Paper ID #38318Board 223: Broadening Participation in Engineering via the TransferStudent Pathway: Findings from an S-STEM-Enabled PartnershipDr. David B. Knight, Virginia Tech David Knight is an associate professor in the Department of Engineering Education at Virginia Tech. He also serves as Special Assistant to the Dean for Strategic Plan Implementation and Director of Research of the Academy of Global Engineering. His research tends to be at the macro-scale, focused on a systems- level perspective of how engineering education can become more effective, efficient, and inclusive, and considers the intersection between
major not within the Engineering Department). As this is a technical areaof active development, an effort is made to incorporate new advances into the lecture material ina timely manner. The author intends to continue offering the course on an annual basis. 3 References 1. Principles of Sustainable Energy Systems, Third Edition Charles F. Kutscher, Jana B. Milford, Frank Kreith CRC Press 2019; ISBN-13 978-1-4987-8892-2 2. Spacecraft Power Systems Mukund R. Patel CRC Press 2005; ISBN 978-0-367-47693-93. Wind and Solar Power Systems; Design, Analysis, and Operation Mukund R. Patel and Omid Beik CRC Press 2021; ISBN 978-0-367-47693-94
Paper ID #38917Applying a Competency-Based Education Approach for Designing a UniqueInterdisciplinary Graduate Program: A Case Study for a SystemsEngineering ProgramDr. Amy Thompson, University of Connecticut Dr. Amy Thompson joined UConn in August 2017 as an Associate Professor-In-Residence of Systems Engineering and as the Associate Director for the Institute for Advanced Systems Engineering at the University of Connecticut. She currently teaches graduate-level engineering courses in model-based sys- tems engineering and systems engineering fundamentals, and coordinates the online graduate programs in Advanced Systems
Paper ID #37229Work in Progress: Emotional Configurations in Undergraduate EngineeringEducationEmily Kostolansky, Tufts University Emily Kostolansky is a master’s student in mechanical engineering at Tufts University. Her research inter- ests in engineering education include undergraduate engineering education and emotions in engineering.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
. Ghaffari, and R.-C. Mihailescu, “An Optimization Model for Group Formation in Project-based Learning,” Proceedings of the Annual Hawaii International Conference on System Sciences, 2020, doi: https://doi.org/10.24251/hicss.2020.009.[14] D. Lambić, B. Lazović, A. Djenić, and M. Marić, “A novel metaheuristic approach for collaborative learning group formation,” Journal of Computer Assisted Learning, vol. 34, no. 6, pp. 907–916, Aug. 2018, doi: https://doi.org/10.1111/jcal.12299.[15] F. S. Hillier and G. J. Lieberman, Introduction to operations research. New York, Ny: Mcgraw-Hill, 2021, pp. 339-340.
metacognition for independent learning and team-based learning, and in-class collaborations between student cohorts in engineering courses.Dr. Neha B. Raikar, University of Maryland, Baltimore County Dr. Raikar is a Lecturer at the University of Maryland, Baltimore County in the Chemical, Biochemi- cal, and Environmental Engineering department. She has taught both undergraduate and graduate-level courses. Dr. Raikar also has 3 years of industry experience from working at Unilever Research in the Netherlands. ©American Society for Engineering Education, 2023 Work-In-Progress: Using senior peer mentoring for experiential learning of core chemical engineering topics
extrinsic. Intrinsic barriers arepersonal to oneself, while extrinsic barriers stem from factors outside the researcher. The termbarrier implies an unyielding obstacle that cannot be overcome. In reality, many obstacles couldbe considered "permeable" if opportunities (or "inlets") are carefully sought by the motivatedengineering education researcher. Figure 1. Summary of authors' barriers to enter EERAmong the researcher's intrinsic barriers include: (a) level of motivation, (b) time constraints toacquire sufficient knowledge to submit competitive grant proposals, and (c) lack of interimresearch productivity while learning EER. Even if engineering faculty can overcome theirintrinsic barriers, they may face extrinsic
design techniques enhances engineers understanding of users’ needs. 2. Bairaktarova, D. (2022). Caring for the future: Empathy in engineering education to empower learning. 3. Bernárdez, B., Durán, A., Parejo, J. A., Juristo, N., & Ruiz–Cortés, A. (2022). Effects of Mindfulness on Conceptual Modeling Performance: A Series of Experiments. 4. Carbonetto, T., & Grodziak, E. M. (2019, July 28). Mindfulness in Engineering v2. 5. Estrada, T., & Dalton, E. (2019). Impact of Student Mindfulness Facets on Engineering Education Outcomes: An Initial Exploration. 6. Hess, J. L., Beever, J., Strobel, J., & Brightman, A. O. (2017). Empathic Perspective- Taking and Ethical Decision-Making in
