Jurnal Riset Pendidikan Matematika


PISA 2015, Multilevel Model, Mathematics

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This research aims at knowing the factors, involving both students and school levels, related to the math learning achievement for students in Indonesia, Japan, and Algeria by using PISA 2015 data. The sample in this study consists of students from three countries that took part in PISA 2015. The three countries chosen are Indonesia, Japan, and Algeria, each respectively having as participants 5.800, 6.411, and 4.460. the findings showed that the sense of belonging of students towards mathematics, the socio-economic status of their families, and the average of school's social-economic status can predict significantly the students' math learning achievement for the Indonesia and Japan, while for the Algerian students the socio-economic status is statistically insignificant in predicting their math learning achievement. The outcome of this analysis support the idea that the school attended plays a big role as far as mathematics learning achievement is concerned. To conclude, it should be summed up that the affective characteristics (sense of belonging of students), family background (students' socio-economic status), and the variable school-level (average socio-economic status of schools) can be among students as far as mathematics learning achievement is concerned.

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Anderson, J. O., Lin, H. S., Treagust, D. F., Ross, S. P., & Yore, L. D. (2007). Using large-scaleeassessment datassets for research in science and mathematics education: Programme for International Student Assessment (PISA). International Journal of Science and Mathematics Education, 5, 591-614. doi:10.1007/s10763-007-9090-y.

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, 599-629. doi:10.1016/j.cedpsych.2006.09.002.

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. doi:10.1016/j.lindif.2007.03.007.

Chiu, M. M., Chow, B. W. Y., McBride, C., & Mol, S. T. (2016). Students' sense of belonging at school in 41 countries: cross-cultural variability. Journal of Cross-Cultural Psychology, 47(2), 175-196. doi:10.1177/0022022115617031.

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Singapore: Sage.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel hierarchical models (Vol. 1). New York, NY: Cambridge University Press.

Goldstein, H. (2011). Multilevel statistical models (4th ed.) (Wiley Series in Probability and Statistics). John Wiley & Sons.

Hampden-Thompson, G. (2013). Family policy, family structure, and children's educational achievement. Social Science Research,42(3), 804-817. doi:10.1037/0022-0663.95.1.124.

Hattie, J. A. C. (2009). Visible learning: a synthesis of over 800 meta-analyses relating to performance. New York, NY: Routledge.

Hox, J.J. (2010). Multilevel analysis: Techniques and applications (2th ed). New York, NY: Routledge.

Kim, J. S., Anderson, C. J., & Keller, B. (2013). Multilevel analysis of assessment data. Handbook of international large-scale assessment: Background, technical issues, and methods of data analysis, 389-425.

Lamb, S., Hogan, D., & Johnson, T. (2001). The stratification of learning opportunities and achievement in Tasmanian secondary schools. Australian Journal of Education, 45(2), 153-167. doi:10.1177/000494410104500205.

Marks, G. N., Cresswell, J., & Ainley, J. (2006). Explaining socioeconomic inequalities in student achievement: The role of home and school factors. Educational Research and Evaluation, 12(2), 105-128. doi: 10.1080/13803610600587040.

Martins, L., & Veiga, P. (2010). Do inequalities in parents' education playan important role in PISA students' mathematics achievement test score disparities?. Economics of Education Review, 29(6), 1016-1033. doi:10.1016/j.econedurev.2010.05.001.

Martin, M.O., Mullis, I. V. S., Foy, P., & Stanco, G.M. (2012). TIMSS 2011 international results in science. Chestnutt Hill, MA: TIMSS & PIRLS International Study Center.

McCoach, D. B., Gable, R. K., & Madura, J. P. (2013). Instrument development in the affective domain. New York, NY: Springer.

Ministry of Education, MOE. (2013). Malaysian education blueprint 2013-2025: preschool to postsecondary education. Putrajaya: MOE.

Mullis, I., Martin, M., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Chestnutt Hill, MA: TIMSS & PIRLS International Study Center.

OECD (2009). PISA data analysis manual: SPSS second edition.

OECD (2013). PISA 2012 results: excellence through equity"”giving every student the chance to succeed Vol. II. Paris, France: PISA, OECD Publishing. doi:10.1787/9789264201132-en.

OECD. (2017a). PISA 2015 Results (Volume III): Students' Well-Being. Paris, France: PISA, OECD Publishing. doi:10.1787/9789264273856-en.

OECD. (2017b). PISA 2015 Assessment and Analytical Framework: Science, Reading, Mathematic, Financial Literacy and Collaborative Problem Solving. Paris, France: PISA, OECD Publishing. doi:10.1787/9789264281820-en.

OECD. (2017c). PISA 2015 Technical Reports. PISA: OECD Publishing. http://www.oecd.org/pisa/data/2015-technical-report/.

Prensel, M., Kobarg, M., Schöps, K., & Rönnebeck, S. (2013). Research on PISA: research outcomes of the PISA research conference 2009. New York, NY: Springer.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

Stankov, L. (2013). Noncognitive predictors of intelligence and academic achievement: An important role of confidence. Personality and Individual Differences, 55(7), 727-732. doi:10.1016/j.paid.2013.07.006.

Steele, F. (2008). Module 5 (concepts): Introduction to multilevel modelling. Centre for Multilevel Modelling. Bristol, England: University of Bristol.

Spiegelhalter, D. (2013). The problems with PISA statistical methods. Retrieved from http://www.statslife.org.uk/opinion/1074-the-problems-with-pisa-statistical-methods.

Tabachnick, B., & Fidel, S. L. (2013). Using multivatiate statistics, 6th ed. Boston, MA: Pearson Education.

Taylor, G., Jungert, T., Mageau, G. A., Schattke, K., Dedic, H., Rosen field, S., & Koestner, R. (2014). A self-determination theory approach to predicting school achievement over time: The unique role of intrinsic motivation. Contemporary Educational Psychology, 39, 342-358. doi:10.1016/j.cedpsych.2014.08.002.

Willms, J. D. (2003). Student engagement at school: A sense of belonging and participation. Paris, France: OECD Publishing.

Woltman, H., Feldstain, A., Mackay, J. C., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1), 52-69. doi:10.20982/tqmp.08.1.p052.