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Elementary Students’ Mathematics Identity: Findings from a Longitudinal Study in an Out-of-School Setting

Journal for STEM Education Research (2022)Cite this article

Abstract

This study presents findings from a randomized controlled trial of an afterschool program intended to develop mathematics identity for students from grades 4 and 5 in groups underrepresented in STEM. Mathematics identity refers to the ways that students think about themselves in relation to mathematics and the extent to which they have developed a commitment to, and have come to see value in, mathematics. While the impact analyses showed no effects of the intervention on mathematics identity or achievement, the exploration of the longitudinal data collected over 2 years provided several insights. On average, student mathematics identity remained constant over the study period; however, the overall averages mask large variations in individual students and sites. Some students saw improvement in mathematics identity, while others saw decreases. Counter to findings in previous literature, we found no overall differences by gender suggesting that boys and girls report similar mathematics identity. Importantly, we found a positive relationship between mathematics identity and achievement. This finding holds in both directions and suggests that boosting mathematics identity could lead to improving mathematics achievement and vice versa. This study contributes to our understanding of mathematics identity, how it is measured, how it evolves over time, what relationship it has to mathematics achievement, and what its potential for development in afterschool environments could be.

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Notes

  1. The student longitudinal panel is unbalanced; 458 students (or 33.7% of our student sample) provided only a single mathematics identity measure at some point in the study, while 901 students (66.3%) provided us with two or more measures of mathematics identity. In the analysis in Section 4, we estimate our growth models in the presence of such partially missing data. Following Curran et al. (2010), we fit our models via maximum likelihood, which helps to weight observations with large number of data points more heavily than observations with a smaller number of data points.

  2. Equivalence results indicated no significant differences in site, student, or educator characteristics between treatment and control groups except for educator gender. The percentage of female treatment educators was significantly higher than the percentage of female control educators (p < .05).

  3. Three factors were selected to rotate based on the scree test. Factor 2 consisted of negatively worded items and, after further analyses, was determined to be a method factor and was not interpreted substantively. Factor 3 included items referring to mathematics in relation to “my future” and the pattern of item loading appeared related primarily to the specific wording of the items. After further investigation, we focused on factor 1 as the measure of mathematics identity. Based on the output from the CFA, we further dropped 2 items without harming the factor structure. The goodness of fit of the final 16-item one-factor model was satisfactory, meeting all established criteria: AGFI = .92, CFI = .98, RMSEA = .05, SRMR = .03. The chi-square was 145.76 (df = 67, p < .001).

  4. Data from the 63 responses from the theme 3 educator survey administration were used in the maximum likelihood factor analysis. Based on the scree plot, 4 factors were rotated with a direct oblimin oblique procedure. We retained three distinct factors, while the fourth factor contained only negatively worded items and was not interpreted substantively.

  5. Growth curve models are also referred to as latent trajectory models or latent growth curve models. This approach to longitudinal modeling explicitly models the shape of trajectories of individuals over time and how these trajectories vary both systematically due to subject-level covariates as well as randomly. In the study here, both the shape of the trajectories and the degree of variability in growth among students are of interest. With random intercept, we allow each site and student to start their trajectory at a different initial level of mathematics identity; and with random slope, we allow the mathematics identity of each site and student to grow at different rates.

  6. Time can also be seen as semesters, since each theme takes roughly one semester to be implemented.

  7. We did not introduce educator mathematics identity as a covariate in the main impact growth model for two reasons. First, educator mathematics identity may have also been affected by the ASM + intervention, and as an endogenous time-varying covariate, it would have required additional assumptions to be estimated. Second, educator mathematics identity measures were collected at the end of each theme but not at baseline, which would have resulted in missing baseline data.

  8. A latent variable is not directly observed but rather inferred through statistical modeling from other observed variables.

  9. There were some missing data in student characteristics (4–6% of students were missing gender, ethnicity, or grade level) and in math achievement scores (37–39% for academic years 2016/2017 and 2017/2018; 64% for academic year 2015/2016, mainly because they were too young to be tested that year). The models were also estimated with simple missing data imputation leading to similar results.

  10. Random effects are not estimated and presented in Table 1; however, we can assign hypothetical values to them and present overall findings visually.

  11. All fixed effects are presented in Table 1.

  12. The random effects represent unobservable characteristics that are relevant and important but not measured by the researchers. They can include things such as educator motivation, parental support, and financial resources.

