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1. Experiential Learning in a Biomedical Device Engineering Course: Proposal Development and Raw Research Data-Based Assignments

   https://doi.org/10.1007/s43683-022-00094-z  

   Goshi, Noah; Girardi, Gregory; Kim, Hyehyun; Seker, Erkin (December 2022, Biomedical Engineering Education)

   Abstract There is a need for novel teaching approaches to train biomedical engineers that are conversant across disciplines and have the technical skills to address interdisciplinary scientific and technological challenges. Here, we describe a graduate-level miniaturized biomedical device engineering course that has been taught over the last decade in in-person, remote, and hybrid formats. The course employs experiential learning components, including a proposal development and review that mimic the National Institutes of Health process and technical assignments that use raw research data to simulate a research experience. The effectiveness of the course was measured via pre-/post-course concept inventory surveys as well as course evaluations with targeted questions on the learning instruments. Statistical comparison of pre-/post-course survey scores suggests that the course was effective in students achieving the learning objectives, and comparison of relative increase in pre-/post-course survey scores across different instruction formats (i.e., in-person, remote, hybrid) showed minimal difference, suggesting that the teaching elements are readily transferrable to remote instruction. 

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2. STEMAmbassadors: Developing Communications, Teamwork, and Leadership Skills for Graduate Students

   https://doi.org/10.18260/1-2--35207  

   Briliyanti, Astri; Rojewski, Julie; Colbry, Dirk; Luchini-Colbry, Katy (January 2020, Proceedings of the 2020 ASEE Annual Conference & Exposition)

   null (Ed.)

   STEM (science, technology, engineering, mathematics) graduate programs excel at developing students’ technical expertise and research skills. The interdisciplinary nature of many STEM research projects means that graduate students often find themselves paired with experts from other fields and asked to work together to solve complex problems. At Michigan State University, the College of Engineering has developed a graduate level course that helps students build professional skills (communications, teamwork, leadership) to enhance their participation in these types of interdisciplinary projects. This semester-long course also includes training on research mentoring, helping students work more effectively with their current faculty mentors and build skills to serve as mentors themselves. Discussions of research ethics are integrated throughout the course, which allows participants to partially fulfill graduate training requirements in the responsible conduct of research. This paper will discuss the development of this course, which is based in part on curriculum developed as part of an ongoing training grant from the National Science Foundation. 18 graduate students from Engineering and other STEM disciplines completed the course in Spring 2019, and we will present data gathered from these participants along with lessons learned and suggestions for institutions interested in adapting these open-source curriculum materials for their own use. Students completed pre- and post-course evaluations, which asked about their expectations and reasons for participating in the course at the outset and examined their experiences and learning at the end. Overall, students reported that the course content was highly relevant to their daily work and that they were highly satisfied with the content of all three major focus areas (communications, teamwork, leadership). Participants also reported that the structure and the pacing of the course were appropriate, and that the experience had met their expectations. The results related to changes in students’ knowledge indicate that the course was effective in increasing participants understanding of and ability to employ professional skills for communications, teamwork and leadership. Statistical analyses were conducted by creating latent constructs for each item as applicable and then running paired t-tests. The evaluation also demonstrated increases in students’ interest, knowledge and confidence of the professional skills offered in the course. 

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3. Creating a Modularized Graduate Curriculum in Chemical Engineering

   https://doi.org/10.18260/1-2--56162  

   Besterfield-Sacre, Mary; Dukes, April; Fullerton_Shirey, Susan; Veser, Götz (June 2025, ASEE Conferences)

