This past year, I had the privilege of working as a member of the interdisciplinary “Improving Undergraduate STEM Education” (IUSE) team that operated under the supervision of professors Dave Harding and Rodolfo Mendoza-Denton and Dr. Claudia von Vacano Ph.D. When I joined the team, one of our interests was in developing an analytic plan for comparing the educational outcomes of students enrolled in Data 8 to those enrolled in different gateway STEM courses at the University of California, Berkeley. This particular point of comparison was of interest because Data 8 was designed to assist students with gaining data science skills while requiring zero prerequisites and providing students with access to initiatives that were being implemented by the Data Science program, which give students opportunities to apply their data science knowledge in a variety of settings such as consulting, research projects, curriculum design, and instruction. For example, students could participate in the Discovery Research Program in which students apply and develop their skills in internships at Cal, in the government, community groups, and business settings. Another example is the Data Scholars Program, which is focused on uplifting students who have identities that are marginalized by society and providing them with a sense of community, research experiences, professional development, and academic support. Further, evidence from social psychology has demonstrated that these types of programs can assist with reducing the racial achievement gap by providing both non-marginalized and marginalized students with an understanding of how their skills can be applied in various work settings (Harackiewicz, Canning, Tibbetts, Priniski, & Hyde, 2016) and by cultivating a sense of community and belonging (Walton & Cohen, 2007).
However, before we could compare the outcomes of students enrolled in different gateway STEM courses, we needed to understand the nuances associated with Data 8 and the departmental context in which the course is embedded. For example, Data 8’s orientation to using data science skills to solve social issues is supported by social psychology findings that demonstrate when classes are structured in a way that allows students to see how course content can positively impact society has been associated with increases in deeper learning of materials and grades (Yeager, Henderson, Paunesku, Walton, D'Mello, Spitzer, & Duckworth, 2014). Meanwhile, a brief review of the Data Science program’s website provides the viewer with a variety of diverse student role models, which has been shown to have led to increased feelings of belonging for students who share the social identities of these role models in this domain (Covarrubias & Fryberg, 2015).
As we developed this type of understanding of Data 8 and the departmental context that it is associated with, we began comparing this understanding to different gateway STEM courses and their associated departmental contexts. Upon doing so, we quickly observed the significant differences between these gateway courses and their associated departmental contexts regarding the way they approach pedagogy, provide students with opportunities to apply their knowledge, and develop community and stances on social issues. These contrasts led us to begin considering how each of these contexts is shaped by larger social systems and how they can contribute to the development of identity-based (i.e., race, gender, social class, and first-generation college student status) achievement gaps across departments and potentially institutions (Stephens, Fryberg, Markus, Johnson, & Covarrubias, 2012 & Murphy, Kroeper, & Ozier, 2018). Moreover, these contrasts may further elucidate past research findings that demonstrated how UC Berkeley’s College of Chemistry was able to narrow the identity-based publication gap of their graduate students (Mendoza-Denton, Patt, Fisher, Eppig, Young, Smith, & Richards, 2017) in comparison to other STEM-related graduate programs by considering the behavioral impact that highly structured environments have on producing equitable student outcomes.
Although we started with developing a way to compare the educational outcomes of students enrolled in Data 8 to those enrolled in other gateway STEM courses at Cal, our collective interest evolved into understanding how differences in organizations’ structures might contribute to identity-based achievement gaps. While the current evidence seems to suggest that there may be a dynamic between structural factors and the psychological impacts that these factors have on subsequent behavioral outcomes, there isn’t a conceptual framework that has been established that links these two concepts.Along these lines, our team is currently in the process of developing a conceptual model that establishes this link so that we can begin understanding how differences in organizations’ structures can impact the educational outcomes of students from different backgrounds.
Covarrubias, R., & Fryberg, S. A. (2015). The impact of self-relevant representations on school belonging for Native American students. Cultural Diversity and Ethnic Minority Psychology, 21(1), 10–18.
Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J. S. (2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class. Journal of Personality and Social Psychology, 111(5), 745–765.
Mendoza-Denton, R., Patt, C., Fisher, A., Eppig, A., Young, I., Smith, A., & Richards, M. A. (2017). Differences in STEM doctoral publication by ethnicity, gender, and academic field at a large public research university. PloS one, 12(4), e0174296.
Murphy, M. C., Kroeper, K. M., & Ozier, E. M. (2018). Prejudiced Places: How Contexts Shape Inequality and How Policy Can Change Them. Policy Insights from the Behavioral and Brain Sciences, 5(1), 66–74.
Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C. S., & Covarrubias, R. (2012). Unseen disadvantage: How American universities' focus on independence undermines the academic performance of first-generation college students. Journal of Personality and Social Psychology, 102(6), 1178–1197.
Yeager, D. S., Henderson, M. D., Paunesku, D., Walton, G. M., D'Mello, S., Spitzer, B. J., & Duckworth, A. L. (2014). Boring but important: A self-transcendent purpose for learning fosters academic self-regulation. Journal of Personality and Social Psychology, 107(4), 559–580.
Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92(1), 82–96.