video production affects student en-gagement: an empirical study of MOOC videos. L@S’14 Proceedings of the First ACM Conference on Learning at Scale, New York: ACM, 41–50.10. deKoning B, Tabbers H, Rikers R, Paas F (2009). Towards a framework for attention cueing in instructional animations: guidelines for research and design. Educ Psychol Rev 21, 113– 140.11. J. Mike Walker '66 Department of Mechanical Engineering. Improving Polymer Devolatilization Technology in Industry. (Feb. 24, 2023). Accessed: Feb. 28, 2023. [Online Video]. Available: https://youtu.be/Ce5XhwU7D4c12. J. Mike Walker '66 Department of Mechanical Engineering. Reversing Irradiation Effects of Inconel 718 Beamline Window. (Feb. 24, 2023). Accessed: Feb. 28
based on average test scoreswith partial credit. In my implementation, the course topics were grouped into categoriescorresponding to grade levels D, C, B, and A. Each category has 3-5 topics [D1-D5, C1-C4, B1-B3, and A1-A3], and the corresponding grade is earned if a short test for each topic (or in somecases, a pair of topics) in the category is “Approved”. A grade of Approved is earned fordemonstrating A-quality work (only minor errors permitted). A grade of “ConditionallyApproved” is earned for demonstrating B-quality work, with full approval being earned throughwriting corrections. A grade of “Not Yet Approved” is assigned for demonstrating C or lowerquality work, with full approval requiring another test of the same topic to be taken. In
latter group was found to have higher finalexam grades implying greater improvement.Juhler et al. [7] examined the test and retest scores for 1,314 students who completed anintermediate algebra course. For each of seven chapter tests, if the student achieved less than a Bgrade, they could opt to take a retake. The score on the retake replaced the original test score,regardless of whether it was an improvement, but was limited to a B grade. On average, studentswere eligible to take 5.30 retakes and opted to take 2.31 retakes. The majority (88-95%) ofstudents who took the retake improved their score. However, there was no significant correlationbetween the number of retakes and the final exam score.Abraham [8] offered 150 students in intuitive
original lab on your own before attempting this quiz.You are allowed to run the Wireshark while completing this lab.The following questions are similar to Network+ type of questions and are relate to trace named:http-ethereal-trace-1.1. If you set the http filter, how many packets you will see: a. 3 b. 4 c. 5 d. 62. If you set the SNMP filter, how many packets you will see: a. 3 b. 4 c. 5 d. 63. For HTTP packet number 10 (Frame 10), ), the total size of the packet is: a. 555 b. 439 c. 541 d. 13954. For HTTP packet number 10 (Frame 10), the requesting user agent is: a. User-Agent: Mozilla/5.0 b. User-Agent: Firefox/5.0 c. User-Agent: Chrome/5.0 d. User
velocity. It should be noted that for this project, only two-dimensional motion is captured, however stereographic PIV can extend this work into threedimensions by making use of a second camera. c b a d eFig. 1 Acrylic Tank Experimental Setup (a-laser, b-concaved lens housing, c-acrylic tank, d-laser sheet and seeding particles, e-submersible pump)An initial experiment, shown in figure 1, was set up to verify the equipment selected was capableof illuminating a specific area and capturing the chosen particles. This work is similar
resubmission of work and flexible deadlines,” in 2003 GSW, 2021. [3] M. L. Amyx, K. B. Hastings, E. J. Reynolds, J. A. Weakley, S. Dinkel, and B. Patzel, “Management and treatment of attention-deficit/hyperactivity disorder on college campuses,” Journal of Psychosocial Nursing and Mental Health Services, vol. 53, no. 11, pp. 46–51, 2015. [4] C. Kuimelis, “The deadline dilemma: when it comes to course assignments, how much flexibility is too much?” Nov 2022. [Online]. Available: https://www.chronicle.com/article/the-deadline-dilemma [5] D. Thierauf, “Feeling better: A year without deadlines,” Nineteenth-Century Gender Studies, vol. 17, no. 1, 2021. [6] M. Schroeder, E. Makarenko, and K. Warren, “Introducing a late bank in online
’ family members, particularly when it comes to college expenditures.However, many of these students are about to experience a significant transformation in their lives, thismeans that they are close to completing an academic degree and obtaining their first professional position,and their financial responsibilities will change considerably. a) Personal Expenditures b) College Expenditures 100% 100% 80% 80% 65
via an Arduino microprocessor connected to a laptop. After several rounds ofcharacterization and design, four experimental modules were completed which allowed studentto perform the following experiments: a) fluid flow (FLU), b) pump and valve characterization,(CUR) c) heat exchangers (HEX), and d) fixed bed columns (BED). Similar kits have beendesigned by other institutions for experiments on momentum and heat transfer, chemicalkinetics, crystallization, and particle science, either for UOLs or as practical modules for lectureclasses[5]–[8]. Using synchronous video-conferencing instruction, multiple sections of the classwere offered in Fall 2020 (100% online) and Spring 2021 (online + in-person; not in our UOL).In both semesters, students
