Skip to main content

Student–Faculty Interaction and Discrimination from Faculty in STEM: The Link with Retention

Abstract

Previous studies have documented student–faculty interaction in STEM, but fewer studies have specifically studied negative forms of interaction such as discrimination from faculty. Using a sample of 562 STEM undergraduates from the National Longitudinal Survey of Freshmen, we use hierarchical generalized linear modeling to investigate various types of student–faculty interaction in Science, Technology, Engineering, and Math (STEM) and in particular, the link between discrimination from faculty and retention in STEM. While Black students interacted more frequently with faculty, they were also most likely to report experiencing racial/ethnic discrimination. Overall, female, Black, and Latinx students were more likely to leave STEM by the fourth year of college than male, White, and Asian American peers. Feeling that professors made a student feel uncomfortable due to race/ethnicity was negatively linked with STEM retention. None of the traditional forms of student–faculty interaction (i.e., non-discriminatory) predicted retention. Variation in patterns by race, gender, and income are discussed, as well as implications for research, policy, and practice.

This is a preview of subscription content, access via your institution.

Notes

  1. Retention refers to the actions institutions take to promote the return of students from semester to semester, and to enhance the likelihood of students’ graduation. In contrast to retention, persistence, refers to the actions students take to continue their educational pursuits until degree completion. In this manuscript we focus on retention to place the responsibility of student success on the institution.

  2. In this study, we utilized the definition of STEM used by Department of Commerce (DOC) to identify STEM majors in our data. Then, based on the categorization of STEM majors by Sax et al. (2015), we grouped the STEM majors into five major categories (disciplines) for data analysis. The five categories and specific majors included in each category are as follows: Biological Sciences (Bio-chemistry, Biological Basis of Behavior, Biology); Computer Science (Computer Science); Engineering (Bio-engineering, Chemical Engineering, Civil Engineering, Electrical Engineering, Mechanical Engineering, Other Engineering); Mathematics/Statistics (Math, Actuarial Science); Physical Sciences (Chemistry, Material Science, Physics, Other Physical Science).

References

  • Astin, A. W., & Astin, H. S. (1992). Undergraduate science education: The impact of different college environments on the educational pipeline in the sciences. Los Angeles, CA: Higher Education Research Institute, University of California.

    Google Scholar 

  • Barnett, E. A. (2011). Validation experiences and persistence among community college students. The Review of Higher Education,34(2), 193–230.

    Google Scholar 

  • Beasley, M., & Fischer, M. J. (2012). Why they leave: The impact of stereotype threat on the attrition of women and minorities from science, math and engineering majors. Social Psychology of Education,15(4), 427–448.

    Google Scholar 

  • Bonous-Hammarth, M. (2000). Pathways to success: Affirming opportunities for science, mathematics, and engineering majors. Journal of Negro Education,69, 92–111.

    Google Scholar 

  • Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York: Greenwood Press.

    Google Scholar 

  • Bourdieu, P., & Wacquant, L. J. (1992). An invitation to reflexive sociology. Chicago, IL: University of Chicago press.

    Google Scholar 

  • Brief of 823 American Social Science Researchers as amici curiae in support of respondents, Fisher v. University of Texas II, 136 S.Ct. 2198 (2016).

  • Byars-Winston, A., Estrada, Y., Howard, C., Davis, D., & Zalapa, J. (2010). Influence of social cognitive and ethnic variables on academic goals of underrepresented students in science and engineering: A multiple-groups analysis. Journal of Counseling Psychology,57(2), 205–218.

    Google Scholar 

  • Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching,44(8), 1187–1218.

    Google Scholar 

  • Chang, M. J., Eagan, M. K., Lin, M. H., & Hurtado, S. (2011). Considering the impact of racial stigmas and science identity: Persistence among biomedical and behavioral science aspirants. The Journal of Higher Education,82(5), 564–596.

    Google Scholar 

  • Chang, M. J., Sharkness, J., Hurtado, S., & Newman, C. B. (2014). What matters in college for retaining aspiring scientists and engineers from underrepresented racial groups. Journal of Research in Science Teaching,51(5), 555–580.

