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1. Defining First-generation and Low-income Students in Engineering: An Exploration

   https://doi.org/10.18260/1-2--34373  

   Atwood, Sara A ; Gilmartin, Shannon K ; Harris, Angela ; Sheppard, Sheri ( January 2020 , ASEE Annual Conference proceedings)

   First-generation (FG) and/or low-income (LI) engineering student populations are of particular interest in engineering education. However, these populations are not defined in a consistent manner across the literature or amongst stakeholders. The intersectional identities of these groups have also not been fully explored in most quantitative-based engineering education research. This research paper aims to answer the following three research questions: (RQ1) How do students’ demographic characteristics and college experiences differ depending on levels of parent educational attainment (which forms the basis of first-generation definitions) and family income? (RQ2) How do ‘first-generation’ and ‘low-income’ definitions impact results comparing to their continuing-generation and higher-income peers? (RQ3) How does considering first-generation and low-income identities through an intersectional lens deepen insight into the experiences of first-generation and low-income groups? Data were drawn from a nationally representative survey of engineering juniors and seniors (n = 6197 from 27 U.S. institutions). Statistical analyses were conducted to evaluate respondent differences in demographics (underrepresented racial/ethnic minority (URM), women, URM women), college experiences (internships/co-ops, having a job, conducting research, and study abroad), and engineering task self-efficacy (ETSE), based on various definitions of ‘first generation’ and ‘low income’ depending on levels of parental educational attainment and self-reported family income. Ourmore »results indicate that categorizing a first-generation student as someone whose parents have less than an associate’s degree versus less than a bachelor’s degree may lead to different understandings of their experiences (RQ1). For example, the proportion of URM students is higher among those whose parents have less than an associate’s degree than among their “associate’s degree or more” peers (26% vs 11.9%). However, differences in college experiences are most pronounced among students whose parents have less than a bachelor’s degree compared with their “bachelor’s degree or more” peers: having a job to help pay for college (55.4% vs 47.3%), research with faculty (22.7% vs 35.0%), and study abroad (9.0% vs 17.3%). With respect to differences by income levels, respondents are statistically different across income groups, with fewer URM students as family income level increases. As family income level increases, there are more women in aggregate, but fewer URM women. College experiences are different for the middle income or higher group (internship 48.4% low and lower-middle income vs 59.0% middle income or higher; study abroad 11.2% vs 16.4%; job 58.6% vs 46.8%). Despite these differences in demographic characteristics and college experiences depending on parental educational attainment and family income, our dataset indicates that the definition does not change the statistical significance when comparing between first-generation students and students who were continuing-generation by any definition (RQ2). First-generation and low-income statuses are often used as proxies for one another, and in this dataset, are highly correlated. However, there are unique patterns at the intersection of these two identities. For the purpose of our RQ3 analysis, we define ‘first-generation’ as students whose parents earned less than a bachelor’s degree and ‘low-income’ as low or lower-middle income. In this sample, 68 percent of students were neither FG nor LI while 11 percent were both (FG&LI). On no measure of demographics or college experience is the FG&LI group statistically similar to the advantaged group. Low-income students had the highest participation in working to pay for college, regardless of parental education, while first-generation students had the lower internship participation than low-income students. Furthermore, being FG&LI is associated with lower ETSE compared with all other groups. These results suggest that care is required when applying the labels “first-generation” and/or “low-income” when considering these groups in developing institutional support programs, in engineering education research, and in educational policy. Moreover, by considering first-generation and low-income students with an intersectional lens, we gain deeper insight into engineering student populations that may reveal potential opportunities and barriers to educational resources and experiences that are an important part of preparation for an engineering career.« less
2. Remote-Region Interior Alaska Community Survey with Two Statistical Measures: Arctic Indigenous Entrepreneurial Readiness (ER); and Remote-Region Digital Technology Needs and Skills (DT).

   https://doi.org/10.18739/A20R9M54S  

   Pasch, Timothy ; Kuhlke, Olaf ; Linton, Renee ( January 2021 )

