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1. Factors Mediating Learning and Application of Computational Modeling by Life Scientists

   https://doi.org/10.1109/FIE.2018.8659328  

   Madamanchi, Aasakiran; Cardella, Monica E.; Glazier, James A.; Umulis, David M. (October 2018, 2018 IEEE Frontiers in Education Conference (FIE))

   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. 

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2. Introductory Engineering Courses With Computational Thinking: The Impact of Educational Privilege and Engineering Major Entry Policy on Student Pathways

   https://doi.org/10.1109/FIE56618.2022.9962501  

   Mendoza Diaz, Noemi V.; Trytten, Deborah A.; Meier, Russ (October 2022, 2022 IEEE Frontiers in Education Conference (FIE))

   This research category paper examines the impact of computational thinking within first-year engineering courses on student pathways into engineering. Computational thinking and programming appear in many introductory engineering courses. Prior work found that early computational thinking development is critical to the formation of engineers. This qualitative research paper extends the research by documenting how pre-university privileges impact first-year student trajectories into engineering through a qualitative examination of student interviews from three institutions with different processes for matriculation into engineering majors. We identify the underlying assumptions of meritocracy that are concealing the role of educational privilege in selecting which engineering students will be allowed to join the field. We provide a suggestion for how institutions can include computational thinking in introductory engineering courses with less risk of furthering the marginalization of students with few academic privileges. 

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3. Introductory Engineering Courses With Computational Thinking: The Impact of Educational Privilege and Engineering Major Entry Policy on Student Pathways

   Mendoza Diaz, Noemi V.; Trytten, Deborah A.; Meier, Russ (October 2022, 2022 IEEE Frontiers in Education Conference (FIE))

   This research category paper examines the impact of computational thinking within first-year engineering courses on student pathways into engineering. Computational thinking and programming appear in many introductory engineering courses. Prior work found that early computational thinking development is critical to the formation of engineers. This qualitative research paper extends the research by documenting how pre-university privileges impact first-year student trajectories into engineering through a qualitative examination of student interviews from three institutions with different processes for matriculation into engineering majors. We identify the underlying assumptions of meritocracy that are concealing the role of educational privilege in selecting which engineering students will be allowed to join the field. We provide a suggestion for how institutions can include computational thinking in introductory engineering courses with less risk of furthering the marginalization of students with few academic privileges. 

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4. Introductory Engineering Courses With Computational Thinking: The Impact of Educational Privilege and Engineering Major Entry Policy on Student Pathways

   Mendoza Diaz, Noemi; Trytten, Deborah A.; Meier, Russ (January 2022, Frontiers in education)

   This research category paper examines the impact of computational thinking within first-year engineering courses on student pathways into engineering. Computational thinking and programming appear in many introductory engineering courses. Prior work found that early computational thinking development is critical to the formation of engineers. This qualitative research paper extends the research by documenting how pre-university privileges impact first-year student trajectories into engineering through a qualitative examination of student interviews from three institutions with different processes for matriculation into engineering majors. We identify the underlying assumptions of meritocracy that are concealing the role of educational privilege in selecting which engineering students will be allowed to join the field. We provide a suggestion for how institutions can include computational thinking in introductory engineering courses with less risk of furthering the marginalization of students with few academic privileges. 

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5. Board 105: The Redshirt in Engineering Consortium: Progress and Early Insights

   Riskin, Eve; Milford, Jana; Callahan, Janet; Cosman, Pamela; Schneider, John; Pitts, Kevin; Knaphaus-Soran, Emily; Llewellyn, Donna; Delaney, Ann; Myers, Beth; et al (June 2018, ASEE annual conference & exposition proceedings)

   The NSF-funded Redshirt in Engineering Consortium was formed in 2016 with the goal of enhancing the ability of academically talented but underprepared students coming from low-income backgrounds to successfully graduate with engineering degrees. The Consortium takes its name from the practice of redshirting in college athletics, with the idea of providing an extra year and support to help promising engineering students complete a bachelor’s degree. The Consortium builds on the success of three existing “academic redshirt” programs and expands the model to three new schools. The Existing Redshirt Institutions (ERIs) help mentor and train the new Student Success Partners (SSP), and SSPs contribute their unique expertise to help ERIs improve existing redshirt programs. The redshirt model is comprised of seven main programmatic components aimed at improving the engagement, retention, and graduation of students underrepresented in engineering. These components include: “intrusive” academic advising and support services, an intensive first-year academic curriculum, community-building (including pre-matriculation summer programs), career awareness and vision, faculty mentorship, NSF S-STEM scholarships, and second-year support. Successful implementation of these activities is intended to produce two main long-term outcomes: a six-year graduation rate of 60%-75% for redshirt students, and increased rates of enrollment and graduation of Pell-eligible, URM, and women students in engineering at participating universities. In the first year of the grant (AY 16-17), SSPs developed their own redshirt programs, hired and trained staff, and got their programs off the ground. ERIs implemented faculty mentorship programs and expanded support to redshirt students into their sophomore year. In the second year (AY 17-18), redshirt programs were expanded at the ERIs while SSPs welcomed their first cohorts of redshirt students. This Work in Progress paper describes the redshirt programs at each of the six Consortium institutions, identifying distinctions between them in addition to highlighting common elements. First-year assessment results are presented for the ERIs based on student surveys, performance, and retention outcomes. Ongoing research into faculty experiences is investigating how participation as mentors for redshirt students changes faculty mindsets and instructional practices. Ongoing research into student experiences is investigating how the varied curricula, advising, and cohort models used across the six institutions influence student retention and sense of identity as engineering students. 

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