    Google Scholar 

  • Cheng, X. (2013). STEM attrition: College students’ path into and out of STEM fields (NCES 2014-001). Washington, DC: National Center for Educational Statistics, Institute for Education Sciences, U.S. Department of Education.

    Google Scholar 

  • Cole, D. (2011). Debunking anti-intellectualism: An examination of African American college students’ intellectual self-concepts. The Review of Higher Education,34(2), 259–282.

    Google Scholar 

  • Cole, D., & Espinoza, A. (2008). Examining the academic success of Latino students in science, technology, engineering, and mathematics (STEM) majors. Journal of College Student Development,49(4), 285–300.

    Google Scholar 

  • Cole, D., & Griffin, K. A. (2013). Advancing the study of student-faculty interaction: A focus on diverse students and faculty. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (pp. 561–611). Netherlands: Springer.

    Google Scholar 

  • Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology,94 (Suppl.), 95–120.

    Google Scholar 

  • Comeaux, E. (2008). Black males in the college classroom: A quantitative analysis of student athlete-faculty interactions. Challenge: A Journal of Research on African American Men,14(1), 1–13.

    Google Scholar 

  • Crisp, G. (2010). The impact of mentoring on the success of community college students. The Review of Higher Education,34(1), 39–60.

    Google Scholar 

  • Crisp, G., Nora, A., & Taggart, A. (2009). 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. American Educational Research Journal,46(4), 924–942.

    Google Scholar 

  • Daempfle, P. A. (2003). An analysis of the high attrition rates among first year college science, math, and engineering majors. Journal of College Student Retention: Research, Theory & Practice,5(1), 37–52.

    Google Scholar 

  • DeAngelo, L. (2014). Programs and practices that retain students from the first to second year: Results from a national study. New Directions for Institutional Research,2013(160), 53–75.

    Google Scholar 

  • Dickey, C. A. (1996). The role of quality mentoring in the recruitment and retention of women students of color. Doctoral dissertation.

  • Diekman, A. B., Weisgram, E. S., & Belanger, A. L. (2015). New routes to recruiting and retaining women in STEM: Policy implications of a communal goal congruity perspective. Social Issues and Policy Review,9(1), 52–88.

    Google Scholar 

  • Dortch, D., & Patel, C. (2017). Black undergraduate women and their sense of belonging in STEM at predominantly White institutions. NASPA Journal About Women in Higher Education,10(2), 202–215.

    Google Scholar 

  • Eagan, K., Herrera, F. A., Garibay, J. C., Hurtado, S., & Chang, M. (2011). Becoming STEM Protégés: Factors predicting the access and development of meaningful faculty-student relationships. Los Angeles: Higher Education Research Institute.

    Google Scholar 

  • Ellington, R. (2006). Having their say: Eight high-achieving African-American undergraduate mathematics majors discuss their success and persistence in mathematics. Doctoral dissertation.

  • Flynn, D. (2014). Baccalaureate attainment of college students at 4-year institutions as a function of student engagement behaviors: Social and academic student engagement behaviors matter. Research in Higher Education,55(5), 467–493.

    Google Scholar 

  • Garibay, J. C. (2018). Beyond traditional measures of STEM success: Long-term predictors of social agency and conducting eesearch for social change. Research in Higher Education,59(3), 349–381.

    Google Scholar 

  • Garson, G. D. (2013). Path analysis. Asheboro, NC: Statistical Associates.

    Google Scholar 

  • Gayles, J. G., & Ampaw, F. (2014). The impact of college experiences on degree completion in STEM fields at four-year institutions: Does gender matter? The Journal of Higher Education,85(4), 439–468.

    Google Scholar 

  • Grandy, J. (1998). Persistence in science of high-ability minority students: Results of a longitudinal study. The Journal of Higher Education,69(6), 589–620.

    Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology,78(6), 1360–1380.

