CAREER: Building a Model of Instructional Congruence through Exploring the Role of Language in Introductory Undergraduate Engineering Courses

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Introductory engineering courses may seem alienating to many undergraduates when fundamental concepts are presented primarily through highly technical language, unfamiliar metaphors and analogies, and complex abstractions. This sense of alienation may be particularly acute when students’ educational backgrounds, prior experiences, and linguistic practices are not reflected in engineering instructors’ written or verbal examples and course materials. To counter this sense, the purpose of this project is to develop a model of instructional congruence that connects undergraduates’ linguistic practices and experiences with core engineering concepts. This project focuses on undergraduates that have historically been marginalized in engineering courses due to their socioeconomic status, race, ethnicity, or language, including people who identify as African American or Black and Hispanic or Latiné. This project is based on the fundamental assumption that language can function to build a sense of belonging in introductory undergraduate engineering courses when instructors encourage the use of students’ home languages; when they use everyday language to build understandings of complex disciplinary ideas and practices; and when they connect students’ experiences with fundamental engineering concepts through familiar analogies, metaphors, or examples. To explore this assumption, the project will conduct research in introductory electrical engineering courses at five universities, including a Hispanic-Serving Institution, a Historically Black Research University, and Predominantly White Institutions with intentionally inclusive engineering courses. The project will result in an empirically-based, open-access, scalable, and nationally-disseminated model that can be used by undergraduate engineering instructors who seek to build their students’ disciplinary expertise and enhance their sense of belonging in engineering through instructionally congruent approaches. This project will explore how various types of language can support undergraduates in developing disciplinary expertise through building connections between familiar examples and fundamental engineering concepts. To achieve this research purpose, a multi-phase discourse analysis will be performed in introductory engineering courses across five colleges or universities, which were purposively selected to ensure diversity among students, instructors, programs, and institution types. Within this context, the research team will collect numerous data sources, including written course materials, protocols from classroom observations, and transcripts from pre- and post-focus groups with undergraduates. Additionally, the research team will interview the instructors to better understand their rationales behind using specific types of language, including analogies and metaphors, to support student learning. Qualitative content analyses of these data sources will result in a model of instructional congruence that is widely transferrable to institutions and programs across the country. This model will be shared through research publications, workshops, webinars, and a graduate course for instructional faculty. Collectively, these resources will result in actionable knowledge regarding how instructors can use language to foster disciplinary expertise in undergraduate engineering courses, while simultaneously enhancing students’ sense of belonging in engineering. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.