NEED: The majority of scientists featured in undergraduate educational resources do not reflect the diversity within the scientific community, nor do they match the identities of students reached by these resources (Wood et al. 2020). This mismatch contributes to stereotype threat, the framework used to interpret the behavior of members from stigmatized groups in situations where negative stereotypes exist (Steele & Aronson 1995; Gonzales et al. 2002). Stereotype threat in science courses reduces the academic performance and decreases the likelihood of pursuing science (Schmader et al. 2008). Stereotype boost theory (Shih et al. 2011) posits that increasing the visibility of successful scientists (i.e. role models) with diverse backgrounds will positively impact students who have been traditionally excluded from science. GUIDING QUESTION: In this study, we quantify how introducing diverse and humanized scientist role models into data literacy instruction affects student attitudes towards science. We investigated the following research questions: (1) Does diversifying and humanizing the scientists highlighted in active learning data literacy activities impact student interest in quantitative biology, interest in science careers, and scientific self-efficacy?; and (2) To what extent is this potential effect moderated by student identification with a historically excluded group? To answer these questions, thirty-four instructors at institutions across the United States implemented quantitative data literacy activities in their undergraduate biology courses during fall 2021. OUTCOMES: Preliminary analyses from the first semester found that the effect of highlighting diverse science role models during data literacy instruction depended on student racial identity (interest in quantitative literacy: χ2 = 6.86, p = 0.03; interest in science careers: χ2 = 6.20, p = 0.04; scientific self-efficacy: χ2 = 6.68, p = 0.04) but not on gender identity (interest in quantitative literacy: χ2 = 0.49, p = 0.78; interest in science careers: χ2 = 0.26, p = 0.88; scientific self-efficacy: χ2 = 3.90, p = 0.14). Specifically, quantitative biology interest among PEERs increased over the course of the semester by 0.22 ± 0.16 (mean ± standard error) along a 7-point Likert-scale when both a picture and an interview were incorporated into the data literacy activity. With only a picture of a diverse scientist added to the data literacy activity, we found that PEERs were less discouraged from pursuing a career in science. Furthermore, when science role models were not included, gains in scientific self-efficacy were smaller among PEERs compared to their white classmates. Scientific self-efficacy increased by only 0.18 ± 0.12 for PEERs versus 0.59 ± 0.11 for non-PEERs when diverse and humanizing elements were not incorporated into data literacy instruction. BROADER IMPACTS: The impact of diverse science role models differs across PEER and non-PEER students. To maintain interest in science and increase scientific self-efficacy, our results highlight the importance of diverse scientist role models during data literacy instruction and provide additional empirical support for the stereotype boost theory.
Emily Driessen, Auburn University, Auburn, AL; Melissa Kjelvik, Michigan State University, East Lansing, MI; Elizabeth Schultheis, Michigan State University, East Lansing, MI; Ash Zemenick, University of California, Berkeley, CA; Marjorie Weber, Michigan State University, East Lansing, MI; Cissy J. Ballen, Auburn University, Auburn, AL