References

  • Adelson, J. L., & McCoach, D. B. (2011). Development and psychometric properties of the mathematics and me survey: Measuring third through sixth graders’ attitudes toward mathematics. Measurement and Evaluation in Counseling and Development, 44(4), 225–247. https://doi.org/10.1177/0748175611418522

    Article  Google Scholar 

  • Afterschool Alliance. (2004). America after 3pm: A household survey on afterschool in America: Key findings. Retrieved from http://www.afterschoolalliance.org/documents/aa3pm_key_findings_2009.pdf. Accessed 21 March 2022

  • Afterschool Alliance. (2011a). Afterschool: A vital partner in STEM education. Retrieved from http://www.afterschoolalliance.org/Afterschool_as_STEMpartner.pdf. Accessed 21 March 2022

  • Afterschool Alliance. (2011b). STEM learning in afterschool: An analysis of impact and outcomes. Retrieved from http://www.afterschoolalliance.org/STEM-Afterschool-Outcomes.pdf. Accessed 21 March 2022

  • Anderson, R. (2007). Being a mathematics learner: Four faces of identity. The Mathematics Educator, 17(1), 7–14. https://files.eric.ed.gov/fulltext/EJ841557.pdf. Accessed 21 March 2022

  • Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Self-efficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development, 72(1), 187–206. https://doi.org/10.1111/1467-8624.00273

    Article  Google Scholar 

  • Barton, A. C., Tan, E., & Rivet, A. (2008). Creating hybrid spaces for engaging school science among urban middle school girls. American Educational Research Journal, 45(1), 68–103. https://doi.org/10.3102/0002831207308641

    Article  Google Scholar 

  • Bevan, B., Michalchik, V., Bhanot, R., Rauch, N., Remold, J., Semper, R. & Shields, P. (2010). Out-of-school time STEM: Building experience, building bridges, San Francisco: Exploratorium, 2010. Retrieved from http://www.integratingengineering.org/after_school/Out%20of%20School%20Time%20STEM_NSF%20AYS%20report.pdf. Accessed 21 March 2022

  • Berry, R. Q. (2008). Access to upper-level mathematics: The stories of successful African American middle school boys. Journal for Research in Mathematics Education, 39(5), 464–488. JSTOR. https://www.jstor.org/stable/40539311. Accessed 21 March 2022

  • Beesley, A., Fancsali, C. & Gulemetova, M. (2021). Survey of mathematical identity: Factor analysis and use in an afterschool program evaluation. Manuscript under review

  • Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers’ math anxiety affects girls’ math achievement. Proceedings of the National Academy of Sciences, 107(5), 1860–1863. https://doi.org/10.1073/pnas.0910967107

    Article  Google Scholar 

  • Black, L., Williams, J., Hernandez-Martinez, P., Davis, P., Pampaka, M., & Wake, G. (2010). Developing a ‘leading identity’: The relationship between students’ mathematical identities and their career and higher education aspirations. Educational Studies in Mathematics, 73(1), 55–72. https://doi.org/10.1007/s10649-009-9217-x

    Article  Google Scholar 

  • Boaler, J., & Selling, S. K. (2017). Psychological imprisonment or intellectual freedom? A longitudinal study of contrasting school mathematics approaches and their impact on adults’ lives. Journal for Research in Mathematics Education, 48(1), 78–105. https://doi.org/10.5951/jresematheduc.48.1.0078

    Article  Google Scholar 

  • Boaler, J., William, D., & Zevenbergen, R. (2000). The construction of identity in secondary mathematics education. Retrieved from ERIC database. (ED482654). https://eric.ed.gov/?id=ED482654. Accessed 21 March 2022

  • Bohrnstedt, G. W., Zhang, J., Park, B. J., Ikoma, S., Broer, M., & Ogut, B. (2020). Mathematics identity, self-efficacy, and interest and their relationships to mathematics achievement. In R. Serpe, R. Stryker, & B. Powell (Eds.), Identity and symbolic interaction: Deepening foundations, building bridges (pp. 169–210). Springer International Publishing. https://doi.org/10.1007/978-3-030-41231-9_7

    Chapter  Google Scholar 

  • California Tomorrow. (2003). Pursuing the promise: Access, equity and diversity in after school programs: National research findings. Oakland, CA: Author. Pursuing the promise: Addressing equity, access and diversity in after school and youth programs (from A Resource Guide for Planning and Operating Afterschool Programs) (sedl.org)

  • Casad, B. J., Hale, P., & Wachs, F. L. (2015). Parent-child math anxiety and math-gender stereotypes predict adolescents’ math education outcomes. Frontiers in Psychology, 6, 1597. https://www.frontiersin.org/article/10.3389/fpsyg.2015.01597. Accessed 21 March 2022

  • Chamany, K. (2006). Science and social justice: Making the case for case studies. Journal of College Science Teaching, 36(2), 54–59. p54-59chamany.indd.

