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1. Learning from small numbers: Studying ruling relations that gender and race the structure of U.S. engineering education

   https://doi.org/10.1002/jee.20247  

   Pawley, Alice L. ( February 2019 , Journal of Engineering Education)

   Women and men of color and White women participate in American engineering education in lower proportions than they represent in the general U.S. population. Much existing engineering education research uses individual‐level (such as psychological) theories to explain this difference. The study reported here instead takes a structural perspective, asking how social relations are coordinated in engineering education.

   This study explores how the intersection of ruling relations, critical race, and feminist theories can investigate how gender and race are built into engineering education's institutional structure.

   This study used interviews collected from 17 women and men of color and White women who were engineering undergraduate students at U.S. universities. The interviews were drawn from a project that takes as its premise that learning from such small numbers of students facilitates analyzing data intersectionally. The primary analysis used narrative methods through repeated readings.

   I offer empirically based illustrations of ruling relations in U.S. universities and schools of engineering that unduly impact minoritized populations. These illustrations include discussions of financial aid knowledge, meeting the needs of transfer and Native students, and how schools crafting “the ideal student” as a young, single White male problematically impact minoritized students. The results illustrate how ruling relations structure engineering education in White‐ and male‐dominated ways.

   This paper offers questions to help readers consider how ruling relations race and gender their own institutions. In addition, it offers an interpretive, emergent method for interrogating institutional structure and ideas for future work using ruling relations in engineering education research.

    

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2. The Centrality of Black Identity for Black Students in Engineering

   Mobley, Catherine ; Brawner, Catherine E. ; Brent, Rebecca ; Orr, Marisa K. ( January 2021 , Collaborative Network for Engineering and Computing Diversity (CoNECD) Conference)

   Introduction and Theoretical Frameworks Our study draws upon several theoretical foundations to investigate and explain the educational experiences of Black students majoring in ME, CpE, and EE: intersectionality, critical race theory, and community cultural wealth theory. Intersectionality explains how gender operates together with race, not independently, to produce multiple, overlapping forms of discrimination and social inequality (Crenshaw, 1989; Collins, 2013). Critical race theory recognizes the unique experiences of marginalized groups and strives to identify the micro- and macro-institutional sources of discrimination and prejudice (Delgado & Stefancic, 2001). Community cultural wealth integrates an asset-based perspective to our analysis of engineering education to assist in the identification of factors that contribute to the success of engineering students (Yosso, 2005). These three theoretical frameworks are buttressed by our use of Racial Identity Theory, which expands understanding about the significance and meaning associated with students’ sense of group membership. Sellers and colleagues (1997) introduced the Multidimensional Model of Racial Identity (MMRI), in which they indicated that racial identity refers to the “significance and meaning that African Americans place on race in defining themselves” (p. 19). The development of this model was based on the reality that individuals vary greatly in the extent to which they attach meaning to being a member of the Black racial group. Sellers et al. (1997) posited that there are four components of racial identity: 1. Racial salience: “the extent to which one’s race is a relevant part of one’s self-concept at a particular moment or in a particular situation” (p. 24). 2. Racial centrality: “the extent to which a person normatively defines himself or herself with regard to race” (p. 25). 3. Racial regard: “a person’s affective or evaluative judgment of his or her race in terms of positive-negative valence” (p. 26). This element consists of public regard and private regard. 4. Racial ideology: “composed of the individual’s beliefs, opinions and attitudes with respect to the way he or she feels that the members of the race should act” (p. 27). The resulting 56-item inventory, the Multidimensional Inventory of Black Identity (MIBI), provides a robust measure of Black identity that can be used across multiple contexts. Research Questions Our 3-year, mixed-method study of Black students in computer (CpE), electrical (EE) and mechanical engineering (ME) aims to identify institutional policies and practices that contribute to the retention and attrition of Black students in electrical, computer, and mechanical engineering. Our four study institutions include historically Black institutions as well as predominantly white institutions, all of which are in the top 15 nationally in the number of Black engineering graduates. We are using a transformative mixed-methods design to answer the following overarching research questions: 1. Why do Black men and women choose and persist in, or leave, EE, CpE, and ME? 2. What are the academic trajectories of Black men and women in EE, CpE, and ME? 3. In what way do these pathways vary by gender or institution? 4. What institutional policies and practices promote greater retention of Black engineering students? Methods This study of Black students in CpE, EE, and ME reports initial results from in-depth interviews at one HBCU and one PWI. We asked students about a variety of topics, including their sense of belonging on campus and in the major, experiences with discrimination, the impact of race on their experiences, and experiences with microaggressions. For this paper, we draw on two methodological approaches that allowed us to move beyond a traditional, linear approach to in-depth interviews, allowing for more diverse experiences and narratives to emerge. First, we used an identity circle to gain a better understanding of the relative importance to the participants of racial identity, as compared to other identities. The identity circle is a series of three concentric circles, surrounding an “inner core” representing one’s “core self.” Participants were asked to place various identities from a provided list that included demographic, family-related, and school-related identities on the identity circle to reflect the relative importance of the different identities to participants’ current engineering education experiences. Second, participants were asked to complete an 8-item survey which measured the “centrality” of racial identity as defined by Sellers et al. (1997). Following Enders’ (2018) reflection on the MMRI and Nigrescence Theory, we chose to use the measure of racial centrality as it is generally more stable across situations and best “describes the place race holds in the hierarchy of identities an individual possesses and answers the question ‘How important is race to me in my life?’” (p. 518). Participants completed the MIBI items at the end of the interview to allow us to learn more about the participants’ identification with their racial group, to avoid biasing their responses to the Identity Circle, and to avoid potentially creating a stereotype threat at the beginning of the interview. This paper focuses on the results of the MIBI survey and the identity circles to investigate whether these measures were correlated. Recognizing that Blackness (race) is not monolithic, we were interested in knowing the extent to which the participants considered their Black identity as central to their engineering education experiences. Combined with discussion about the identity circles, this approach allowed us to learn more about how other elements of identity may shape the participants’ educational experiences and outcomes and revealed possible differences in how participants may enact various points of their identity. Findings For this paper, we focus on the results for five HBCU students and 27 PWI students who completed the MIBI and identity circle. The overall MIBI average for HBCU students was 43 (out of a possible 56) and the overall MIBI scores ranged from 36-51; the overall MIBI average for the PWI students was 40; the overall MIBI scores for the PWI students ranged from 24-51. Twenty-one students placed race in the inner circle, indicating that race was central to their identity. Five placed race on the second, middle circle; three placed race on the third, outer circle. Three students did not place race on their identity circle. For our cross-case qualitative analysis, we will choose cases across the two institutions that represent low, medium and high MIBI scores and different ranges of centrality of race to identity, as expressed in the identity circles. Our final analysis will include descriptive quotes from these in-depth interviews to further elucidate the significance of race to the participants’ identities and engineering education experiences. The results will provide context for our larger study of a total of 60 Black students in engineering at our four study institutions. Theoretically, our study represents a new application of Racial Identity Theory and will provide a unique opportunity to apply the theories of intersectionality, critical race theory, and community cultural wealth theory. Methodologically, our findings provide insights into the utility of combining our two qualitative research tools, the MIBI centrality scale and the identity circle, to better understand the influence of race on the education experiences of Black students in engineering. 

