & MacLeod, J. (2003). A meta-analysis of longitudinal research on preschool prevention programs for children. Prevention & Treatment, 6(1), No Pagination Specified Article 31a.
- OECD. (2005), OECD Economic Surveys: Greece 2005. Paris: OECD Publishing.
- OECD (2008). Policy brief: Ten steps to equity in education. Paris: PISA, OECD Publishing.
- OECD (2011). Education Policy Advice for Greece. Paris: OECD Publishing. OECD (2012a). Equity and quality in education: Supporting disadvantaged students and schools. Paris: OECD Publishing. OECD (2012b). PISA 2012 main survey: School sampling preparation manual. Paris: PISA, OECD Publishing.
- Palaiologou, N., & Faas, D. (2012). How “intercultural” is education in Greece? Insights from policymakers and educators. Compare: A Journal of Comparative and International Education, 42(4), 563–584.
- Pangeni, K.P. (2014). Factors determining educational quality: Student mathematics achievement in Nepal. International Journal of Educational Development, 34(1), 30–41.
- Rasbash, J., Steele, F., Browne, W., & Goldstein, H. (2015). A user’s guide to MLwiN: Version 2.32. Bristol: Centre for Multilevel Modelling.
- Schunk, D.H. (1991). Self-efficacy and academic motivation. Education Psychology, 26(3-4), 207–231.
- Schunk, D.H., & Pajares, F. (2009). Self-efficacy theory. In K.R. Wentzel & A. Wigfield (Eds.), Handbook of Motivation at School (pp. 35–53). New York, NY: Taylor & Francis.
- Seaton, M., Parker, P., Marsh, W.H., Craven, R.G., & Yeung, A.S. (2014). The reciprocal relations between selfconcept, motivation and achievement: Juxtaposing academic self-concept and achievement goal orientations for mathematics success. Educational Psychology, 34(1), 49–72.
- Simzar, R.M., Martinez, M., Rutherford, T., Domina, T., & Conley, A.M. (2015). Raising the stakes: How students’ motivation for mathematics associates with high-and low-stakes test achievement. Learning and Individual Differences, 39, 49–63.
- Snijders, T., & Bosker, R. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: SAGE.
- Stankov, L. (2013). Noncognitive predictors of intelligence and academic achievement: An important role of confidence. Personality and Individual Differences, 55(7), 727–732.
- Steele, F. (2008). Introduction to multilevel modelling: MLwiN practicals. Bristol: Centre for Multilevel Modelling.
- Steele, F. (2009). Multilevel models for binary responses: MLwiN practical. Bristol: Centre for Multilevel Modelling.
- Suárez-Álvarez, J., Fernández-Alonso, R., & Muñiz, J. (2014). Self-concept, motivation, expectations, and socioeconomic level as predictors of academic performance in mathematics. Learning and Individual Differences, 30, 118–123.
- Swedish National Agency for Education. (2009). What influences educational achievement in Swedish schools: A systematic review and summary analysis. Stockholm: Swedish National Agency for Education.
- Tabachnick, B., & Fidel, S.L. (2013). Using multivatiate statistics (6th ed.). Boston, MA: Pearson Education.
- Tariq, V.N., Qualter, P., Roberts, S., Appleby, Y., & Barnes, L. (2013). Mathematical literacy in undergraduates: Role of gender, emotional intelligence and emotional self-efficacy. International Journal of Mathematical Education in Science & Technology, 44(8), 1143–1159.
- Tarling, R. (2009). Statistical modelling for social researchers: Principles and practice. New York, NY: Routledge. A. Karakolidis, V. Pitsia, A. Emvalotis
- Tobias, S. (1985). Test anxiety: Interference, defective skills, and cognitive capacity. Educational Psychologist, 20(3), 135-142.
- Trowler, V. (2010). Student engagement literature review. Lancaster: The Higher Education Academy.
- UNICEF (2001). The state of the world’s children 2001. Geneva: United Nations Publications.
- Vandecandelaere, M., Speybroeck, S., Vanlaar, G., De Fraine, B., & Van Damme, J. (2012). Learning environment and students’ mathematics attitude. Studies in Educational Evaluation, 38(3-4), 107–120.
- Williams, T., & Williams, K. (2010). Self-efficacy and performance in mathematics: Reciprocal determinism in 33 nations. Journal of Educational Psychology, 102(2), 453–466.
- Yaratan, H., & Kasapoğlu, L. (2012). Eighth grade students’ attitude, anxiety, and achievement pertaining to mathematics lessons. Procedia-Social and Behavioral Sciences, 46, 162–171.
- Zeidner, M., & Matthews, G. (2011). Anxiety 101. New York, NY: Springer. To cite this article: Karakolidis, A., Pitsia, V., & Emvalotis, A. (2016). Mathematics low achievement in Greece: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data. Themes in Science and Technology Education, 9(1), 3-24.

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### Mathematics low achievement in Greece: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data

#### Anastasios Karakolidis, Vasiliki Pitsia & Anastassios Emvalotis, University of Ioannina

Themes in Science and Technology Education Vol. 9, No. 1 (Jul 11, 2016) pp. 3–24

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