environments.Dr. Sridhar S. Condoor, Saint Louis University Professor with a demonstrated history of working in the design innovation and technology entrepreneurship areas. Skilled in Innovation Management, Applied Research & Product Design, Entrepreneurship, and Training Next Generation Innovators and Entrepreneurs.Dr. Jalil Kianfar, Saint Louis University Dr. Jalil Kianfar is an associate professor of civil engineering at Saint Louis University and a registered professional engineer (P.E.) in the state of Missouri. In addition to his academic experience, Dr. Kianfar has five years of industry experience as a traffic engineer that informs his teaching, research and service. Dr. Kianfar research interests and
implementation of cost-effective and accurate 3D Printed Lab Equipment (PLE) for control theory and vibration & dynamic labs. 3D printed lab equipment promotes educational inclusivity, innovation, and sustainability. In addition to her research, Sydney is passionate about becoming a high school STEM educator. She is a member of the American Society of Mechanical Engineering, National Black Society of Engineers, and Zeta Phi Beta Sorority, Incorporated. When not engaged in research, she enjoys volunteering, playing sports, spending time outdoors and with family, and is committed to promoting STEM education and being a mentor for young girls.Vanessa S Young, Kennesaw State UniversitySagar Patel, Kennesaw State
of Delaware Dr. Buckley is an Associate Professor of Mechanical Engineering at University of Delaware. She received her Bachelorˆa C™s of Engineering (2001) in Mechanical Engineering from the University of Delaware, and her MS (2004) and PhD (2006) in Mechanical EngineDr. Amy Trauth, University of Delaware Amy Trauth, Ph.D., is a Researcher at the American Institutes for Research (AIR) and Affiliate Faculty in the Department of Mechanical Engineering at the University of Delaware. Her research focuses on inservice and preservice teacher education and inclusive, accessible learning environments for students in P-16 STEM education. ©American Society for Engineering Education, 2025
, ABET, 2022.[3] W. H. Guilford, "Teaching peer review and the process of scientific writing," Advances in physiology education, vol. 25, no. 3, pp. 167-175, 2001.[4] J. Ford and S. Teare, "The right answer is communication when capstone engineering courses drive the questions," Journal of STEM education, vol. 7, no. 3, 2006.[5] W. E. Britton, "What is technical writing?," College Composition and Communication, vol. 16, no. 2, pp. 113-116, 1965.[6] C. Brooks and R. P. Warren, Understanding Poetry: By Cleanth Brooks and Robert Penn WOrren. Holt., 1960.[7] A. F. Warsame, "The Gap Between Engineering Education and Post-graduate Preparedness," Ed.D., Walden University, United States -- Minnesota, 10634462
of Chicago Press, 2012.[3] M. Besterfield-Sacre, C. J. Atman, and L. J. Shuman, "Characteristics of freshman engineering students: Models for determining student attrition in engineering," J. Eng. Educ., vol. 90, no. 2, pp. 139–150, 2001.[4] B. N. Geisinger and D. R. Raman, "Why they leave: Understanding student attrition from engineering majors," Int. J. Eng. Educ., vol. 29, no. 4, pp. 914–925, 2013.[5] M. W. Ohland, S. D. Sheppard, G. Lichtenstein, O. Eris, D. Chachra, and R. A. Layton, "Persistence, engagement, and migration in engineering programs," J. Eng. Educ., vol. 97, no. 3, pp. 259–278, 2008.[6] R. M. Felder and L. K. Silverman, "Learning and teaching styles in engineering education," Eng. Educ., vol. 78
& Make: Annual Global Report,” Autodesk, https://www.autodesk.com/hk/insights/research/state-of-design-and-make (Accessed August 13, 2024).[2] B. Caldwell and G. M. Mocko, “Product Data Management in Undergraduate Education,” Volume 3: 28th Computers and Information in Engineering Conference, Parts A and B, pp. 433–441, Jan. 2008. doi:10.1115/detc2008-50015.[3] R. O. Buchal, “The Use of Product Data Management (PDM) Software to Support Student Design Projects,” Proceedings of the Canadian Engineering Education Association (CEEA), Aug. 2011. doi:10.24908/pceea.v0i0.3862[4] K. Del Re, S. Yun, E. Kozikowski, T. Fuerst, and J. Camba, “Integrating a Product Life- Cycle Management System into a