   Abstract

   Between 2018 and 2021 PIs for National Science Foundation Awards # 1758781 and 1758814 EAGER: Collaborative Research: Developing and Testing an Incubator for Digital Entrepreneurship in Remote Communities, in partnership with the Tanana Chiefs Conference, the traditional tribal consortium of the 42 villages of Interior Alaska, jointly developed and conducted large-scale digital and in-person surveys of multiple Alaskan interior communities. The survey was distributed via a combination of in-person paper surveys, digital surveys, social media links, verbal in-person interviews and telephone-based responses. Analysis of this measure using SAS demonstrated the statistically significant need for enhanced digital infrastructure and reworked digital entrepreneurial and technological education in the Tanana Chiefs Conference region. 1. Two statistical measures were created during this research: Entrepreneurial Readiness (ER) and Digital Technology needs and skills (DT), both of which showed high measures of internal consistency (.89, .81). 2. The measures revealed entrepreneurial readiness challenges and evidence of specific addressable barriers that are currently preventing (serving as hindrances) to regional digital economic activity. The survey data showed statistically significant correlation with the mixed-methodological in-person focus groups and interview research conducted by the PIs and TCC collaborators in Hughes and Huslia, AK, which further corroborated stated barriers to entrepreneurship development in the region. 3. Data generated by the survey and fieldwork is maintained by the Tanana Chiefs Conference under data sovereignty agreements. The survey and focus group data contains aggregated statistical/empirical data as well as qualitative/subjective detail that runs the risk of becoming personally identifiable especially due to (but not limited to) to concerns with exceedingly small Arctic community population sizes. 4. This metadata is being provided in order to serve as a record of the data collection and analysis conducted, and also to share some high-level findings that, while revealing no personal information, may be helpful for policymaking, regional planning and efforts towards educational curricular development and infrastructural investment. The sample demographics consist of 272 women, 79 men, and 4 with gender not indicated as a response. Barriers to Entrepreneurial Readiness were a component of the measure. Lack of education is the #1 barrier, followed closely by lack of access to childcare. Among women who participated in the survey measure, 30% with 2 or more children report lack of childcare to be a significant barrier to entrepreneurial and small business activity. For entrepreneurial readiness and digital economy, the scales perform well from a psychometric standpoint. The summary scores are roughly normally distributed. Cronbach’s alphas are greater than 0.80 for both. They are moderately correlated with each other (r = 0.48, p < .0001). Men and women do not differ significantly on either measure. Education is significantly related to the digital economy measure. The detail provided in the survey related to educational needs enabled optimized development of the Incubator for Digital Entrepreneurship in Remote Communities. Enhanced digital entrepreneurship training with clear cultural linkages to traditions and community needs, along with additional childcare opportunities are two among several specific recommendations provided to the TCC. The project PIs are working closely with the TCC administration and community members related to elements of culturally-aligned curricular development that respects data tribal sovereignty, local data management protocols, data anonymity and adherence to human subjects (IRB) protocols. While the survey data is currently embargoed and unable to be submitted publicly for reasons of anonymity, the project PIs are working with the NSF Arctic Data Center towards determining pathways for sharing personally-protected data with the larger scientific community. These approaches may consist of aggregating and digitally anonymizing sensitive data in ways that cannot be de-aggregated and that meet agency and scientific community needs (while also fully respecting and protecting participants’ rights and personal privacy). At present the data sensitivity protocols are not yet adapted to TCC requirements and the datasets will remain in their care.
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3. Intersectionality of Non-normative Identities in the Cultures of Engineering

   https://doi.org/10.18260/p.25448  

   Kirn, Adam ; Godwin, Allison ; Benson, Lisa ; Potvin, Geoff ; Doyle, Jacqueline ; Boone, Hank ; Verdin, Dina ( June 2016 , ASEE Annual Conference & Exposition)