    Google Scholar 

  • Griffin, K. A., Pérez, D., Holmes, A. P., & Mayo, C. E. (2010). Investing in the future: The importance of faculty mentoring in the development of students of color in STEM. New Directions for Institutional Research,2010(148), 95–103.

    Google Scholar 

  • Higher Education Research Institute. (2010). Degrees of success: Bachelor’s degree completion rates among initial STEM majors. Los Angeles, CA: Higher Education Research Institute, University of California.

    Google Scholar 

  • Huang, G., Taddese, N., & Walter, E. (2000). Entry and persistence of women and minorities in college science and engineering education (NCES 2000-601). Washington, DC: National Center Educational Statistics, U.S. Department of Education.

    Google Scholar 

  • Hurtado, S., Cabrera, N. L., Lin, M. H., Arellano, L., & Espinosa, L. (2009). Diversifying science: Underrepresented student experiences in structured research programs. Research in Higher Education,50(2), 189–214.

    Google Scholar 

  • Hurtado, S., Eagan, M. K., Cabrera, N., Lin, M., Park, J., & Lopez, M. (2008). Training future scientists: Factors predicting underrepresented minority student participation in undergraduate research. Research in Higher Education,49(2), 126–152.

    Google Scholar 

  • Hurtado, S., Eagan, M.K., & Hughes, B. (2012, June). Priming the pump or the sieve: Institutional contexts and URM STEM degree attainments. Paper presented at the Annual Forum of the Association for Institutional Research, New Orleans, LA.

  • Hurtado, S., Eagan, M. K., Tran, M. C., Newman, C. B., Chang, M. J., & Velasco, P. (2011). “We do science here”: Underrepresented students’ interactions with faculty in different college contexts. Journal of Social Issues,67(3), 553–579.

    Google Scholar 

  • Hurtado, S., Han, J. C., Sáenz, V. B., Espinosa, L. L., Cabrera, N. L., & Cerna, O. S. (2007). Predicting transition and adjustment to college: Biomedical and behavioral science aspirants’ and minority students’ first year of college. Research in Higher Education,48(7), 841–887.

    Google Scholar 

  • Johnson, A. (2007). Unintended consequences: How science professors discourage women of color. Science Education,91(5), 805–821.

    Google Scholar 

  • Jones, M. T., Barlow, A. E. L., & Villarejo, M. (2010). Importance of undergraduate research for minority persistence and achievement in biology. The Journal of Higher Education,81(1), 82–115.

    Google Scholar 

  • Justin-Johnson, C. (2004). Good fit or chilly climate: An exploration of the persistence experiences of African-American women graduates of predominantly White college science programs. Doctoral dissertation.

  • Karakas, M. (2009). Cases of science professor’s use of nature of science. Journal of Science Education and Technology,18(2), 101–119.

    Google Scholar 

  • Kim, Y. K. (2010). Racially different patterns of student-faculty interaction in college: A focus on levels, effects, and causal directions. Journal of the Professoriate,3(2), 161–189.

    Google Scholar 

  • Kim, Y. K., Chang, M. J., & Park, J. J. (2009). Engaging with faculty: Examining rates, predictors, and educational effects for Asian American undergraduates. Journal of Diversity in Higher Education,2(4), 206–218.

    Google Scholar 

  • Kim, M. M., & Conrad, C. F. (2006). The impact of historically Black colleges and universities on the academic success of African-American students. Research in Higher Education,47(4), 399–427.

    Google Scholar 

  • Kim, Y. K., & Sax, L. J. (2009). Student–faculty interaction in research universities: Differences by student gender, race, social class, and first-generation status. Research in Higher Education,50(5), 437–459.

    Google Scholar 

  • Kim, Y. K., & Sax, L. J. (2011). Are the effects of student-faculty interaction dependent on academic major? An examination using multilevel modeling. Research in Higher Education,52(6), 589–615.

    Google Scholar 

  • Kim, Y. K., & Sax, L. J. (2014). The effects of student–faculty interaction on academic self-concept: Does academic major matter? Research in Higher Education,55(8), 780–809.

