Email the author Why is Math Difficult? : Beliefs That Affecting Students' Mathematics Skills
Abstract
This research aims to describe the factors that influence students' beliefs that mathematics is difficult and how to change students' beliefs that mathematics is not difficult. This research method used a Systematic Literature Review with a qualitative approach. This data was obtained from the thematic analysis of several books and articles discussing 'intelligence", "mathematics," and "mindset" those researchers obtained from Google Scholar. The research results showed that parents, teachers, and peers influence students who think intelligence did not change and could be changed. Apart from that, with a fixed mindset, someone might think that mathematics was difficult because the task given was difficult, assume that if the achievement was low, then mathematics was also low, lack motivation after experiencing failure, and mathematics could not be completed if done for a long period. Parents had the biggest influence on students' mindsets. Genes and socioeconomic background were not the reason. Parental parenting patterns that prioritized performance over process influenced students' thinking patterns. Another biggest influence was the teacher. Like parents, teachers who assess based on performance rather than process would make students lose self-confidence when they fail. Teachers who supported their students to surrender to their situation also made students unmotivated to study mathematics more deeply. Collaboration between teachers, parents, and peers is needed to change a fixed mindset to a growth mindset. Constructive feedback is needed so students are always oriented towards a process without results.
Keywords
Full Text:
PDF (ENGLISH)References
Ackerman, P. L., & Lohman, D. F. (2006). Individual Differences in Cognitive Functions. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (pp. 139–161). Lawrence Erlbaum Associates Publishers.
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), 246–263.
Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Psychology and Instruction (5th ed.). MA: Pearson.
Distefano, M., Brien, B. O., Storozuk, A., Ramirez, G., & Maloney, E. A. (2020). Exploring math anxious parents ’ emotional experience surrounding math homework-help. International Journal of Educational Research, 99(October 2019), 101526. https://doi.org/10.1016/j.ijer.2019.101526
Dweck, C. S. (2008). Mindsets and math/science achievement. Carnegie Corporation of New York-Institute. https://doi.org/IJIv4i2A8
Dweck, C. S. (2010). Mind-sets. Principal Leadership, 10(5), 26–29.
Ericsson, K. A. (2014). Expertise. Current Biology, 24(11), 508–510.
Gál, É., Tóth-Király, I., Szamosközi, I., & Orosz, G. (2020). Fixed Intelligence Mindset Moderates the Impact of Adverse Academic Experiences on Students’ Self-Esteem. Journal of College Student Retention: Research, Theory and Practice. https://doi.org/10.1177/1521025120961322
Haimovitz, K., & Dweck, C. S. (2017). The origins of children’s growth and fixed mindsets: New research and a new proposal. Child Development, 88(6), 1849–1859. https://doi.org/10.1111/cdev.12955
Hodaňová, J., & Nocar, D. (2016). Mathematics importance in our life. INTED2016 Proceedings, 1(March), 3086–3092. https://doi.org/10.21125/inted.2016.0172
Hwang, N., Reyes, M., & Eccles, J. S. (2016). Who holds a fixed mindset and whom does it harm in mathematics ? Youth & Society, 51(2), 247–267. https://doi.org/10.1177/0044118X16670058
Jonsson, A. C., Beach, D., Korp, H., & Erlandson, P. (2012). Teachers’ implicit theories of intelligence: Influences from different disciplines and scientific theories. European Journal of Teacher Education, 35(4), 387–400. https://doi.org/10.1080/02619768.2012.662636
King, R. B. (2019). Mindsets are contagious : The social contagion of implicit theories of intelligence among classmates. British Journal of Educational Psychology (2019), 1–15. https://doi.org/10.1111/bjep.12285
Li, Y., & Schoenfeld, A. H. (2019). Problematizing teaching and learning mathematics as “given” in STEM education. International Journal of STEM Education, 6(1). https://doi.org/10.1186/s40594-019-0197-9
Mcnabb, B. (2021). A Fixed Mindset in Mathematics. BU Journal of Graduate Studies in Education, 13(2), 28–32.
Nurdin, I., & Hartati, S. (2019). Nurdin, I., dan Hartati, S. (2019). Metodologi Penelitian Sosial. Media Sahabat Cendekia.
OECD. (2019). PISA 2018 Results Combined Executive Summaries Volume I, II & III. https://www.oecd.org/pisa/Combined_Executive_Summaries_PISA_2018.pdf
Poplin, M. S. (1988). The Reductionistic Fallacy in Learning Disabilities. Journal of Learning Disabilities, 21(7), 389–400.
Qi, W., Shi, J., & Cui, L. (2022). A Developmental System Perspective to Interpret the Link between Parental Fixed Mindset and Youth Mental Health: A Moderated Mediation Model. International Journal of Environmental Research and Public Health, 19(20). https://doi.org/10.3390/ijerph192013285
Rattan, A., Good, C., & Dweck, C. S. (2012). “It’s ok - Not everyone can be good at math”: Instructors with an entity theory comfort (and demotivate) students. Journal of Experimental Social Psychology, 48(3), 731–737. https://doi.org/10.1016/j.jesp.2011.12.012
Rattan, A., Savani, K., Naidu, N. V. R., & Dweck, C. S. (2012). Can everyone become highly intelligent? Cultural differences in and societal consequences of beliefs about the universal potential for intelligence. Journal of Personality and Social Psychology, 103(5), 787–803. https://doi.org/10.1037/a0029263
Scherr, R. E., Plisch, M., Gray, K. E., Potvin, G., & Hodapp, T. (2017). Fixed and growth mindsets in physics graduate admissions. Physical Review Physics Education Research, 13(2), 1–12. https://doi.org/10.1103/PhysRevPhysEducRes.13.020133
Schleider, J. L., Abel, M. R., & Weisz, J. R. (2015). Implicit theories and youth mental health problems: A random-effects meta-analysis. Clinical Psychology Review, 35, 1–9. https://doi.org/10.1016/j.cpr.2014.11.001
Silverman, D. (2014). Interpreting Qualitative Data 5th ed. Sage.
Sun, K. L. (2019). The mindset disconnect in mathematics teaching: A qualitative analysis of classroom instruction. Journal of Mathematical Behavior, 56(April), 100706. https://doi.org/10.1016/j.jmathb.2019.04.005
Vaismoradi, M., Jones, J., Turunen, H., & Snelgrove, S. (2016). Development in Qualitative Content Analysis and Thematic Analysis. Journal of Nursing Education and Practice, 6(5). https://doi.org/https://doi. org/10.5430/jnep.v6n5p100
Zhang, J., Kuusisto, E., Nokelainen, P., & Tirri, K. (2020). Peer Feedback Reflects the Mindset and Academic Motivation of Learners. Frontiers in Psychology, 11(July), 1–10. https://doi.org/10.3389/fpsyg.2020.01701
DOI: https://doi.org/10.33394/jp.v10i4.8652
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 The Author(s)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jurnal Paedagogy
ISSN: 2722-4627 (Online)
Published by Fakultas Ilmu Pendidikan dan Psikologi (FIPP)
Universitas Pendidikan Mandalika (UNDIKMA)
Email: [email protected]
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.