Article Sidebar

Download
PDF
Statistic
Read Counter : 3 Download : 3

Downloads

Download data is not yet available.

Main Article Content

Abstract

Shifting students to a growth mindset can increase their achievements. Nevertheless, only a few studies have been conducted on this topic in developing countries. This study aims to examine the relationship between growth mindset, school context, and mathematics achievement in Indonesia. Using a multilevel model on the PISA 2018 data, this study explored the variables that contributed to mathematics achievement. The multilevel analysis showed that students’ gender, growth mindset, index of economic social, and cultural status were statistically significant predictors of students’ mathematics achievement. Girls have been reported to have a higher mathematics achievement than boys in Indonesia. As the students’ growth mindset increases, so do their mathematics achievement.  

Keywords

PISA 2018 Mathematics Multilevel Growth Mindset

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Kismiantini, K., Setiawan, E. P. ., Pierewan, A. C., & Montesinos-López, O. A. . (2021). Growth mindset, school context, and mathematics achievement in Indonesia: A multilevel model. Journal on Mathematics Education, 12(2), 279–294. Retrieved from https://jme.ejournal.unsri.ac.id/index.php/jme/article/view/3720
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Alpar, G., & Van Hoeve, M. (2019). Towards growth-mindset mathematics teaching in the Netherlands. Proceedings of Learning Innovations and Quality (LINQ), 2, 1–17. https://doi.org/10.29007/gdgh
  2. Anderson, J. O., Milford, T., & Ross, S. P. (2009). Multilevel modeling with HLM: Taking a second look at PISA. In Quality Research in Literacy and Science Education: International Perspectives and Gold Standards (pp. 263–286). Springer. https://doi.org/10.1007/978-1-4020-8427-0_13
  3. Avvisati, F., Echazarra, A., Givord, P., & Schwabe, M. (2019). Programme for International Student Assessment (PISA) 2018 Results Country note: Indonesia. OECD Publishing.
  4. Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), 246–263. https://doi.org/10.1111/j.1467-8624.2007.00995.x
  5. Boaler, J., Dieckmann, J. A., Pérez-Núñez, G., Sun, K. L., & Williams, C. (2018). Changing students minds and achievement in mathematics: The impact of a free online student course. Frontiers in Education, 3, 26. https://doi.org/10.3389/feduc.2018.00026
  6. Caniëls, M. C. J., Semeijn, J. H., & Renders, I. H. M. (2018). Mind the mindset! The interaction of proactive personality, transformational leadership and growth mindset for engagement at work. Career Development International, 23(1), 48–66. https://doi.org/10.1108/CDI-11-2016-0194
  7. Cheng, Q., & Hsu, H.-Y. (2016). Low SES and high mathematics achievement: A two-level analysis of the paradox in six Asian education systems. Journal of Education and Human Development, 5(1), 77–85. https://doi.org/10.15640/jehd.v5n1a8
  8. Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences of the United States of America, 113(31), 8664–8668. https://doi.org/10.1073/pnas.1608207113
  9. Dweck, C. S. (2007). The perils and promises of praise - Educational leadership. Educational Leadership, 65(2), 34–39. http://www.ascd.org/publications/educational-leadership/oct07/vol65/num02/The-Perils-and-Promises-of-Praise.aspx
  10. Dweck, C. S., & Yeager, D. S. (2019). Mindsets: A view from two eras. Perspectives on Psychological Science, 14(3), 481–496. https://doi.org/10.1177/1745691618804166
  11. Finch, H., Bolin, J. E., & Kelley, K. (2019). Multilevel Modeling Using R (2nd editio). Chapman and Hall/CRC. https://www.routledge.com/Multilevel-Modeling-Using-R/Finch-Bolin-Kelley/p/book/9781138480674
  12. Good, C., Rattan, A., & Dweck, C. S. (2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology, 102(4), 700–717. https://doi.org/10.1037/a0026659
  13. Horn, I. S. (2007). Fast kids, slow kids, lazy kids: Framing the mismatch problem in mathematics teachers’ conversations. Journal of the Learning Sciences, 16(1), 37–79. https://doi.org/10.1080/10508400709336942
  14. Incikabi, L., Ozgelen, S., & Tjoe, H. (2012). A comparative analysis of numbers and biology content domains between Turkey and the USA. International Journal of Environmental & Science Education, 7(4), 523–536. http://www.ijese.com/
  15. Karakolidis, A., 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. https://www.learntechlib.org/p/173602/