analyze data. Finally, our interpretation ofthe conceptual nature and contexts for the items we reviewed is based on our ownunderstandings, experiences, and assumptions. We do not know the intentions of the authors ofthose concept inventories beyond what was present in their prior publications. It is possible thatour own misunderstandings or misconceptions could have influenced these results.AcknowledgmentsMany thanks to Dr. Eric Davishahl, Dr. Scott Danielson, Dr. Christopher Papadopoulos, and Dr.Paul Steif for their responses and support of this project. Many more regards and appreciationalso go out to all the other professors who helped provide their concept inventories for initialreview.References[1] A. Madsen, S. B. McKagan, and E. C. Sayre
College Students with Disabilities in STEM,” JPED, vol. 24, no. 4, pp.375–388.[7] E. A. Cech, “Engineering’s Systemic Marginalization and Devaluation of Students andProfessionals With Disabilities,” in Proceedings of the 2021 ASEE Annual Conference VirtualMeeting: American Society of Engineering Educators, Jul. 2021. Accessed: Jan. 11, 2025.[Online]. Available: https://peer.asee.org/engineering-s-systemic-marginalization-and-devaluation-of-students-and-professionals-with-disabilities.pdf.[8] C. Funk, “Black Americans’ Views of and Engagement with Science,” Pew Research Center,Apr. 2022.[9] C. Funk and M. H. Lopez, “Hispanic American’s Trust in and Engagement with Science,”Jun. 2022. Accessed: Jan. 11, 2025.[10] J. C. Richard, S. Y. Yoon, M. C
study of how alternative grading methods, suchas specifications grading, affect student perceptions of learning. This will likely involve focusgroups. Based on the results of the design course surveys, some questions for future studyinclude how students use feedback and how grades affect a student’s enjoyment of the course.References[1] S. Krinsky, R. C. Bosley, D. Verdin, E. Schiorring, and E. L. Allen, “Grading: The (Mis)use of Mathematics in Measuring Student Learning and its Disproportionate Impact on Equity and Inclusion,” in 2024 Collaborative Network for Engineering & Computing Diversity (CoNECD), Arlington, VA, Feb. 2024. Available: https://peer.asee.org/45456.[2] S. D. Blum, Ed., Ungrading: Why Rating Students Undermines
. F. Burch, and S. C. Vick, "Engineering soft skills vs. engineering entrepreneurial skills," The International Journal of Engineering Education, vol. 35, no. 4, pp. 988-998, 2019.[4] H. Jang, "Identifying 21st century STEM competencies using workplace data," Journal of Science Education and Technology, vol. 25, pp. 284-301, 2016.[5] L. Ballesteros-Sanchez, I. Ortiz-Marcos, and R. Rodriguez-Rivero, "Investigating the Gap Between Engineering Graduates and Practicing Project Managers," International Journal of Engineering Education, vol. 37, no. 1, pp. 31-43, 2021.[6] P. L. Hirsch and A. F. McKenna, "Using reflection to promote teamwork understanding in engineering design education," International
. Vereczkei, “Developing numerical ability in children with mathematical difficulties using origami,” Perceptual and Motor Skills, vol. 121, no. 1, pp. 233–243, 2015. [5] S. Arıcı and F. Aslan-Tutak, “The effect of origami-based instruction on spatial visualization, geometry achievement, and geometric reasoning,” International Journal of Science and Mathematics Education, vol. 13, pp. 179–200, 2015. [6] N. J. Boakes, “Origami instruction in the middle school mathematics classroom: Its impact on spatial visualization and geometry knowledge of students,” RMLE Online, vol. 32, no. 7, pp. 1–12, 2009. [7] A. Orlofsky, C. Liu, S. Kamrava, A. Vaziri, and S. M. Felton, “Mechanically programmed miniature origami grippers,” in 2020 IEEE
frontier, this study offers a glimpse of how Copilot can support course updates usingsimple prompts. Alternative AI tools with different capabilities may be more effective in creatingspecific technical content.References [1] Batista J, Mesquita A, Carnaz G. Generative AI and Higher Education: Trends, Challenges, and Future Directions from a Systematic Literature Review. Information. 2024; 15(11):676. https://doi.org/10.3390/info15110676 [2] Noroozi, O., Soleimani, S., Farrokhnia, M., & Banihashem, S.K. (2024). Generative AI in education: Pedagogical, theoretical, and methodological perspectives. International Journal of Technology in Education (IJTE), 7(3), 373-385. https://doi.org/10.46328/ijte.845 [3] Choi, G.W