   U.S. graduate engineering programs traditionally follow a “one-size-fits-all” approach that prioritizes research skills, is slow to adapt to industry trends, and defaults to training students for academic careers. Further, these programs implicitly assume that students start at the same knowledge level, disregarding differences in educational preparation and students’ backgrounds, including socioeconomic and sociocultural factors, prior work experience, and professional development. Through a National Science Foundation Innovations in Graduate Education award, the University of Pittsburgh Swanson School of Engineering is creating and validating a five-component personalized learning model (PLM) for graduate education within the Department of Chemical and Petroleum Engineering. This model, designed around the students' self-identified goals, aims to modernize graduate STEM education through a student-centered approach, advancing existing knowledge on the relationship between personalized learning and student outcomes. The first three components provide an intentional approach to learning: Instructional Goals developed for each student based on a learner profile and individual development plans (IDP), a purposeful Task Environment that breaks the traditional three-credit coursework into modules and co-curricular professional development streams, and a persistent approach to Scaffolding Instruction that leads to students’ mastery. The last two components provide feedback and reflection: Assessment of Performance Learning and Reflection and Evaluation. This paper reports on the methodology, results, and application of work conducted on the second component of the model, the Task Environment. This component purposefully breaks the traditional three-credit coursework into single-credit classes, specifically one-credit fast-paced fundamentals review, one-credit graduate-level core content, and one-credit graduate-level specialized content. This change provides a flexible and personalized learning experience. It enables students to customize their education to fit program requirements and align with their interests, thus allowing students to have agency on the breadth and depth of their intellectual development. To create the modularized curriculum, we initiated a collaborative process that leveraged the collective expertise of our chemical engineering faculty and external subject matter experts (SMEs), including chemical engineers from industry, government, academia, and start-ups. Starting with our faculty's existing learning objectives from each core course, we employed GroupWisdom group concept mapping software to (1) brainstorm on additional graduate-level chemical engineering learning objectives, (2) group the learning objectives into one of three levels: fundamental, graduate core, and specialized topics for each course topic, and (3) rate the importance of each learning objective. The multi-session group concept mapping technique leveraged 25 SMEs. Two sets of learning objectives were produced. The first was a prioritized set of learning outcomes for each content area organized according to the three levels. The second set comprised learning outcomes necessary for graduate students to be successful post-graduation, including technical and non-technical learning objectives. For the first set, faculty have formed a learning community to interpret the results and collectively work on restructuring course content and pedagogy. For the second set, the same SMEs rated the importance of each learning objective, which informed the priority of incorporation into the revised curriculum. 

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4. 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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5. Piloting A Personalized Learning Model for Chemical Engineering Graduate Education – Lessons Learned from Creating a Chemical Engineering Body of Knowledge

   https://doi.org/10.18260/1-2--54106  

   Dukes, April; Besterfield-Sacre, Mary; Fullerton_Shirey, Susan (February 2025, ASEE Conferences)

   Despite calls over the past twenty years to develop graduate STEM education models that prepare students for the new post-graduate workforce, few innovations in graduate STEM education have been disseminated. Given the diversity of graduate candidates’ prior skills, preparation, and individual career aspirations, modernizing STEM graduate programs is needed to support and empower student success in graduate programs and beyond. In this session, participants will learn about new research into the development and implementation of a personalized learning model (PLM) for graduate STEM education employed in a chemical engineering department at an R1 university. Our PLM integrates student-centered approaches in coursework, research, and professional development in multiple programmatic components. The key components of our PLM include guided student creation of independent development plans (IDPs), modularization of graduate courses and professional development streams, scaffolding curricular instruction to prioritize independence and mastery, using IDPs for directed research and career discussions and assessment of student performance and learning, and evaluation of the program from current students, alumni, faculty, and industry partners. Our comprehensive PLM plan is designed to maximize impact through personalized learning touchpoints throughout all aspects of graduate training. This presentation will focus on one element of our PLM, the modularization of the chemical engineering core graduate courses. To ensure the learning in core graduate courses reflects the diverse needs of chemical engineers, we generated a body of knowledge (BOK) for graduate chemical engineering in collaboration with our technical advisory board (TAB), which included chemical engineers and people that work with chemical engineers from industry, national labs, academia, and entrepreneurial representatives. We started by collecting the learning objectives (LO’s) from all core chemical engineering courses: Thermodynamics, Kinetics and Reactor Design, Transport Phenomena, Mathematical Methods, and Issues in Research and Teaching. The LO’s were refined by alignment with course assignments and activities and re-written using the most accurate Bloom’s Taxonomy verbs in collaboration with an instructional designer. We utilized GroupWisdomTM for group concept mapping of the new LO’s and provided an opportunity for the TAB to add new LO’s, identified by the individuals in the TAB to be critical for success in each member’s occupation. LO’s for the chemical engineering core courses were sorted on the level of knowledge (undergraduate, graduate, and specialized) and rated for importance by the TAB. Using GroupWisdom’sTM analytic tool for creating a similarity matrix for sorting the level of LO’s, multidimensional scaling, and hierarchical cluster analysis, all core course LO’s have been grouped into 6 clusters. These clusters, along with the current course list and the LO importance ratings, helped us visualize converting chemical engineering core courses into 1-credit hour modules. This restructured curriculum offers opportunities for ensuring that students have learned the requisite prior knowledge by review of essential undergraduate principles, streamlining essential graduate-level material, and supporting self-directed learning through the selection of specialized modules that align with students’ research and career goals. This proposed approach shifts core graduate chemical engineering education to an asset-based system, addressing knowledge gaps and ensuring rigorous, tailored learning experiences. 

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