Abstract
This study is part of a larger research that explores the creation of an instrument to capture the social and cultural factors that affect Black students’ persistence in STEM. Most research on self-efficacy in the science education literature were either done at predominantly White institutions, during summer programs for students of color, or on predominantly White populations. This study provides insights into self-efficacy indicators at an institution that was specifically created to consider the social, cultural, and historical implications for educating Blacks in STEM. One hundred sixty-four undergraduate students enrolled in an introductory biology course at an Historically Black College and University completed a questionnaire. The survey addressed the hypothesized factors—expectancy, self-efficacy, familial self-efficacy, cognitive self-efficacy, and commitment. The results highlight the importance of science identity and familial sources of vicarious experiences as important indicators of persistence and performance in STEM. The importance of social and cultural factors for Black students’ persistence in STEM is underscored.
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References
Bandura, A. (1997). Self-efficacy; the exercise of control. W.H. Freeman and Company.
Boykin, A.W. & Noguera, P. (2011). Creating the opportunity to learn. Moving from research to practice to close the achievement gap. ASCD.
Brown, B. A. (2004). Discursive identity: Assimilation into the culture of science and its implications for minority students. Journal of Research in Science Teaching, 41(8), 810–834.
Brown, B. A. (2006). “It isn’t no slang that can be said about this stuff”: Language, identity, and appropriating science discourse. Journal of Research In Science Teaching, 43(1), 96–126.
Brown, B. A., Henderson, J. B., Gray, S., Donovan, B., Sullivan, S., Patterson, A., & Waggstaff, W. (2016). From description to explanation: An empirical exploration of the African-American pipeline problem in STEM. Journal of Research in Science Teaching, 53(1), 146–177.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Lawrence Erlbaum Associates, Inc.
Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lense. Journal of Research in Science Teaching, 44, 1187–1218.
Cheung, D. (2015). The combined effects of classroom teaching and learning strategy use on students’ chemistry self-efficacy. Research in Science Education, 45, 101–116.
Dumbauld, J., Black, M., Depp, C. A., Daly, R., Curran, M. A., Winegarden, B., & Jeste, D. V. (2014). Association of learning styles with research self-efficacy: Study of short-term research training program for medical students. Clinical and Translational Science, 7, 489–492.
Enciso, P., & Ryan, C. (2011). Sociocultural theory. In D. Lapp & D. Fisher (Eds.), Handbook of research on teaching the English language arts (pp. 132–138). Routledge.
Estrada, M., Woodcock, A., Hernandez, P. R., & Schultz, P. W. (2011). Toward a model of social influence that explains minority student integration into the scientific community. Journal of Educational Psychology, 103(1), 206–222.
Fabrigar, R. L., Wegener, T. D., MacCallum, C. R., & Strahan, J. E. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Gee, J. P. (2000-2001). Identity as an analytic lens for research in education. Review of Research in Education, 25: 99–125.
Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191–205. https://doi.org/10.1177/1094428104263675.
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. https://doi.org/10.1177/0013164405282485.
Margolis, J., Fisher, A., & Miller, F. (2000). The anatomy of interest. Women in undergraduate computer science. Women’s Studies Quarterly, 28(1/2), 104–127.
Molden, D. C., & Higgins, E. T. (2005). Motivated thinking. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of reasoning and thinking (pp. 295–317). Cambridge University Press.
Moore, F. M. (2007). Language in science education as a gatekeeper to learning, teaching, and professional development. Journal of Science Teacher Education, 18, 319–343.
Mutegi, J. W. (2013). “Life’s first need is for us to be realistic” and other reasons for examining the sociocultural construction of race in the science performance of African American students. Journal of Research in Science Teaching, 50(1), 82–103.
National Science Board. (2018). Science and Engineering Indicators 2018. NSB-2018–1. Alexandria: National Science Foundation. Available at https://www.nsf.gov/statistics/indicators/.
Panizzon, D., & Levins, L. (1997). An analysis of the role of peers in supporting female students’ choices in science subjects. Research in Science Education, 27(2), 251–270.
Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis. Sage Publications, Inc. https://doi.org/10.4135/978141298489.
Robnett, R. D., Chemers, M. M., & Zurbriggen, E. L. (2015). Longitudinal associations among undergraduates’ research experience, self-efficacy, and identity. Journal of Research in Science Teaching, 52(6), 847–867.
Salto, L. M., Riggs, M. L., De Leon, D. D., Casiano, C. A., & De Leon, M. (2014). Underrepresented minority high school and college students report STEM-pipeline sustaining gains after participating in the Loma Linda University summer health disparities research program. PLoS ONE, 9(9), 1–13.
Syed, M., Goza, B. K., Chemers, M. M., & Zurbriggen, E. L. (2012). Individual differences in preferences for matched-ethnic mentors among high-achieving ethnically diverse adolescents in STEM. Child Development, 83(3), 896–910.
Walls, L. (2012). Third grade African American students’ views of the nature of science. Journal of Research in Science Teaching, 49(1), 1–37.
Walls, L. (2016). Awakening a dialogue: A critical race theory analysis of U.S. Nature of Science Research From 1967 to 2013. Journal of Research in Science Teaching, 53(10), 1546–1570.
Warren, B., Ballenger, C., Ogonowski, M., Rosebery, A. S., & Hudicourt-Barnes, J. (2001). Rethinking diversity in learning science: The logic of everyday sense-making. Journal of Research in Science Teaching, 38(5), 529–552.
Webb-Williams, J. (2018). Science self-efficacy in the primary classroom: Using mixed methods to investigate sources of self-efficacy. Research in Science Education, 48, 939–961.
Wong, B. (2015). Careers “from” but not “in” science: Why are aspirations to be a scientist challenging for minority ethnic students? Journal of Research in Science Teaching, 52(7), 979–1002.
Wong, S. Y., Liang, J. C., & Tsai, C. C. (2019). Uncovering Malaysian secondary school students’ academic hardiness in science, conceptions of learning science, and science learning self-efficacy: A structural equation modelling analysis. Research in Science Education. https://doi.org/10.1007/s11165-019-09908-7
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Quinlan, C.L., Picho, K. & Burke, J. Creating an Instrument to Measure Social and Cultural Self-efficacy Indicators for Persistence of HBCU Undergraduates in STEM. Res Sci Educ 52, 1583–1601 (2022). https://doi.org/10.1007/s11165-021-09992-8
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DOI: https://doi.org/10.1007/s11165-021-09992-8