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1. (Work in Progress) Examining how students critically evaluate racial bias in a medical device in a first-year computing course

   Pangan, T. J.; Andrews, C. (August 2022, 2022 ASEE Annual Conference & Exposition)

   Engineering has historically been positioned as “objective” and “neutral” (Cech, 2014), supporting a technical/social dualism in which “hard” technical skills are valued over “soft” social skills such as empathy and team management (Faulkner, 2007). Disrupting this dualism will require us to transform the way that engineering is taught, to include the social, economic, and political aspects of engineering throughout the curriculum. One promising approach to integrating social and technical is through developing students’ critical sociotechnical literacy, supporting students in coming to “understand the intrinsic and systemic sociotechnical relationship between people, communities, and the built environment” (McGowan & Bell, 2020, p. 981). This work-in-progress study is part of a larger NSF-funded research project that explores integrating sociotechnical topics with technical content knowledge in a first-year engineering computing course. This course has previously focused on teaching students how to code, the basics of data science, and some applications to engineering. The revised course engages students in a series of sociotechnical topics, such as analyzing and interpreting data-based evidence of environmental racism. Each week, students read short articles and write reflections to prepare for in-class small group discussions. Near the end of the semester, students examined the topic of racial bias in medical equipment. Students read two popular news articles that discussed differences in accuracies of pulse oximeter readings for patients with different skin tones. We analyze students’ reflection responses for evidence of their developing sociotechnical literacy along three dimensions: (1) bias, (2) differential impact, and (3) responsibility. This exploratory case study employs thematic analysis (Braun & Clarke, 2006) to analyze the students’ written reflections for this topic. Students reflected on evidence of racial bias and potential causes of bias in the device, how this bias is located in and furthers historical patterns of racism in medicine, and considered who or what might be responsible for either causing or fixing the now-known racial bias. 

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2. How Do Students Take up Notions of Environmental Racism in an Engineering Computational Methods Course?

   https://doi.org/10.18260/1-2--43362  

   Ozkan, Desen; Hampton, Cynthia (June 2023, Annual ASEE Conference and Exhibition)

   Engineering departments have begun to prioritize more computational methods in their disciplines. Across engineering schools, computational methods are taught differently, but traditionally without context. In this study, we have revised an introductory engineering computing course such that students take up social, economic, and political contexts as they are introduced to coding and statistics. These contextual elements take three forms. The first is a weekly assignment where students read, reflect, and discuss various equity and justice-themed articles. The second is four weeklong projects over the semester that require a sociotechnical perspective to complete. Lastly, students complete an open-ended final project that requires attention to equity dimensions in each project step. This paper will examine the students’ responses to the weekly discussion reading on environmental racism. In this study, we focus on one week in which students read and reflected on two articles. One was an article from The Atlantic, titled “A New EPA Report Shows that Environmental Racism is Real” (Newkirk II, 2018). The other was an article from Vox titled, “There’s a clear fix to helping Black communities fight pollution” (Ramirez, 2021). The majority of students in this study are first-years enrolled in the school of engineering. The study takes place at a medium-sized, private, predominantly white institution in the northeast region of the US. Responses were collected across two years of this sociotechnical engineering revision. This study is not intended to compare the two years but to understand the breadth of ideas and responses students have in response to reading and reflecting on the article. Notably, two class sections of the course were revised in year one of the projects (2021), and all five sections of the course were revised in year two (2022). Each section is taught by a different engineering instructor. This study is not intended to compare students across different sections. Instead, through this qualitative thematic analysis, we attend to the different ways students take up and respond to social, political, and economic dimensions that have to do with the environment. 

