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In this Work In Progress in the Research Category we present a qualitative study that explores how computing instruction experiences may mediate the progression of social and cognitive factors associated with career development in community college engineering students. The rapidly evolving technological landscape has created an increased need for an engineering workforce with computing skills. Traditionally, policymakers, educators and researchers have focused on K-12 and four-year secondary institutions as the primary arena for addressing our workforce development needs. However, community colleges, which enroll over one-third of U.S. undergraduates, represent an important, and not yet fully utilized resource for training future engineers. However, there is a dearth of engineering education research that seeks to critically understand the experiences of community college engineering students in learning computational skills. To address this gap, our study includes semi-structured interviews with first- and second-year students enrolled in an engineering program at a public two-year institution in the midwestern United States. Our work is informed by a social cognitive career theory (SCCT) perspective from which we examine how engineering course learning experiences involving integrated computing instruction influence students’ self-efficacy beliefs and outcome expectations. In alignment with SCCT, our analysis will aim to identify personal attributes and external environmental factors that mediate students’ differential responses to computational instruction. This pilot study is the first part of a larger research effort directed towards learning how to support students as they navigate the community college pathway to engineering.

This Work in Progress paper in the Research category studies undergraduate students use of higher order metacognitive skills while engaging in computational modeling engineering research. Policymakers, universities and other key stakeholders have promoted ‘authentic learning experiences’ including undergraduate research experiences as a vehicle for the cognitive and metacognitive development of undergraduates. Indeed, undergraduate research experiences in STEM fields have been demonstrated to have positive effects including enhanced technical skills development and greater persistence in STEM degrees. However, specific research is needed to understand how metacognitive skills are engaged during undergraduate research experiences. Further, work on undergraduate research experiences has typically focused on science research, and relatively little work has been done to understand student development during research experiences in engineering and computational fields. To address this gap, we have initiated a qualitative study of undergraduate researchers within a computational modeling engineering research group at a large midwestern university. Here, we share preliminary results from semi-structured interviews of undergraduate student researchers and their supervisors. The research and implications of our work are grounded within a cognitive apprenticeship framework and will provide insight into how best to support the cognitive and metacognitive development of undergraduate engineering researchers.

This research study presents our work focused on studying the development of introductory computational thinking in undergraduate biomedical engineering students. In response to the growing computational intensity of the healthcare industry, biomedical engineering (BME) undergraduate education is starting to emphasize computation and computational thinking. Computational thinking is a way of thinking that uses concepts and methodologies of computing to solve problems in interdisciplinary and multidisciplinary subjects. In broader terms, computational thinking is not only associated with using computational tools but also with the thought process of solving a problem by data representation, problem decomposition, and algorithm design. Despite being so important, there is little research work or information available on the development of computational thinking in BME undergraduate students. Our research focuses on how BME undergraduate students develop computational thinking skills while performing group activities related to problem-solving. In order to conduct this study, we incorporate a teaching methodology that prompts computational thinking in a thermodynamics course being taught at a public mid-western university to approximately 120 sophomore students. We observe classroom activities involving analytical problem solving followed by pseudo code generation for computational coding. In order to investigate computational thinking, we collect classroom observations of small groups of students as they come up with a solution to an analytical problem with each other. We complement the observation notes of the classroom activities with follow up semi structured interviews with individual students from five groups. Thematic analysis of the student interviews is used in order to analyze student responses towards the incorporation of computation intensive teaching methodology. This Work in Progress helps us expand our understanding of computational thinking development and the challenges involved in performing computational thinking activity in BME undergraduate students.

This study explores interactions between computational thinking, gender and engineering identity among biomedical engineering undergraduate students. Biomedical engineering enjoys higher rates of women’s enrollment than other engineering disciplines, but nevertheless has gender disparities in persistence within the field. Additionally, trends towards greater incorporation of computation into biomedical engineering have the potential to recreate the gender inequities seen in more computationally intensive engineering disciplines. Recently, ‘engineering identity’ has emerged as a powerful analytic lens to understand persistence in engineering, particularly for underrepresented groups such as women. However, there is limited work examining how experiences using computational methods influences engineering identity formation in undergraduate biomedical engineers. Further, it remains unclear to what extent gender differentially mediates the effects of computational practice on engineering identity formation. In order to explore the intersection of these issues, we study a thermodynamics course in the biomedical engineering department of a large Midwestern public research institution in the United States. The thermodynamics course includes in-class computational modeling group activities and has an enrollment of more than 120, primarily sophomore year, undergraduate students. We use a qualitative study approach that includes gathering data through classroom observation and detailed semi-structured interviews. We analyze classroom observation data to try to understand student experiences of learning and participation during in-class computational modeling exercises. Specifically, we look for evidence of gendered differences in task sorting and engagement with the exercise. Classroom data is complemented by semi-structured interviews. Thematic analysis of semi-structured interviews gains student’s perspectives on how gender has influenced their learning experience and their identity as engineers.

This Work-in-Progress paper in the Research Category explores the unique challenges and opportunities of interdisciplinary education in computational modeling for life sciences student researchers at emerging research institutions (ERIs), specifically in predominantly undergraduate institutions (PUIs), and minority serving institutions (MSIs). Engineering approaches such as computational modeling have underappreciated potential for capacity building for the biomedical research enterprises of ERIs. We perform a bibliometric analysis to assess the prevailing use of computational modeling in life sciences research at MSIs, and PUIs. Additionally, we apply Social and Cognitive Theory to identify unique attitudinal, social and structural barriers for student researchers in learning and using computational modeling approaches at each of these types of institutions. Specifically, we use quantitative retrospective pre- and post-survey data and qualitative interviews of students who have attended a short-format computational modeling training course. We supplement these data with qualitative interviews of the students’ faculty sponsors. Upon completion, this study will provide deeper understanding of issues related to computer science and engineering education at non-Research I institutions.

This Work-in-Progress paper in the Research Category uses a retrospective mixed-methods study to better understand the factors that mediate learning of computational modeling by life scientists. Key stakeholders, including leading scientists, universities and funding agencies, have promoted computational modeling to enable life sciences research and improve the translation of genetic and molecular biology high-throughput data into clinical results. Software platforms to facilitate computational modeling by biologists who lack advanced mathematical or programming skills have had some success, but none has achieved widespread use among life scientists. Because computational modeling is a core engineering skill of value to other STEM fields, it is critical for engineering and computer science educators to consider how we help students from across STEM disciplines learn computational modeling. Currently we lack sufficient research on how best to help life scientists learn computational modeling.To address this gap, in 2017, we observed a short-format summer course designed for life scientists to learn computational modeling. The course used a simulation environment designed to lower programming barriers. We used semi-structured interviews to understand students’ experiences while taking the course and in applying computational modeling after the course. We conducted interviews with graduate students and post-doctoral researchers who had completed the course. We also interviewed students who took the course between 2010 and 2013. Among these past attendees, we selected equal numbers of interview subjects who had and had not successfully published journal articles that incorporated computational modeling. This Work-in-Progress paper applies social cognitive theory to analyze the motivations of life scientists who seek training in computational modeling and their attitudes towards computational modeling. Additionally, we identify important social and environmental variables that influence successful application of computational modeling after course completion. The findings from this study may therefore help us educate biomedical and biological engineering students more effectively.Although this study focuses on life scientists, its findings can inform engineering and computer science education more broadly. Insights from this study may be especially useful in aiding incoming engineering and computer science students who do not have advanced mathematical or programming skills and in preparing undergraduate engineering students for collaborative work with life scientists.