Paper ID #31072Work in Progress: Veterinary Medicine as a Context for Student Reasoningin a Mechanical Engineering Capstone Design CourseIsabella Stuopis, Tufts University PhD Candidate in Mechanical Engineering at Tufts University. Interests: undergraduate learning, learning outside of the classroom setting, collaboration in engineering, learning assistantsDr. Kristen B Wendell, Tufts University Kristen Wendell is Associate Professor of Mechanical Engineering and Adjunct Associate Professor of Education at Tufts University. Her research efforts at at the Center for Engineering Education and Out- reach focus on
National Science Foun- dation, on the board of the American Society for Engineering Education, and as an associate dean and director of interdisciplinary graduate programs. Her research awards include U.S. Presidential Early Ca- reer Award for Scientists and Engineers (PECASE), a National Science Foundation CAREER award, and two outstanding publication awards from the American Educational Research Association for her journal articles. All of Dr. Borrego’s degrees are in Materials Science and Engineering. Her M.S. and Ph.D. are from Stanford University, and her B.S. is from University of Wisconsin-Madison.Dr. David B Knight, Virginia Polytechnic Institute and State University David B. Knight is an Associate Professor and
Paper ID #29564WIP: Understanding Ambiguity in Engineering Problem SolvingMarah B. Berry, University of Florida Marah Berry is a PhD student at the University of Florida studying Environmental Engineering. Her re- search focuses on engineering problem solving. Her interest for problem solving began while she obtained her M.E. in Systems Engineering at the Pennsylvania State University.Dr. Elliot P. Douglas, University of Florida Elliot P. Douglas is Professor of Environmental Engineering Sciences and Engineering Education, and Distinguished Teaching Scholar at the University of Florida. His research interests are in
various research and development projects in industry and academia for more than 15 years.Dr. Nicholas B. Conklin, Gannon University Nicholas B. Conklin received a B.S. in applied physics from Grove City College in 2001, and a Ph.D. in physics from Penn State University in 2009. He is currently an associate professor and chair of the Physics Department at Gannon University, Erie, PA. c American Society for Engineering Education, 2020 Assessment and Analysis of Use of Self-Regulated Learning in Laboratory-Based Extracurricular Undergraduate/First-year Graduate Research ProjectsAbstract This paper in the Research category examines student use of the self
workforce.Dr. Joyce B. Main, Purdue University at West Lafayette Joyce B. Main is Associate Professor of Engineering Education at Purdue University. She received an Ed.M. in Administration, Planning, and Social Policy from the Harvard Graduate School of Education, and a Ph.D. degree in Learning, Teaching, and Social Policy from Cornell University. Dr. Main examines student academic pathways and transitions to the workforce in science and engineering. She was a recipi- ent of the 2014 American Society for Engineering Education Educational Research and Methods Division Apprentice Faculty Award, the 2015 Frontiers in Education Faculty Fellow Award, and the 2019 Betty Vetter Award for Research from WEPAN. In 2017, Dr. Main
. Retrieved from Washington, DC:Brubaker, E. R., Kohn, M., & Sheppard, S. (2019). Comparing outcomes of introductory makerspaces courses: The role of reflection and multi-age communities of practice. Paper presented at the International Symposium on Academic Makerspaces, New Haven, CT.Carbonell, R. M., & Andrews, M. E., & Boklage, A., & Borrego, M. J. (2019, June), Innovation, Design, and Self-Efficacy: The Impact of Makerspaces Paper presented at 2019 ASEE Annual Conference & Exposition, Tampa, Florida. https://peer.asee.org/32965Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Thousand Oaks, CA: Pine Forge Press.Fasso, W., & Knight, B. A. (2019
aerospace manufacturing sector, change wasidentified as an expectation, with one being “…ready to go to Plan B if Plan A is not available,and then move on to consider Plans C and D, and perhaps Plan E if circumstances dictate”[64]. In terms of Big Data and automation technologies in aircraft the need for the humans toadapt more fluidly are significant in the sense of changing and working through times of suddendisorder and uncertainty [65], [66]. Traditionally structured views of “the round peg goes intothe round hole… that there is only one answer to a question… these structures are moremalleable in modern operations. than we may want admit…ultimately the big data messinessconcept requires the human being to change in order to tap into and harness