    Google Scholar 

  • Kim, Y. K., & Sax, L. J. (2015, November). The effect of positive faculty support on mathematical self-concept for male and female students in STEM majors. Paper presented at the Annual Meeting of the Association for the Study of Higher Education, Denver, CO.

  • Kim, Y. K., & Sax, L. J. (2017). The impact of college students’ interactions with faculty: A review of general and conditional effects. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (Vol. 32, pp. 85–139). Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Ko, L. T., Kachchaf, R. R., Ong, M., & Hodari, A. K. (2013). Narratives of the double bind: Intersectionality in life stories of women of color in physics, astrophysics and astronomy. Proceedings of the AIP Conference (Vol. 1513, No. 1, pp. 222–225).

  • Lee, J. D. (2002). More than ability: Gender and personal relationships influence science and technology involvement. Sociology of Education,75(4), 349–373.

    Google Scholar 

  • Lin, N. (2000). Inequality in social capital. Contemporary Sociology,29(6), 785–795.

    Google Scholar 

  • Lundberg, C. A., & Schreiner, L. A. (2004). Quality and frequency of faculty–student interaction as predictors of learning: An analysis by student race/ethnicity. Journal of College Student Development,45(5), 549–565.

    Google Scholar 

  • Madigan, T. (1997). Science proficiency and course taking in high school: The relationship of science course-taking patterns to increases in science proficiency between 8thand 12thgrades (NCES 97-838). Washington, DC: National Center for Educational Statistics, U.S. Department of Education.

    Google Scholar 

  • Mayhew, M. J., Rockenbach, A. B., Bowman, N. A., Seifert, T. A., Wolniack, G. C., Pascarella, E. T., et al. (2016). How college affects students: 21st century evidence that higher education works. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • McGee, E. O., & Martin, D. B. (2011). ‘‘You would not believe what I have to go through to prove my intellectual value!’’: Stereotype management among academically successful Black mathematics and engineering students. American Education Research Journal,48(6), 1347–1389.

    Google Scholar 

  • Moore, J. L. (2006). A qualitative investigation of African American males’ career trajectory in engineering: Implications for teachers, school counselors, and parents. Teachers College Record,108(2), 246–266.

    Google Scholar 

  • Museus, S. D., Palmer, R. T., Davis, R. J., & Maramba, D. (Eds.). (2011). Racial and ethnic minority student success in STEM education: ASHE higher education report, Volume 36, Number 6. Hoboken: Wiley.

    Google Scholar 

  • National Science Board. (2016). Science and engineering indicators 2016 (NSB 2016-1). Arlington, VA: National Science Foundation.

    Google Scholar 

  • Ong, M., Wright, C., Espinosa, L., & Orfield, G. (2011). Inside the double bind: A synthesis of empirical research on undergraduate and graduate women of color in science, technology, engineering, and mathematics. Harvard Educational Review,81(2), 172–209.

    Google Scholar 

  • Packard, B. W. L. (2015). Successful STEM mentoring initiatives for underrepresented students: A research-based guide for faculty and administrators. Sterling, VA: Stylus Publishing.

    Google Scholar 

  • Palmer, R. T., Maramba, D. C., & Dancy, T. E. (2011). A qualitative investigation of factors promoting the retention and persistence of students of color in STEM. The Journal of Negro Education,8(4), 491–504.

    Google Scholar 

  • Putnam, R. D. (2000). Bowling alone. The collapse and revival of American community. New York: Simon and Schuster.

    Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). Thousand Oaks, CA: Sage Publications Inc.

    Google Scholar 

  • Rios-Aguilar, C., Kiyama, J. M., Gravitt, M., & Moll, L. C. (2011). Funds of knowledge for the poor and forms of capital for the rich? A capital approach to examining funds of knowledge. School Field,9(2), 163–184.

    Google Scholar 

  • Russell, M. L., & Atwater, M. M. (2005). Traveling the road to success: A discourse on persistence throughout the science pipeline with African American students at a predominantly White institution. Journal of Research in Science Teaching,42(6), 691–715.