Age Mean = 19.96 SD = 1.48 Sex 24% Male 76% Female Grade Level 37% Freshman 13% Sophomore 20% Junior 30% Senior Expected Grade 46% A 52% B 2% C 0% Below CPhase I ResultsA Kaiser-Meyer Olkin (KMO) factor adequacy was run, and a cut-off below .55 was usedto identify and eliminate underperforming items. This KMO adequacy was rerun with thetop items until item removal did not improve the overall Measurement System Analysis(MSA). The purpose of MSA computation was to assure that a selected
robot (consisting of a mobile base and itsmicro-controller, the Cortex). The Cortex controls the robot’s basic motion, while the RaspberryPI handles image processing and high-level decision making. Commands such as “turning to theleft”, “going straight”, and “turning to the right” were sent from the PI to the Cortes for execution. This paper describes vision-based control of a PI-controlled VEX robot. This new version doesnot include the Cortex. The VEX mobile base was controlled directly/solely by the Raspberry PI.In other words, there is only one “brain”, i.e., the Raspberry PI [Fig. 1 (b)]. While in the previousversion, there are two “brains”: the PI and the Cortex [Fig. 1 (a)]. This development addressed onedirection of further
O. Barambones, "A Multidisciplinary PBL Approach for Teaching Industrial Informatics and Robotics in Engineering," IEEE Transactions on Education, vol. 61, no. 1, pp. 21-28, 2018, doi: 10.1109/te.2017.2721907.[3] H. G. Denton, "Multidisciplinary team-based project work: planning factors," Design Studies, vol. 18, no. 2, pp. 155-170, 1997.[4] J. K. L. Leung, S. K. W. Chu, T.-C. Pong, D. T. K. Ng, and S. Qiao, "Developing a Framework for Blended Design-Based Learning in a First-Year Multidisciplinary Design Course," IEEE Transactions on Education, vol. 65, no. 2, pp. 210-219, 2022, doi: 10.1109/te.2021.3112852.[5] B. Tiwari, P. Nair, and S. Barua, "Effectiveness of Freshman Level Multi-disciplinary Hands
, agriculture, materials, career planning, and other topics. b) This is Engineering, taken in the second semester, will be a freshmen design style class, with hands-on problem-based learning, with sustainability embedded in all projects. c) A seminar on Justice, Equity, Diversity, and Inclusion will be developed for students to explore issues such as implicit bias and paternalism and reinforce the idea that co-design with communities will reduce discrimination and lead to better solutions. d) New courses, Wellbeing and Sustainability Economics will be developed to introduce students to essential ideas of natural capital, circular economies, and measures of well-being and prosperity. e) Other new courses include Products, Services, and
). While military training andexperience are valued they, does not always translate to a clear and straightforward career incivilian life after retirement or when servicemen (i.e., military personnel, soldiers, and officers)separate from the military; every year, about 2000,000 veterans leave the military. Over the nextfive to ten years, an increasing number of those 2000,000 people will become engaged in datascience and machine learning, driven by their interests, skills, backgrounds, and changing businessneeds[26]. The reason for this is (a) Data science will drive every type of business, and (b) TheArmy on a continuous basis, will need skillful personnel ( data engineers, analysts and scientists )to embrace its growth in emerging analytic
onmentoring. For example, Elliott et al. found that mentoring proved to be vital for women andunderrepresented minorities in STEM fields and Engineering coursework that had a focus onentrepreneurship [20] Additionally, Blaique et al. found that mentoring was a key predictor ofwomen and underrepresented groups in STEM fields going into and staying in the STEMworkforce [21]. b. financial support through scholarshipsScholarships helped retain the students by providing them with financial resources to continuetheir studies and reduce their financial burden and need to work extended hours. Scholarshipsprovided students with motivation and recognition for their achievements, which encouragedthem to stay in school and continue their studies. Additionally
Paper ID #38817A comparison of shared mental model measurement techniques used inundergraduate engineering contexts: A systematic reviewMr. Gregory Litster, University of Toronto Greg Litster is a PhD student in Engineering Education at the University of Toronto in the Institute for Studies in Transdisciplinary Engineering Education and Practice. He received his MASc degree in Man- agement Sciences (2022) and a Bachelor of Knowledge Integration degree (2020), both from the Univer- sity of Waterloo. His research interests are focused on mental models for engineering design teams, group dynamics and how collaboration
