GROWTH MINDSET, SCHOOL CONTEXT, AND MATHEMATICS ACHIEVEMENT IN INDONESIA: A MULTILEVEL MODEL
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
Full Text:
PDFReferences
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
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
Avvisati, F., Echazarra, A., Givord, P., & Schwabe, M. (2019). Programme for International Student Assessment (PISA) 2018 Results Country note: Indonesia. OECD Publishing.
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
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
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
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
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
Dweck, Carol 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
Dweck, Carol Sorich. (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
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
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
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
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/
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/
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
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
Lazarevi?, L. B., & Orli?, 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
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
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
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
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
Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. https://doi.org/10.1177/0049124194022003006
Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267–316. https://doi.org/10.2307/271070
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
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
OECD. (2019). PISA 2018 results (Volume III) What school life means for students’ lives. OECD Publishing. https://doi.org/10.1787/acd78851-en
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
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
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
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
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
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
Sun, Kathy Lht. (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
Sun, Kathy Liu. (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
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
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
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
DOI: https://doi.org/10.22342/jme.12.2.13690.279-294
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Kampus FKIP Bukit Besar
Jl. Srijaya Negara, Bukit Besar
Palembang - 30139
p-ISSN: 2087-8885 | e-ISSN: 2407-0610
Journal on Mathematics Education (JME) is licensed under a Creative Commons Attribution 4.0 International License.
View My Stats