Fall 2016 and Fall 2018 are presented below infigures 5a and 5b respectively. (a) Before Scaffolding (b) After Scaffolding Figure 4: Histogram of on-time submission ratios before and after scaffoldingWhen comparing the histograms in figure 5, the histogram shifts to the left, indicating a sharpdecrease in missing submissions after scaffolding. We also observe that over 70% of students hadmissed less than 10% of all submissions after scaffolding (Fall 2018).For in-depth analysis, we performed a test of the hypothesis (t-test) as well as Cohen’s effect size. (a) Before Scaffolding (b) After Scaffolding Figure 5: Histogram of missing submission
the identification and deconstruction ofmechanisms that hinder diverse pathways into engineering, promote a liberal approach toengineering education, and support individual diversity.References[1] J. B. Main, K. A. Smith, A. W. Fentiman, and K. L. Watson, “The next Morrill Act for the 21st century,” J. Eng. Educ., vol. 108, no. 2, pp. 152–155, 2019.[2] A. F. Mckenna, J. Froyd, and T. Litzinger, “The complexities of transforming engineering higher education: Preparing for next steps,” J. Eng. Educ., vol. 103, no. 2, pp. 188–192, 2014.[3] C. M. Campbell and K. A. O’Meara, “Faculty Agency: Departmental Contexts that Matter in Faculty Careers,” Res. High. Educ., vol. 55, no. 1, pp. 49–74, 2014.[4] S. Billett
University Fullerton, the Office of the Vice Provost for Graduate Education at Stanford University, the School of Medicine at Stanford University, and the School of Fisheries and Ocean Sciences at the University of Alaska, Fairbanks.Dr. Carol B. Muller, Stanford University Carol B. Muller is the Executive Director of WISE Ventures, an internal initiative at Stanford located in the Office of Faculty Development, designed to communicate, build networks, and help amplify existing and seed new and needed ventures across the Stanford campus to advance equity in science and engineer- ing. She also serves as executive director for Stanford’s Faculty Women’s Forum. A longtime university administrator, educator, and social
through college.Dr. Kristen B Wendell, Tufts University Kristen Wendell is Associate Professor of Mechanical Engineering and Adjunct Associate Professor of Education at Tufts University. Her research efforts at at the Center for Engineering Education and Out- reach focus on supporting discourse and design practices during K-12, teacher education, and college- level engineering learning experiences, and increasing access to engineering in the elementary school ex- perience, especially in under-resourced schools. In 2016 she was a recipient of the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE). https://engineering.tufts.edu/me/people/faculty/kristen- bethke-wendellProf. Chris Buergin
Paper ID #29752WIP: Exploring an Engineering Faculty’s Intention Toward InclusiveTeachingMemoria Matters, Purdue University at West Lafayette Memoria Matters is a PhD student in the School of Engineering Education at Purdue University. She is also pursuing a Master’s degree at the School of Electrical and Computer Engineering for computer engineering, in which she obtained her BSE from the University of Pennsylvania. Her research interest is in increasing the diversity of engineering by improving the inclusivity of engineering higher education through teaching methods, policies, and culture change.Dr. Carla B. Zoltowski
Roy, West Virginia University Abhik Roy is a professor educational psychology in the Department of Learning Sciences & Human Development (https://lshd.wvu.edu/) within the College of Education & Human Services at West Virginia University. Dr. Roy holds a Ph.D. in Program Evaluation with expertise in data science, visualization, and social network analysis and is an evaluator on multiple federal grants spanning both the National Science Foundation and the National Institutes of Health. He currently conducts research in (a) the use of machine learning to evaluate programs, (b) using predictive networks to assess change, and (c) deep learning architectures for text classification
impact might be captured longitudinally over the twelve-month period following theworkshop.References[1] J. A. Koenig, “Assessing 21st Century Skills: Summary of a Workshop,” 2015. [Online]. Available: https://www.learntechlib.org/p/159080/. [Accessed January 21, 2020].[2] V. Byrd, “Introducing Data Visualization: A Hands-on Approach for Undergraduates,” in Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Las Vegas, NV, USA, November 4-7, 2018, Association for the Advancement of Computing in Education (AACE), pp. 730-73, 2018.[3] B. T. Ladd, “The Information Frontiers Program: Expanding Student Capacity for Crossing Domain and Institutional Borders,” in
students in STEM. Journal of Research in Science Teaching, 54(2), 169–194. https://doi.org/10.1002/tea.21341[3] Collins, T. W., Grineski, S. E., Shenberger, J., Morales, X., Morera, O. F., & Echegoyen, L. E. (2017, May). Undergraduate Research Participation Is Associated With Improved Student Outcomes at a Hispanic-Serving Institution. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309399/.[4] Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G.,… Zavala, M. E. (2016). Improving underrepresented minority student persistence in stem. CBE Life Sciences Education, 15(3), 1–10. https://doi.org/10.1187/cbe.16-01-0038[5] Estrada, M., Hernandez, P. R