Annual Conference & Exposition, Jun. 2011, p. 22.867.1-22.867.31. Accessed: Feb. 26, 2023. [Online]. Available: https://peer.asee.org/industry-university-partnership-in-senior-capstone-design-course[4] G. Crain and M. Tull, “A Capstone Course Targeting Industry Transition,” presented at the 2004 Annual Conference, Jun. 2004, p. 9.11.1-9.11.9. Accessed: Feb. 26, 2023. [Online]. Available: https://peer.asee.org/a-capstone-course-targeting-industry-transition[5] M. McGinnis and R. Welch, “Capstones With An Industry Model,” presented at the 2010 Annual Conference & Exposition, Jun. 2010, p. 15.260.1-15.260.13. Accessed: Feb. 26, 2023. [Online]. Available: https://peer.asee.org/capstones-with-an-industry-model[6] J. B
graphic library for allowing userinteraction with the optimized model. This library is a part of the python standard library system.Students build an interactive window to write their own digits on it. Later, their own handwrittendigit is converted into the image. This image is passed to the pre-trained model to predict its owndigit. The predicted outcome is displayed in the same window as the confidence level.Part 2: Deploy Object Classification on Raspberry Pi and Short the Objects using RobotARMIn this part of the exercise, students use Raspberry Pi model-B to run TensorFlow pre-trainedmodel on it [15]. TensorFlow Lite platform is used to deploy machine learning models on edgedevices. It is a highly optimized conversion that allows end images
vertical shaft that bears an impeller at itsend. Impeller was immersed in a liquid, and when the machine was in motion, the impelleragitated the liquid. The bill of materials is given in Table I. (a) (b)Figure 2. The troubleshooting setup used in the study (a). A commercially available gamma-type Stirling engine is modified for this study’s purpose. In this setup, an electric heaterprovides the heat energy to run the impeller. The load on the impeller is determined by theviscosity of the liquid that it stirs. Close view of the heating coils on the displacer cylinder (b). Table I. The Bill of Materials for a Stirling
. The group bubbled pure CO2 in samplesof 0.5M and 1M NaOH. A diagram of the experimental setup is depicted below in Figure 1b.Figure 1: a) PFD of original flooding point experiment and b) Initial experimental set-up forDAC using NaOHRotation 2The first group in rotation two built a small semi-batch reactor using a Vernier carbon dioxidemonitor, parts available in the laboratory, and supplemental parts from the local hardware store.They were able to measure and present the first set of carbon dioxide removal data to the classand passed on a prototype reactor as well as suggested improvements to the next group. Theyalso ordered additional parts for the next group, including a second gas sensor. The group in thesecond section also created a small
inthe course (minimum grade of B), (2) they had to have shown mastery of the equipment theyworked on while in the course, (3) they had to be outgoing and willing to engage with thestudents in the class, and (4) they had to exhibit a willingness to teach the students in the coursewithout just giving them the answer. The author used a combination of observing the studentswhen they took the course and an informal interview with the perspective coaches to addressthese four criteria in terms of selection of the coaches. After the coaches were recruited, theywere then added to the payroll of the Department of Chemical Engineering which providedfinancial support for the students in the form of an hourly wage with a weekly workload of ~8-10hours
bystudying the current state of wind engineering tracks within civil engineering programs offeredworldwide and identifying their Strengths, Weaknesses, Opportunities, and Threats (SWOT). Toachieve these objectives, this research (a) analyzed the different civil engineering programs thatinclude wind engineering tracks offered worldwide and identified the academic institutions thathave academic expertise and equipment including atmospheric boundary layer (ABL) windtunnels, a fundamental tool for the research and study of wind events; (b) conducted a survey toall WE faculty and students doing research on these topics at Florida International University togather information on the courses offered and the intention of the course, as well as informationon
data-driven, or in other words, we allow themesto emerge from the data. Subsequently, we allow the research question to evolve through thecoding process. For thematic analysis, we draw on Braun and Clarke’ framework (2006) [16],which includes six phases (a) familiarizing/reading all data, (b) generating initial codes, (c)identifying initial themes, (d) reviewing and refining themes, (e) defining and naming thethemes, and (f) producing the report.Analysis and DiscussionThematic analysis of the data that captured transfer student experiences generated 17 initialcodes (548 coded text segments), from which four major themes emerged: universitycharacteristics, department academics, department support services, and student affectiveelements. Figure