   Traditionally, engineering culture has limited rather than fostered diversity in engineering. To address this persistent issue, we examine how diverse students identify with engineering and navigate the culture of engineering. We define diversity not by making a priori categorizations according to traditional demographic information (e.g., race, gender, sexual orientation, etc.), but instead by investigating the variation in students’ attitudinal profiles on a host of affective measures. Using these measures, we develop an identification of large, “normative” groups of engineers as well as “non-normative” students who emerge as having distinct attitudinal profiles. This mixed methods study investigates the intersectionality of engineering students' personal identities to understand: How do non-normative groups in engineering form an engineering identity and navigate a culture dominated by limited diversity? The focus of this paper is on the first phase this project, in which students' identities, motivation, psychological traits, perceived supports and barriers to engineering, and other background information is being quantitatively assessed. Pilot survey data were collected from participants enrolled in second semester first-year engineering programs across three institutions (n=374). We used topological data analysis (TDA) to create normative and non-normative attitudinal profiles of respondents. As a relatively new and powerful set of analytic methods, TDAmore »clusters variegated data to understand an underlying structure, or topology, which emerges from the data. Our preliminary results show definite patterns which we then break down according to students' self-identified demographics. Additionally, a subset of participants who completed our quantitative instrument were interviewed about their experiences in and identification with engineering (n=7). Initial qualitative data analysis indicate that students who reside at intersectional boundaries of diversity have difficulty finding similar role models in engineering and often find themselves expending additional effort when compared to their peers to establish themselves in both engineering and non-engineering communities. Results of this quantitative and qualitative work were used to further refine the quantitative instrument that is to be used in subsequent phases of the project.« less
4. Revealing the queer-spectrum in STEM through robust demographic data collection in undergraduate engineering and computer science courses at four institutions

   https://doi.org/10.1371/journal.pone.0264267  

   Casper, A. M. ; Atadero, Rebecca A. ; Fuselier, Linda C. ( March 2022 , PLOS ONE)

   Grinnell, Frederick (Ed.)

   Queer identities are often ignored in diversity initiatives, yet there is a growing body of research that describes notable heterosexist and gender-normative expectations in STEM that lead to unsupportive and discriminatory environments and to the lower persistence of queer individuals. Research on the experiences of queer-spectrum individuals is limited by current demographic practices. In surveys that are queer-inclusive there is no consensus on best practices, and individuals with queer genders and queer sexual, romantic, and related orientations are often lumped together in a general category (e.g. LGBTQ+). We developed two queer-inclusive demographics questions and administered them as part of a larger study in undergraduate engineering and computer science classes (n = 3698), to determine which of three survey types for gender (conventional, queered, open-ended) provided the most robust data and compared responses to national data to determine if students with queer genders and/or queer sexual, romantic, and related orientations were underrepresented in engineering and computer science programs. The gender survey with queer-identity options provided the most robust data, as measured by higher response rates and relatively high rates of disclosing queer identities. The conventional survey (male, female, other) had significantly fewer students disclose queer identities, and the open-ended survey hadmore »a significantly higher non-response rate. Allowing for multiple responses on the survey was important: 78% of those with queer gender identities and 9% of those with queer sexual, romantic and related orientations selected multiple identities within the same survey question. Queer students in our study were underrepresented relative to national data. Students who disclosed queer gender identities were 7/100ths of the expected number, and those with queer orientations were under-represented by one-quarter. Further work developing a research-based queered demographics instrument is needed for larger-scale changes in demographics practices, which will help others identify and address barriers that queer-spectrum individuals face in STEM.« less
5. Profiles of Language Brokering Experiences and Contextual Stressors: Implications for Adolescent Outcomes in Mexican Immigrant Families

   https://doi.org/10.1007/s10964-018-0851-4  

   Kim, Su Yeong ; Hou, Yang ; Song, Jiaxiu ; Schwartz, Seth J. ; Chen, Shanting ; Zhang, Minyu ; Perreira, Krista M. ; Parra-Medina, Deborah ( January 2018 , Journal of Youth and Adolescence)

   Adolescents from Mexican immigrant families are often embedded in a challenging social environment and experience multiple contextual stressors, including economic stress, discrimination, and foreigner stress. We consider how the effects of these contextual stressors may be amplified or diminished for adolescents who function as language brokers, interpreting and mediating for their English-limited parents. Using two waves of survey data collected from a sample (N = 604 at Wave 1; N = 483 at Wave 2) of Mexican American adolescents with ages ranging from 11 to 15 (Mage = 12.41, 54% female), four distinct brokering – stress profiles were identified. Latent profile analyses revealed that with moderate levels of contextual stress, adolescents with more positive language brokering experiences (protective group) demonstrated more favorable outcomes than those with neutral language brokering experiences (moderate group) and those who did not involve themselves as frequently in language brokering activities (less-involved group). In contrast, high levels of contextual stress, coupled with more negative language brokering experiences (risk group), produced the least favorable outcomes among adolescents.