FINDINGS § Gendered performance differences in many courses are small but
reliably present from term to term. § Gendered performance differences in biology, chemistry, and physics
lecture courses tend to favor men while those in the corresponding lab courses tend to be more equitable.
§ While the majority of mathema>cs and sta>s>cs courses yield a grade penalty, overall they appear to favor neither men nor women.
§ Wri>ng and social science courses (with the excep>on of economics) do not tend to yield substan>al gendered performance differences.
§ These results are consistent across five rela>vely similar universi>es and six academic years.
LIMITATIONS § Using high school GPA as an addi>onal covariate would have been
preferable, but we found no reasonable way to reconcile the many ways that they are recorded and processed by our admissions offices.
§ Transfer students are missing standardized test score data at a higher rate than non-‐transfer students, though we found no significant rela>onship between the rate of missing data and observed GPDs.
CONCLUSIONS Though simple interven>ons to address gendered performance differences in STEM are aNrac>ve5, in many cases they have failed to replicate6, and it is important to recognize differences among individual STEM disciplines.7 We agree with the assessment of Halpern et al. that “there are no single or simple answers to the complex ques>ons about sex differences in science and mathema>cs”. Rather, this work should compel faculty to ask, as many are already doing, how we can learn from this informa>on to change the future in whatever ways are appropriate in our local contexts. Grades are consequen>al performance measures, and imposing large penal>es on female students rela>ve to males (or vice versa) creates yet another headwind impeding gender equity in STEM. In the face of evidence that faculty, especially male STEM faculty, are reluctant to accept evidence of gendered biases in STEM8, it is impera>ve that we con>nue to collaborate across ins>tu>ons to make all courses equitable spaces for all students to learn. This work has been submiNed for publica>on to AERA Open.
ACKNOWLEDGEMENTS: The Sloan Founda>on and the Provosts of our universi>es supported this CommiNee on Ins>tu>onal Coopera>on (CIC) Learning Analy>cs project. Numerous faculty and staff at each university helped us determine which courses would be comparable. Special support for this project was provided at Indiana University by Vice Provost Dennis Groth; Dawit Gelan and Yanan Feng at the Bloomington Assessment and Research (BAR) office helped as well.
REFERENCES: 1Huberth, M., Chen, P., Tritz, J., & McKay, T. A. (2015). PLOS ONE, 10(9), e0137001. 2Has>e, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Sta>s>cal Learning: Data Mining, Inference, and Predic>on (2nd edi>on). New York: Springer. 3Hansen, B. B. (2004). Journal of the American Sta>s>cal Associa>on, 99(467), 609–618. 4Hansen, B. B. (2007). Optmatch: Flexible, Op>mal Matching for Observa>onal Studies. R News, 7(2), 18–24. 5Miyake, A., Kost-‐Smith, L. E., Finkelstein, N. D., Pollock, S. J., Cohen, G. L., & Ito, T. A. (2010). Science, 330(6008), 1234–1237. 6Madsen, A., McKagan, S. B., & Sayre, E. C. (2013). Physical Review Special Topics -‐ Physics Educa>on Research, 9(2), 020121. 7Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Psychological Bulle>n, 143(1), 1–35. 8Handley, I. M., Brown, E. R., Moss-‐Racusin, C. A., & Smith, J. L. (2015). Proceedings of the Na>onal Academy of Sciences, 112(43), 13201–13206.
RESEARCH QUESTION How do gendered performance differences within introductory courses in a discipline compare across ins>tu>ons?
DATA ANALYSIS § The student-‐level data were locally maintained and analyzed
separately at each ins>tu>on using common code wriNen in R. § Both mul>ple linear regression2 and op>mal matching3,4 models were
used to analyze course grade as a func>on of gender, GPAO, ACT Mathema>cs and English subscores, and term.
§ We report results based on the matching method because it is more conserva>ve, although both methods return similar results overall.
RESULTS
ABSTRACT The success of females in higher educa>on has improved over the past few decades, but gendered differences in par>cipa>on and career progress persist in some STEM-‐related disciplines. The purpose of this study is to explore paNerns of performance in gateway courses that introduce male and female students to STEM majors at five large research ins>tu>ons. A8er controlling for factors known to relate to academic performance using opKmal matching, we find evidence of gendered performance differences in some STEM lecture courses that largely favor men. These results highlight the importance of encouraging all STEM faculty, not just those who focus on teaching or educa>on research, to adopt mul>modal methods of instruc>on so that all courses can be equitable spaces for all students to learn.
DEFINING THREE USEFUL MEASURES Grade point average in other courses (GPAO): a student’s cumula>ve GPA calculated based on all course enrollments except for the course of interest. GPAO is strongly correlated with course grades.1 Average grade anomaly (AGA): the average difference between final course grade and GPAO across all student enrollments in a course.
Posi>ve AGA à “grade bonus” Nega>ve AGA à “grade penalty”
Gendered performance difference (GPD): the difference in AGA between men and women.
Posi>ve GPD à favors women Nega>ve GPD à favors men
DATA COLLECTION § Twelve semesters (Fall 2008 to Spring 2014) of student record data for
introductory courses were collected at each of five large, research universi>es.
§ The data were limited to degree-‐seeking undergraduate students, including transfer students.
§ Variables collected included student ID, gender, ACT Mathema>cs and English subscores / SAT Mathema>cs and Verbal subscores, course name, term, course grade, and GPAO.
§ The overall data set includes 1,122,586 course enrollments across 249 courses in 13 disciplines (Tables 1 and 2).
Gendered Differences in Introductory STEM Courses are Consistent Across Five UniversiKes
Rebecca L. Matz1, Benjamin P. Koester2, Stefano Fiorini3, Galina Grom2, Linda Shepard3, Charles G. Stangor4, Brad Weiner5, Timothy A. McKay2 1CREATE for STEM Ins>tute and Hub for Innova>on in Learning and Technology, Michigan State U, 2Dept. of Physics, U of Michigan, 3Bloomington Assessment and Research, Indiana U, 4Dept. of Psychology, U of Maryland, 5Office of Undergraduate Educa>on, U of Minnesota (current affilia>on: Capture Higher Ed, Louisville, KY)
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Fig. 1. Gendered performance differences versus average grade anomaly for 172 introductory STEM courses, represen>ng 677,949 course enrollments, including lectures (gray), labs (orange), and mixed courses (blue).
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Fig. 2. Gendered performance differences versus average grade anomaly parsed by discipline for 22 (N = 160,828), 39 (N = 172,345), and 16 (N = 111,464) introductory non-‐STEM courses in accoun>ng and economics (gray); communica>on, poli>cal science, psychology, and sociology (blue); and wri>ng (orange).
Fig. 3. Differences between average GPAO (arrowtail) and average grade (arrowhead) for men (blue) and women (red) by academic year (e.g., 2008 indicates Fall 2008 and Spring 2009).