’ engineering identity, suggestions will be proposed forengineering staff to optimize the design of PBL curriculum and incorporate effective learningactivities to improve students’ teamwork experience.Reference[1] D. P. Dannels, “Learning to be professional,” Journal of Business and Technical Communication, vol.14, no. 1, pp. 5-37, 2000.[2] B. Johnson and R. Ulseth, “Development of professional competency through professional identityformation in a PBL curriculum,” in Proceedings - Frontiers in Education Conference, FIE, November, 2016,pp. 1–9. Available: https://doi.org/10.1109/FIE.2016.7757387[3] F. Dehing, W. Jochems, and L. Baartman, “Development of an engineering identity in the engineeringcurriculum in Dutch higher education: An exploratory study
University for reviewingthis paper and providing constructive feedback.References[1] W. Zhou and X. Shi, “Culture in groups and teams: A review of three decades of research,” Int. J. Cross Cult. Manag., vol. 11, no. 1, pp. 5–34, 2011.[2] A. S. Tsui, S. Nifadkar, and A. Y. Ou, “Cross-national, cross-cultural organizational behavior research: Advances, gaps, and recommendations,” J. Manage., vol. 33, no. 3, pp. 426–478, 2007.[3] S. Wei, D. M. Ferguson, M. W. Ohland, and B. Beigpourian, “Examining the cultural influence on peer ratings of teammates between international and domestic students,” in the American Society for Engineering Education Annual Conference & Exposition, 2019.[4] J. Wang, G. H.-L. Cheng, T
aims to provide national data and trends amongABET-accredited undergraduate engineering programs. (a) (b) Figure 2. (a) Summary of Retention Benchmarks (BM) 1 through 3, among student ethnicities and genders, legend is shown on the left and data points or fluctuations between the two years are not shown (b) Benchmark 4, showing interquartile ranges of BM1 through BM3. The bottom and top blue lines indicate lower and upper quartiles respectively, while the middle red lines indicate medianThe data from the first 3 benchmarks are summarized in Figure 2(a); it
C. Ifstudents apply the knowledge to a project, they are at a B grade level. Finally, if students achievehigh external value with their project, they will receive the grade of an A.Choosing a Team and TopicAs students decide on learning objectives, most of the learning is based around an innovationproject that teams choose. At the beginning of the semester, students look atcardiovascular-related funding opportunity announcements from agencies like National ScienceFoundation and National Institute of Health to determine projects of interest. From there, studentspitch project ideas and form teams based around the projects [20]. Students are not evaluatedbased on their ability to solve the problem presented in the funding opportunity
each behavior:1) student behavior without UORs, 2) instructors’ beliefs about students’ behavior without UORs,3) student behavior using UORs, and 4) instructors’ beliefs about students’ behavior using UORs(Fig 1a). Student and instructor responses for each item in List 2 were accrued (Fig 1b). (a) (b) (c)Fig. 1. Plots of survey results. (a) Histograms of student (left, red) and instructor (right, blue) responses for copying textbook homework solutions(List 1, question 7) without using UORs (top) and using UORs (bottom). The left and right vertical axes are normalized to the total number of validstudent and instructor surveys
expectedlearning outcomes mentioned above.To understand these tasks, let us describe a typical 2-day week: a) quiz on the reading of theweek followed by lecture with added examples on that topic (Tuesday); b) in-class activities(ICAs) where students practice the part of the UX process taught that week (Thursday); c)project group meetings with the facilitation of the TAs (in lieu of office hours). Lecture timeis augmented with complementary activities, such as ungraded polling questions (usingmentimeter.com [36]), real-world examples with some brief activity, and mini individualpresentations of good-bad-ugly UX examples (GBUX). In-class activities (ICAs) arecomplemented with sharing design artifacts to the whole class (using sharypic.com [37]) andmini
. Table 1: Treatment Group Test Matrix No. of No. of Group Activity Activity Grade Type of students sketches Course Instructor ID location frequency value students (n students) (n sketches) I-A Engineering Every Sophomore 16 91 A In class None Mechanics class - Senior II-B