    Google Scholar 

  • Sandefur, R. L., & Laumann, E. O. (1998). A paradigm for social capital. Rationality and Society,10(4), 481–501.

    Google Scholar 

  • Sax, L. J. (2008). The gender gap in college: Maximizing the developmental potential of women and men. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Sax, L. J., Kanny, M. A., Riggers-Piehl, T. A., Whang, H., & Paulson, L. N. (2015). “But I’m not good at math”: The changing salience of mathematical self-concept in shaping women’s and men’s STEM aspirations. Research in Higher Education,56(8), 813–842.

    Google Scholar 

  • Seymour, E. (1995). The loss of women from science, mathematics, and engineering undergraduate majors: An explanatory account. Science Education,79(4), 437–473.

    Google Scholar 

  • Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduate leave the sciences. Boulder, CO: Westview Press.

    Google Scholar 

  • Shih, J. (2006). Circumventing discrimination: Gender and ethnic strategies in Silicon Valley. Gender & Society,20(2), 177–206.

    Google Scholar 

  • Soldner, M., Rowan-Kenyon, H., Inkelas, K. K., Garvey, J., & Robbins, C. (2012). Supporting students’ intentions to persist in stem disciplines: The role of living-learning programs among other social-cognitive factors. Journal of Higher Education,83, 311–336.

    Google Scholar 

  • Stanton-Salazar, R. (1997). A social capital framework for understanding the socialization of racial minority children and youths. Harvard Educational Review,67(1), 1–41.

    Google Scholar 

  • Stanton-Salazar, R. D., & Dornbusch, S. M. (1995). Social capital and the reproduction of inequality: Information networks among Mexican-origin high school students. Sociology of Education,68(2), 116–135.

    Google Scholar 

  • Stolle-McAllister, K. (2011). The case for summer bridge: Building social and cultural capital for talented Black STEM students. Science Educator,20(2), 12–22.

    Google Scholar 

  • Szelényi, K., Denson, N., & Inkelas, K. K. (2013). Women in STEM majors and professional outcome expectations: The role of living-learning programs and other college environments. Research in Higher Education,54(8), 851–873.

    Google Scholar 

  • Szelényi, K., & Inkelas, K. K. (2011). The role of living–learning programs in women’s plans to attend graduate school in STEM fields. Research in Higher Education,52(4), 349–369.

    Google Scholar 

  • Tate, E. D., & Linn, M. C. (2005). How does identity shape the experiences of women of color engineering students? Journal of Science Education and Technology,14(5–6), 483–493.

    Google Scholar 

  • Tovar, E. (2015). The Role of faculty, counselors, and support programs on Latino/a community college students’ success and intent to persist. Community College Review,43(1), 46–71.

    Google Scholar 

  • Vogt, C. M. (2008). Faculty as a critical juncture in student retention and performance in engineering programs. Journal of Engineering Education,97(1), 27–36.

    Google Scholar 

  • Vogt, C. M., Hocevar, D., & Hagedorn, L. S. (2007). A social cognitive construct validation: Determining women’s and men’s success in engineering programs. The Journal of Higher Education,78(3), 337–364.

    Google Scholar 

  • Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal,50(5), 1081–1121.

    Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Webber, K. L., Nelson Laird, T. F., & BrckaLorenz, A. M. (2013). Student and faculty member engagement in undergraduate research. Research in Higher Education,54, 227–249.

    Google Scholar 

  • Yeager, D. S., & Walton, G. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research,81, 267–301.

    Google Scholar 

  • Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race, Ethnicity and Education,8(1), 69–91.

    Google Scholar 

Download references

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 1660914.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julie J. Park.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 6.

Table 6 Variable definitions, coding schemes, and descriptive statistics

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Park, J.J., Kim, Y.K., Salazar, C. et al. Student–Faculty Interaction and Discrimination from Faculty in STEM: The Link with Retention. Res High Educ 61, 330–356 (2020). https://doi.org/10.1007/s11162-019-09564-w

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-019-09564-w

Keywords

  • Student–faculty interaction
  • Retention
  • STEM
  • Discrimination from faculty
  • Higher education outcomes