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3. Metalearners for estimating heterogeneous treatment effects using machine learning

   https://doi.org/10.1073/pnas.1804597116  

   Künzel, Sören R. ; Sekhon, Jasjeet S. ; Bickel, Peter J. ; Yu, Bin ( February 2019 , Proceedings of the National Academy of Sciences)

   There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

    

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4. Student Behaviors and Interactions Influence Group Discussions in an Introductory Biology Lab Setting

   https://doi.org/10.1187/cbe.20-03-0054  

   Paine, Alex R. ; Knight, Jennifer K. ( December 2020 , CBE—Life Sciences Education)

   Gardner, Grant Ean (Ed.)

   Past research on group work has primarily focused on promoting change through implementation of interventions designed to increase performance. Recently, however, education researchers have called for more descriptive analyses of group interactions. Through detailed qualitative analysis of recorded discussions, we studied the natural interactions of students during group work in the context of a biology laboratory course. We analyzed multiple interactions of 30 different groups as well as data from each of the 91 individual participants to characterize the ways students engage in discussion and how group dynamics promote or prevent meaningful discussion. Using a set of codes describing 15 unique behaviors, we determined that the most common behavior seen in student dialogue was analyzing data, followed by recalling information and repeating ideas. We also classified students into one of 10 different roles for each discussion, determined by their most common behaviors. We found that, although students cooperated with one another by exchanging information, they less frequently fully collaborated to explain their conclusions through the exchange of reasoning. Within this context, these findings show that students working in groups generally choose specific roles during discussions and focus on data analysis rather than constructing logical reasoning chains to explain their conclusions. 

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5. Sister-girl Talk: A Community-based Method for Group Interviewing and Analysis

   https://doi.org/10.1177/1525822X221093824  

   Mitchell, Charlayne F. ; Ore, Ersula J. ; Wutich, Amber ; SturtzSreetharan, Cindi ; Brewis, Alexandra ; Davis, Olga I. ( May 2022 , Field Methods)

   Leveraging ground-breaking work of Black feminist scholars alongside established techniques of focus group and community-based participatory research, we explain sister-girl talk as a novel method for collecting and analyzing group interview data with Black women. We outline the procedures for consultation, facilitation and preliminary analysis of the sister-girl talk method. 

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