and even third year, these labelings have both false positives and falsenegatives. Our study which seeks to identify, using a data science approach, a consistent wayto label all students as either retained or not retained, enjoys the following advantages: a) It does not rely on the requirement of earning a degree in engineering, b) It is not based on enrollment at a fixed point in time, and c) It can be used as the data set continues to grow.Using a data science pipeline, we analyze student enrollment gaps to determine a reasonablelabeling of not-retained. In the following, we start by describing our methods, then presentour findings and finish with a discussion and conclusions.MethodsA Data-driven Pipeline for Retention
situations wecreate, we may find additional paths forward.This paper is organized as follows. In the next section we offer three vignettes to concretize thework. We follow these vignettes with a traditional section devoted to related work. We thendescribe the activities that led to the findings section including (a) the context of the surveydevelopment efforts, (b) specific details of the survey development effort during a four monthperiod, and (c) the processes that led to the findings presented in this paper. Then, afterpresenting the findings, we turn to discussion and implications. MotivationAs part of our work on promoting reflection in engineering education, we have hadconversations with
learning objectives, instructional strategies, and assessments forsustainable infrastructure topics. Subsequent problem-based learning activities are being revisedand improved.AcknowledgmentsThis work was funded by the Scholarship of Teaching and Learning grant from the University ofNorth Carolina at Charlotte.References[1] A. Steinemann, "Implementing Sustainable Development through Problem-Based Learning:Pedagogy and Practice," Journal of Professional Issues in Engineering Education and Practice,vol. 129, no. 4, pp. 216-224, 2003, doi: 10.1061[2] S. A. Gallagher, B. T. Sher, W. J. Stepien, and D. Workman, "Implementing Problem-BasedLearning in Science Classrooms," School Science and Mathematics, vol. 95, no. 3, pp. 136-146,1995, doi: 10.1111/j
literature toidentify key components of the entrepreneurial mindset. As Figure 1 shows, this review was inthe disciplinary areas of a) education or learning theory b) engineering education and c) businessmanagement.Fig1: Disciplinary areas for literature reviewThe first stage yielded the identification of key components that define these three disciplinaryareas. These involved elements such as: risk tolerance, empathy, pro-activity, co-regulation, etc.We could find homologues in entrepreneurship and learning theory. Nonetheless, there weregaps as it related specifically to engineering education. Therefore, as Figure 2 shows, we neededto triangulate this information with sources derived from praxis like the perceptions of students,instructors, and
). “An overview of computational thinking,” International Journal of Computer Science Education in Schools, 3 (1) 1-11.[3] V. Shute, C. Sun, & J. Asbell-Clarke (2017). “Demystifying computational thinking’” Educational Research Review, 22, 142-158.[4] M. Berland & U. Wilensky (2015). “Comparing virtual and physical robotics environments for supporting complex systems and computational thinking,” Journal of Science Education and Technology 24(5), 628-647.[5] M. Bers, L. Flannery, E. Kazakoff, & A. Sullivan, (2014). “Computational thinking and tinkering: Exploration of an early childhood robotics curriculum,” Computers & Education 72, 145–157.[6] B. Zhone, Q. Wang, J. Chen, & Y. Li (2016). “An exploration of
measurestudents' responses to the types of instruction delivered in the undergraduate engineeringclassrooms [7]. It consists of three main sections and eleven subscales, as seen in Table I. TABLE I SECTIONS OF THE StRIP QUESTIONNAIRE AND ITS SUBSCALES Instrument Sections Subscales 1. Interactive 2. Constructive A. Types of instruction 3. Active Student 4. Passive response to B. Strategies for using in- 5. Explanation
Education, vol. 78, no. 7, pp. 674-681, 1988.[2] A. Kaw, R. Clark, E. Delgado, and N. Abate, "Analyzing the use of adaptive learning in aflipped classroom for preclass learning," Computer Applications in Engineering Education, vol.27, no. 3, pp. 663-678, 2019, doi: 10.1002/cae.22106.[3] G. Morgan, J.-M. Lowendahl, and T.-L. Thayer. Top 10 Strategic Technologies ImpactingHigher Education in 2016.[4] L. Yarnall, B. Means, T. Wetzel, “Lessons Learned from Early Implementations of AdaptiveCourseware,” SRI Education. SRI Project Nos. 21987 and 22997, 2016.
.2017.00236/full[5] C. Seron, S. S. Silbey, E. Cech, and B. Rubineau,“Persistence is cultural: Professional socialization and the reproduction of sex segregation,” Work and Occupations, vol. 43, no. 2, pp.178–214, 2015.[6] C. Seron, S.S. Silbey, E. Cech, and B. Rubineau, “‘I am not a feminist, but . . .’: Hegemony of a meritocratic ideology and the limits of critique among women in engineering,” Work and Occupations, vol. 45, no.2, pp.131-167, 2018.[7] B.W. Packard, J.L. Gagnon, O. LaBelle, K. Jeffers, and E. Lynn, “Women’s experiences in the STEM community college transfer pathway,” Journal of Women and Minorities in Science and Education, vol.17, no. 2, pp. 129-147, 2011.[8] P. Black and D. William