  16. Kartianom, K., & Ndayizeye, O. (2017). What “s wrong with the Asian and African students” mathematics learning achievement? The multilevel PISA 2015 Data Analysis for Indonesia, Japan, and Algeria. Jurnal Riset Pendidikan Matematika, 4(2), 200–210. https://doi.org/10.21831/jrpm.v4i2.16931
  17. Koc, N., & Celik, B. (2015). The impact of number of students per teacher on student achievement. Procedia - Social and Behavioral Sciences, 177, 65–70. https://doi.org/10.1016/j.sbspro.2015.02.335
  18. Lazarević, L. B., & Orlić, A. (2018). PISA 2012 mathematics literacy in Serbia: A multilevel analysis of students and schools. Psihologija, 51(4), 413–432. https://doi.org/10.2298/PSI170817017L
  19. Limeri, L. B., Carter, N. T., Choe, J., Harper, H. G., Martin, H. R., Benton, A., & Dolan, E. L. (2020). Growing a growth mindset: characterizing how and why undergraduate students’ mindsets change. International Journal of STEM Education, 7(1), 1–19. https://doi.org/10.1186/s40594-020-00227-2
  20. Luschei, T. F. (2017). 20 years of TIMSS: Lessons for Indonesia. | IRJE |Indonesian Research Journal in Education|, 1(1), 6–17. https://doi.org/10.22437/irje.v1i1.4333
  21. Maas, C. J. M., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology, 1(3), 86–92. https://doi.org/10.1027/1614-1881.1.3.86
  22. Muthén, B. O. (1991). Multilevel factor analysis of class and student achievement components. Journal of Educational Measurement, 28(4), 338–354. https://doi.org/10.1111/j.1745-3984.1991.tb00363.x
  23. Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. https://doi.org/10.1177/0049124194022003006
  24. Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267–316. https://doi.org/10.2307/271070
  25. Nugrahanto, S., & Zuchdi, D. (2019). Indonesia PISA result and impact on the reading learning program in Indonesia. Proceedings of the International Conference on Interdisciplinary Language, Literature and Education (ICILLE 2018), 373–377. https://doi.org/10.2991/icille-18.2019.77
  26. OECD. (2016). Socio-economic status, student performance and students’ attitudes towards science in PISA 2015 results (Vol. I): Excellence and equity in education (pp. 201–239). OECD Publishing. https://doi.org/10.1787/9789264266490-10-en
  27. OECD. (2019). PISA 2018 results (Volume III) What school life means for students’ lives. OECD Publishing. https://doi.org/10.1787/acd78851-en
  28. Pakpahan, R. (2016). Faktor-faktor yang memengaruhi capaian literasi matematika siswa Indonesia dalam PISA 2012. Jurnal Pendidikan Dan Kebudayaan, 1(3), 331–347. https://doi.org/10.24832/jpnk.v1i3.496
  29. Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 48(1), 85–112. https://doi.org/10.1016/j.jsp.2009.09.002
  30. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & Team, R. C. (2021). nlme: Linear and nonlinear mixed effects models, R package version 3.1-152. https://cran.r-project.org/package=nlme
  31. Raudenbush, S. W., & Bryck, A. S. (2002). Hierarchical linear models applications and data analysis methods (2nd ed.). SAGE Publications, Inc. https://us.sagepub.com/en-us/nam/hierarchical-linear-models/book9230
  32. Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. https://doi.org/10.1177/0956797617739704
  33. Stacey, K. (2011). The PISA view of mathematical literacy in Indonesia. Journal on Mathematics Education, 2(2), 95–126. https://doi.org/10.22342/jme.2.2.746.95-126
  34. Sun, K. L. (2018). The role of mathematics teaching in fostering student growth mindset. Journal for Research in Mathematics Education, 49(3), 330–355. https://doi.org/10.5951/jresematheduc.49.3.0330
  35. Sun, K. L. (2019). The mindset disconnect in mathematics teaching: A qualitative analysis of classroom instruction. Journal of Mathematical Behavior, 56, 100706. https://doi.org/10.1016/j.jmathb.2019.04.005
  36. Thien, L. M., Darmawan, I. G. N., & Ong, M. Y. (2015). Affective characteristics and mathematics performance in Indonesia, Malaysia, and Thailand: what can PISA 2012 data tell us? Large-Scale Assessments in Education, 3(3), 1–16. https://doi.org/10.1186/s40536-015-0013-z
  37. Thien, L. M., & Ong, M. Y. (2015). Malaysian and Singaporean students’ affective characteristics and mathematics performance: evidence from PISA 2012. SpringerPlus, 4(1), 1–14. https://doi.org/10.1186/s40064-015-1358-z
  38. Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C. S., Hinojosa, C. P., Paunesku, D., Romero, C., Flint, K., Roberts, A., Trott, J., Iachan, R., Buontempo, J., Yang, S. M., Carvalho, C. M., … Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573, 364–369. https://doi.org/10.1038/s41586-019-1466-y