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3. Characterizing student argument justifications in small group sociotechnical discussions

   https://doi.org/10.18260/1-2--43187  

   Andrews, Chelsea; Rahman, Fatima (June 2023, Annual ASEE Conference and Exhibition)

   There have been increased calls to include sociotechnical thinking–grappling with issues of power, history, and culture–throughout the undergraduate engineering curriculum. One way this more expansive framing of engineering has been integrated into engineering courses is through in-class discussions. There is a need to understand what students are attending to in these conversations. In particular, we are interested in how students frame and justify their arguments in small-group discussions. This study is part of an NSF-funded research project to implement and study integrating sociotechnical components throughout a first-year computing for engineers course. In one iteration of the revised course, each week students read a news article on a current example of the uneven impacts of technology, then engaged in in-class small-group discussions. In this study, we analyze students’ discourse to answer the research questions: What arguments do students use to argue against the use of a technology? How do these arguments relate to common narratives about technology? In this qualitative case study, we analyzed videorecordings of the small group discussions of two focus groups discussing the use of AI in hiring. We looked closely at the justifications students gave for their stated positions and how they relate to the common narratives of technocracy, free market idealism, technological neutrality, and technological determinism. We found all students in both groups rejected these common narratives. We saw students argue that (1) AI technology does not solve the hiring problem well, (2) it is important to regulate AI, (3) using AI for hiring will stagnate diversity, and (4) using AI for hiring unfairly privileges some groups of people over others. While students in both groups rejected the common narratives, only one group explicitly centered those who are harmed and how this harm would likely occur, and this group did so consistently. The other group managed to consistently reject the narratives using vague, safe language and never explicitly mentioned who is harmed by the technology. As a result, only one group’s discussion was clearly centered on justice concerns. These results have implications for how to scaffold small group sociotechnical discussions, what instructors should attend to during these discussions, and how to support students to orient toward systemic impacts and sustain a focus on justice. 

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4. Ten simple rules for creating and sustaining antiracist graduate programs

   https://doi.org/10.1371/journal.pcbi.1010516  

   Perez-Lopez, Edgar; Gavrilova, Larisa; Disla, Janice; Goodlad, Melissa; Ngo, Dalena; Seshappan, Arabi; Sharmin, Farhana; Cisneros, Jesus; Kello, Christopher T.; Berhe, Asmeret Asefaw (October 2022, PLOS Computational Biology)

   Schwartz, Russell (Ed.)

   In 2020, the combination of police killings of unarmed Black people, including George Floyd, Breonna Taylor, and Ahmaud Arbery, and the Coronavirus Disease 2019 (COVID-19) pandemic brought about public outrage over long-standing inequalities in society. The events of 2020 ignited global attention to systemic racism and racial inequalities, including the lack of diversity, equity, and inclusion in the academy and especially in science, technology, engineering, mathematics, and medicine (STEMM) fields. Racial and ethnic diversity in graduate programs in particular warrants special attention as graduate students of color report experiencing alarming rates of racism, discrimination, microaggressions, and other exclusionary behaviors. As part of the Graduate Dean’s Advisory Council on Diversity (GDACD) at the University of California Merced, the authors of this manuscript held a year-long discussion on these issues and ways to take meaningful action to address these persistent issues of injustices. We have outlined 10 rules to help graduate programs develop antiracist practices to promote racial and ethnic justice, equity, diversity, and inclusion (JEDI) in the academy. We focus on efforts to address systemic causes of the underrepresentation and attrition of students from minoritized communities. The 10 rules are developed to allow graduate groups to formulate and implement rules and policies to address root causes of underrepresentation of minoritized students in graduate education. 

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5. Poetry writing to enhance conceptual understanding of mathematical models and approaches for inventory management

   Akcali, Elif; Belay, Saron G.; Williams, Jade. (June 2023, 2023 ASEE Annual Conference & Exposition)

   To enhance conceptual understanding of mathematical models for inventory management, we developed poetry-writing assignments for a required, upper-level undergraduate course in an industrial and systems engineering program. Specifically, two poetry-writing assignments were incorporated into an inventory and supply chain system design and control course. The first assignment, due one week before the first term exam, asked students to write a poem about a concept, model or topic related to deterministic inventory modeling. The second and assignment, due one week before the second term exam, asked the students to write a poem about a concept, model or topic related to stochastic inventory modeling. The students were also asked to respond to several open-ended questions on their approach to writing the poems and their assessment of the impact of these poetry writing on improving their conceptual understanding of the underlying mathematical models. Data was collected in Spring 2022 semester. The student written-poetry will be analyzed for correctness and to identify misunderstandings or gaps in understanding. In this paper, we will present our findings from the content analysis of student-written poetry and our preliminary findings on the effectiveness of poetry-writing assignments to enhance conceptual understanding of mathematical models for inventory management. 

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