    Google Scholar 

  • Cobb, P., Gresalfi, M., & Hodge, L. L. (2009). An interpretive scheme for analyzing the identities that students develop in mathematics classrooms. Journal for Research in Mathematics Education, 40(1), 40–68. https://doi.org/10.5951/jresematheduc.40.1.0040

    Article  Google Scholar 

  • Correll, S. J. (2001). Gender and the career choice process: The role of biased self-assessments. American Journal of Sociology, 106(6), 1691–1730. https://doi.org/10.1086/321299

    Article  Google Scholar 

  • Cross, D. I. (2009). Alignment, cohesion, and change: Examining mathematics teachers’ belief structures and their influence on instructional practices. Journal of Mathematics Teacher Education, 12(5), 325–346. https://doi.org/10.1007/s10857-009-9120-5

    Article  Google Scholar 

  • Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11(2), 121–136. https://doi.org/10.1080/15248371003699969

    Article  Google Scholar 

  • Cvencek, D., Nasir, N. I. S., O’Connor, K., Wischnia, S., & Meltzoff, A. N. (2015). The development of math–race stereotypes: “They say Chinese people are the best at math.” Journal of Research on Adolescence, 25(4), 630–637. https://doi.org/10.1111/jora.12151

    Article  Google Scholar 

  • Darragh, L. (2015). Recognising ‘good at mathematics’: Using a performative lens for identity. Mathematics Education Research Journal, 27, 83–102. https://doi.org/10.1007/s13394-014-0120-0

    Article  Google Scholar 

  • Darragh, L. (2016). Identity research in mathematics education. Educational Studies in Mathematics, 93(1), 19–33. https://doi.org/10.1007/s10649-016-9696-5

    Article  Google Scholar 

  • Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82(1), 405–432. https://doi.org/10.1111/j.1467-8624.2010.01564.x

    Article  Google Scholar 

  • Farrington, C., Levenstein, R., & Nagaoka, J. (2013). “Becoming effective learners” survey development project. Society for Research on Educational Effectiveness.

  • Fernandez, F., Froschl, M., Lorenzetti, L., & Stimmer, M. (2022). Investigating the importance of girls’ mathematical identity within United States STEM programmes: A systematic review. International Journal of Mathematical Education in Science and Technology, 0(0), 1–41. https://doi.org/10.1080/0020739X.2021.2022229

  • Gonzalez, L., Chapman, S., & Battle, J. (2020). Mathematics identity and achievement among Black students. School Science and Mathematics, 120(8), 456–466. https://doi.org/10.1111/ssm.12436

    Article  Google Scholar 

  • Graven, M., & Heyd-Metzuyanim, E. (2019). Mathematics identity research: The state of the art and future directions. ZDM, 51, 361–377. https://doi.org/10.1007/s11858-019-01050-y

    Article  Google Scholar 

  • Gresalfi, M. S., & Cobb, P. (2006). Cultivating students’ discipline-specific dispositions as a critical goal for pedagogy and equity. Pedagogies: An International Journal, 1(1), 49–57. https://doi.org/10.1207/s15544818ped0101_8

    Article  Google Scholar 

  • Grootenboer, P., & Zevenbergen, R. (2008). Identity as a lens to understand learning mathematics: Developing a model. Navigating Currents and Charting Directions (Proceedings of the 31st Annual Conference of the Mathematics Education Research Group of Australasia), 1, 243–250

  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511815355

  • Martin, D. B. (2000). Mathematics success and failure among African-American youth: The roles of sociohistorical context, community forces, school influence, and individual agency (1st ed.). Routledge.

    Book  Google Scholar 

  • Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907. https://doi.org/10.1002/sce.20441

    Article  Google Scholar 

  • McCoach, D. B., & Kaniskan, B. (2010). Using time-varying covariates in multilevel growth models. Frontiers in Psychology, 1. https://www.frontiersin.org/article/10.3389/fpsyg.2010.00017. Accessed 21 March 2022

  • McKown, C., & Strambler, M. J. (2009). Developmental antecedents and social and academic consequences of stereotype-consciousness in middle childhood. Child Development, 80(6), 1643–1659. https://doi.org/10.1111/j.1467-8624.2009.01359.x

    Article  Google Scholar 

  • Mulhern, F., & Rae, G. (1998). Development of a shortened form of the Fennema-Sherman Mathematics Attitudes Scales. Educational and Psychological Measurement, 58(2), 295–306. https://doi.org/10.1177/0013164498058002012

    Article  Google Scholar 

  • Nasir, N. (2002). Identity, goals, and learning: Mathematics in cultural practice. Mathematical Thinking and Learning, 4, 213–247. https://doi.org/10.1207/S15327833MTL04023_6

    Article  Google Scholar 

  • Nasir, N., & Cobb, P. (2006). Improving access to mathematics: Diversity and equity in the classroom. Multicultural Education Series. Teachers College Press.