, 2023.[3] ANSYS Workbench, https://www.ansys.com/products/ansys-workbench[4] N. Smith, J.L. Davis, “Connecting theory and software: Experience with an undergraduatefinite element course,” The ASEE Annual Conference and Exhibition, Seattle, June 2015.[5] K.A. Watson, A.O. Brown, R.K. Hackett, A. Pham, “ Finite Element Analysis LearningModules for an Undergraduate Heat Transfer Course: Implementation and Assessment,” TheASEE Annual Conference & Exposition, June 2012.[6] S. Higbee, S. Miller, “Finite Element Analysis as an Iterative Design Tool for Students in anIntroductory Biomechanics Course,” Journal of Biomechanical Engineering, 143(12), 2021.[7] A. Hickey, S. Xiao, “Finite element modeling and simulation of car crash
about student learning for AI-based feedback. References[1] V. Fakiyesi, D. Fabiyi, I. Dunmoye, O. Olaogun, and N. Hunsu, “A Scoping Review ofConcept Inventories in Engineering Education,” 2024 ASEE Annual Conference & ExpositionProceedings, doi: 10.18260/1-2--46487.[2] Evans, D.L., Gray, G.L., Krause, S., Martin, J., Midkiff, C., Notaros, B.M., Pavelich, M.,Rancour, D., Reed-Rhoads, T., Steif, P. and Streveler, R., 2003, November. Progress on conceptinventory assessment tools. In 33rd Annual Frontiers in Education, 2003. FIE 2003. (Vol. 1, pp.T4G-1). IEEE.[3] S. Filippi and B. Motyl, “Large Language Models (LLMs) in Engineering Education: ASystematic Review and Suggestions for Practical Adoption
’ opinions were collected using a survey approved by theuniversity ethics review board. 47 out of 72 students participated voluntarily in the study, whichwas completely blind to the instructor. Three example projects are explained, and outcomes arepresented.XR educational content development: Documents, videos, and training sessions were prepared toteach students how to translate their final designs from SolidWorks to other platforms that can beused for virtual reality (VR), augmented reality (AR), and mixed reality (MR). XRdemonstration and rubric: The XR demonstration was designed as a team assignment in whichstudents were required to demonstrate the function(s) of their final design concept using an XR-developed prototype/environment.An extended
example of student measurement of wave velocity in three test samples. Students repeated similar measurements at three locations on these samples, affirming good replicate data across the entire sample. Table 1: Wave Velocity Table. Material 𝑉𝐿 (in/s) 𝑉𝑇 (in/s) 2.57E+05 1.25E+05 Aluminum 6061 2.62E+05 1.25E+05 2.58E+05 1.24E+05 Young’s Modulus: Results from ultrasonic NDT closely aligned with standard values from the material datasheet, validating the
that ‘The autograders were incredibly helpful’ while another mentionedthat ‘I think the auto graders should give hints as to what is required otherwise even debuggingmultiple times ends up giving the same errors.’ The authors are of the opinion that in a junior-level mechanical engineering class, an autograder is not meant to fix student code. Instead, wewant students to exercise their critical thinking to build their own debugging skills given minimaldirection. In the root-finding example, students are not told which equation(s) the autograder isusing to determine their function’s accuracy. However, students are given a list of equations toperform their own tests. It is our expectation that if the autograder says “Your bisection code isnot
implemented to improve the overall efficiency of this cycle.The introduction and discussion of these power cycles rely heavily on the use of temperature versusentropy diagrams (T-s) which clearly show the process as the working fluid moves from state tostate. A T-s diagram of a simple ideal Rankine Cycle is shown in Figure 1. Figure 1. T-s diagram of a generic simple, ideal Rankine Cycle.Assignment Students were assigned the task of designing a new powerplant for the university. Thispowerplant was required to produce a minimum 12-MW of power. Due to metallurgical constraintsstudents were limited to a maximum temperature of 620 ℃, unless they could prove theircomponents could withstand a higher temperature. All components used had to be
). The undecided college student: An academic and career advisingchallenge (2nd. Ed.) Springfield, IL: Charles C. Thomas.[10] Hathaway R.S., Nagda B.A., Gregerman S.R. The relationship of undergraduate researchparticipation to graduate and professional education pursuit: an empirical study.[11] Kremer J.F., Bringle R.G. The effects of an intensive research experience on the careers oftalented undergraduates. J. Res. Dev. Educ. 1990;24:1–5.[12] Lin, L., & Atkinson, R. K. (2011). Using Animations and Visual Cueing to Support Learningof Scientific Concepts and Processes. Computers & Education, 56(3), 650-658.[13] Marquez, E., Garcia Jr., S. Nurturing Brilliance in Engineering: Creating Research Venuesfor Undergraduate Underrepresented
C.M. Firetto, "Development of an intervention to improve students’ conceptual understanding of thermodynamics," in 2013 ASEE Annual Conference & Exposition, Atlanta, GA, USA, June 23-26, 2013.[18] S. Kesidou and R. Duit, "Students’ conceptions of the second law of thermodynamics – An interpretive study," Journal of Research in Science Teaching, vol. 30 (1), pp. 85-106, Jan. 2006.[19] W. Dempster, C.K. Lee, and J.T. Boyle, "Teaching thermodynamics and fluid mechanics using interactive learning methods in large classes," in 2002 ASEE Annual Conference & Exposition, Montreal, Quebec, CA, June 16-19, 2002.[20] K.J. Nasr and B. Ramadan, "Implementation of problem-based learning into engineering
between predicted and actual scores averaged over 1 point loweron predicted score). There could also be investigations into whether students experience post-quiz anxiety or cognitive biases that influence their self-assessments.The pre-quiz predictions of the students tended to better at the end of the course compared to thebeginning. However, this was not the case for the post-quiz surveys. Also, the regressioncoefficient was fairly low, indicating only a small correlation. So, although there is a slight trendhere, there is little evidence to support the second hypothesis of students being better atpredictions at the end of the course.It was found that students in their early and mid 20’s tended to be the ones who underestimatedtheir abilities
measure a single construct are generallyconsidered interchangeable. However, Item Response Theory (IRT) [45] evaluates the distinctcharacteristics of each item. Examining the resulting Item Characteristic Curves (ICCs) is astraightforward method for understanding this. These curves illustrate the likelihood of anexaminee answering correctly based on their ability. This probability is small for individuals withlower ability and higher for those with greater ability. Each item produces a smooth S-shapedcurve. In Figure 1, with ability ranging from -4 to +4, the probability starts near zero at lowerlevels and approaches one at higher levels. An S-shaped curve is considered to be an indicator ofa good question. For the FMCI, as shown in Figure 1, we
Leave: Understanding Student Attrition from Engineering Majors," Interntational Journal of Engineering Education, pp. 914-925, 2013.[2] N. Honken and P. Ralston, "Freshman Engineering Retention: A Holistic Look," Journal of STEM Education, vol. 14, no. 2, pp. 29-37, 2013.[3] E. Seymour and N. M. Hewitt, Talking About Leaving: Why Undergraduates Leave the Sciences, Boulder, CO: Westview Press, 1997.[4] S. Sheppard, A. Colby, K. Macatangay and W. Sullivan, "What is Engineering Practice?," International Journal of Engineering Education, vol. 22, no. 3, pp. 429-438, 2006.[5] J. E. Froyd, P. C. Wankat and K. A. Smith, "Five major shifts in 100 years of engineering education," Proceedings of the IEEE, vol. 100, no. Special Centennial
evaluations are a very standard part of monitoring the efficacy of university instructorsand provide instructors with valuable feedback for improving their own performance and theexperience of students.1 The instruments to evaluate standard academic courses, however welldesigned and validated they may be for that task, do not typically serve well to evaluate how theadvisors of senior design (capstone) project teams perform their duties. Yet the same courseevaluation instrument is often applied to capstone project advisors by default, since capstone istypically listed and registered as an academic course.The idea of the modern capstone project largely emerged in the 1980’s, rapidly accelerating inadoption through the end of the millennium and reaching