    Google Scholar 

  • National Academies of Sciences, Engineering, and Medicine (2021). Cultivating interest and competencies in computing: Authentic experiences and design factors. The National Academies Press. https://doi.org/10.17226/25912

  • Okeke, N. A., Howard, L. C., Kurtz-Costes, B., & Rowley, S. J. (2009). Academic race stereotypes, academic self-concept, and racial centrality in African American youth. Journal of Black Psychology, 35(3), 366–387. https://doi.org/10.1177/0095798409333615

    Article  Google Scholar 

  • Radovic, D., Black, L., Williams, J., & Salas, C. (2018). Towards conceptual coherence in the research on mathematics learner identity: A systematic review of the literature. Educational Studies in Mathematics, 99, 1–22. https://doi.org/10.1007/s10649-018-9819-2

    Article  Google Scholar 

  • Samuelsson, M., & Samuelsson, J. (2016). Gender differences in boys’ and girls’ perception of teaching and learning mathematics. Open Review of Educational Research, 3(1), 18–34. https://doi.org/10.1080/23265507.2015.1127770

    Article  Google Scholar 

  • Scott, D. (2001). Review of mathematics success and failure among African American youth [Review of Review of mathematics success and failure among African-American youth, by D. B. Martin]. The American Mathematical Monthly, 108(5), 481–485. JSTOR. https://doi.org/10.2307/2695817

  • Sfard, A., & Prusak, A. (2005a). Telling identities: In search of an analytic tool for investigating learning as a culturally shaped activity. Educational Researcher, 34(4), 14–22. https://doi.org/10.3102/0013189X034004014

    Article  Google Scholar 

  • Sfard, A., & Prusak, A. (2005b). Identity that makes a difference: Substantial learning as closing the gap between actual and designated identities. Proceedings of the 29th Conference of the International Group for the Psychology of Mathematics Education, 16. (PDF) Identity that makes a difference: Substantial learning as closing the gap between actual and designated identities (researchgate.net)

  • Shapiro, J. R., & Williams, A. M. (2012). The role of stereotype threats in undermining girls’ and women’s performance and interest in STEM fields. Sex Roles, 66(3–4), 175–183. https://doi.org/10.1007/s11199-011-0051-0

    Article  Google Scholar 

  • Simpson, A., & Bouhafa, Y. (2020). Youths’ and adults’ identity in STEM: A systematic literature review. Journal for STEM Education Research, 3(2), 167–194. https://doi.org/10.1007/s41979-020-00034-y

    Article  Google Scholar 

  • Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press.

  • Solomon, Y. J. (2007). Not belonging? What makes a functional learner identity in undergraduate mathematics? Studies in Higher Education, 32(1), 79–96. https://doi.org/10.1080/03075070601099473

    Article  Google Scholar 

  • StataCorp (2019). Stata statistical software: College Station, TX. StataCorp.

  • Tajfel, H., & Turner, J. (1986). The social identity theory of intergroup behavior. In S. Worchel & W. Austin (Eds.), Psychology of intergroup relations (pp. 7–24). Nelson-Hall.

    Google Scholar 

  • Walls, F. (2009). Mathematical subjects: Children talk about their mathematics lives. Springer Science & Business Media.

    Book  Google Scholar 

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Funding

This research was supported by Award #1515586 from the National Science Foundation.

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Authors and Affiliations

  1. American Institutes for Research, Arlington, VA, USA

    Michaela Gulemetova & Uttara Balakrishnan

  2. SRI International, Menlo Park, CA, USA

    Andrea D. Beesley

  3. Research Alliance for New York City Schools, New York, NY, USA

    Cheri Fancsali

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  1. Michaela Gulemetova
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  2. Andrea D. Beesley
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  4. Uttara Balakrishnan
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Correspondence to Michaela Gulemetova.

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Gulemetova, M., Beesley, A.D., Fancsali, C. et al. Elementary Students’ Mathematics Identity: Findings from a Longitudinal Study in an Out-of-School Setting. Journal for STEM Educ Res (2022). https://doi.org/10.1007/s41979-022-00067-5

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  • DOI: https://doi.org/10.1007/s41979-022-00067-5

Keywords

  • Mathematics identity
  • Growth curve model
  • Afterschool
  • Extended learning