sizes.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.DUE-IUSE-2116226. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References [1] Autodesk Inc., “The Essentials of IoT for Modern Engineers,” https://www.autodesk.com/industry/manufacturing/resources/mechanical-engineer/iot- internet-of-things-essentials-for-engineers, 2016. [2] W. Mahmoud and N. Zhang, “Disrputive technologies: An educational prespective.” 2018 ASEE Mid-Atlantic Section Spring Conference, Washington DC. [3] A. Huderson, E. Peiffer, S. Shamsi, F. Plazaand, and E. Collins
employability: stakeholder perceptions on the connection,” High. Educ., vol. 59, no. 5, pp. 599–613, May 2010, doi: 10.1007/s10734-009-9268-z.[3] H. Li, A. Öchsner, and W. Hall, “Application of experiential learning to improve student engagement and experience in a mechanical engineering course,” Eur. J. Eng. Educ., vol. 44, no. 3, pp. 283–293, May 2019, doi: 10.1080/03043797.2017.1402864.[4] S. F. Pamungkas, I. Widiastuti, and Suharno, “Kolb’s experiential learning for vocational education in mechanical engineering: A review,” AIP Conf. Proc., vol. 2114, no. 1, p. 030023, Jun. 2019, doi: 10.1063/1.5112427.[5] S. F. Pamungkas, I. Widiastuti, and Suharno, “Vocational Student’s Attitude and Response Towards Experiential
and Aerospace Engineering at the University of Central Florida. He coordinates two undergraduate programs – B. S. Mechanical Engineering and B. S. Aerospace Engineering. He has published over 130 peer-reviewed journal and proceeding papers. He has 12 and 6 patents granted in the U.S. and Korea, respectively, in the areas of sensors, microfluidic devices, and micro/nanofabrication. His current research focus is on miniaturized environmental sensors and sample handling devices. He earned his Ph.D. in Electrical Engineering from the University of Cincinnati in 2002. He worked as Research Engineer at Korea Electronics Technology Institute (KETI) from 1993 to 1997. He received the NSF CAREER award in 2004 and was given
motivates them to strive for good performance.AcknowledgmentThe authors appreciate the valuable discussion with Jiadi Zhang and Qinchun Li in the College ofEducation at the University of Illinois Urbana-Champaign.References [1] R. M. Carini, G. D. Kuh, and S. P. Klein, “Student engagement and student learning: Testing the linkages,” Research in higher education, vol. 47, pp. 1–32, 2006. [2] A. L. Reschly and S. L. Christenson, Handbook of research on student engagement, 2nd ed. Springer, 2022. [3] M. Kalogiannakis, S. Papadakis, and A.-I. Zourmpakis, “Gamification in science education. a systematic review of the literature,” Education sciences, vol. 11, no. 1, p. 22, 2021. [4] M. Jun and T. Lucas, “Gamification elements and their
flow between the Drain (D) and the Source (S) terminals. When this occurs, the load is energized. If the load is inductive, such as a motor or an electromagnet, it is recommended to add a flyback diode in parallel with the load to suppress voltage spikes that appear when the load is rapidly turned off. A typical control circuit is shown in Fig. 3.Figure 3 Controlling loads with an N-channel power MOSFET transistor (GQP30N06L) and Pulse Width Modulated signals. Suggested activities a) Assemble the circuit of Fig. 3 and use a 12V fan as the load. b) Write microcontroller code to command different speeds to the fan by using PWM signals of different duty cycles e.g., 0, 50
previous list. For each system list the following information: What type of system is it? (Mechanical, Electrical, Fluid/Thermal, Combinations?) If you were to model the system, what would the dynamic variable(s) be? What kind of physical quantities would you need to look up/solve for to model this system? • Talk as a group about everyone’s ideas. Share the story (as much or as little as you are comfortable with) surrounding the system you are interested in modeling. Start FBD’s of the systems you are leaning towards as a group to make sure you can model it.After completing the brainstorming activity, groups were instructed to use the generated ideas tochoose a project and then write a project proposal, which was due