Johns Hopkins University, Laboratory for Computational Se ©American Society for Engineering Education, 2025 Integration of Capstone Class and Student Competition Design TeamsAbstractMany student competition design teams, such as SAE Collegiate Design Series teams, ASMEdesign project teams, and others, feature interesting and challenging projects. These projects areoften open-ended and require use of material from multiple engineering classes and disciplines,which suit them in many ways for capstone projects in senior design classes. In this paper, a teamof faculty who have been involved with student competition design teams and have taughtcapstone classes analyze the student experiences with capstones and
correlated with laboratory-based micro-Vickers hardness testing to understand the measurement differences and uncertainties. The project team was then tasked with establishing a tolerance range for portable hardness measurements. 2. Offshore Swing Rope Transfer Alternative (Williams Corporation) – in the petroleum industry, swing rope transfers are one of the most dangerous transfer methods of personnel in Session XXXXX offshore construction and operations. Yet, they remain one of the only viable methods for transferring personnel from a small offshore vessel to an unmanned structure. This team project was to develop a concept design for an alternate means of
third option of reallocating the two credits to form a 4-credit CE250 SustainableCE course would lead to an overload of course credits for students in Winter Quarter of Year 3.The current instructor of this course could absorb the extra credits, or another course typicallytaught that quarter could be taught by another instructor.Adding an EnvE applications course in Spring Quarter of Year 3 would not alter course loads forstudents, as they would choose between CE431 Steel Design 1 or the new EnvE Applicationscourse. The new EnvE Applications course could be taught by the former instructor of CE461EnvE Laboratory, as that course will no longer be taught.Figure 1: CE curriculum flow chart notated to indicate possible rearrangement of courses
successful in their careers. Mastery learning is a promising approach for enablingmore students to succeed without lowering standards.References[1] B. S. Bloom, “Learning for Mastery. Instruction and Curriculum.” Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1,” Evaluation Comment Vol. 1 No. 2, May, 1968.[2] J. B. Carroll, “A Model of School Learning,” Teachers College Record, 64(8) , p. 723-733, 1963. https://doi.org/10.1177/0161468163064008[3] A. Essa, S. Mojarad, S. “Does Time Matter in Learning? A Computer Simulation of Carroll’s Model of Learning” in R.A. Sottilare, J. Schwarz, Eds. Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science, vol 12214
forunderrepresented groups in STEM [19, 20].Course Structure:A traditional lecture-based introductory Materials Science and Engineering course oftenincorporates laboratory activities such as XRD experimentation, tensile testing, and hardnesstesting. While these activities offer valuable hands-on experience, they are typically pre-designed,limiting student engagement in experimental design and data analysis. Even final projects, whichmay require students to design experiments, frequently lack a focus on computational modeling—a critical skill in modern engineering. It should also be noted that this is the introductory levelMaterials Science course with pre-requisites of Calculus III, Chemistry, and at least anintroductory level of programming course (either
skills includingreasoning, creativity and open problem solving . The learners experience difficulty understandingthe basic knowledge and skills in understanding physics. Lecture classes, problem-solvingsessions, and laboratory activities deliver these fundamental physics topics to learners. The lackof organization creates many difficulties in the comprehension of basic concepts and in solvingcomplex problems. This leads to the common complaint that students' knowledge of physics isreduced to formulas and labels of the concepts, which are unable to significantly contribute tomeaningful reasoning processes [4]. To address students’ learning difficulties in physics, the subject needs to be made enjoyableand the learning content needs to be
Laboratory at Texas A&M University, a state-of-the-art facility for education and research in the areas of automation, robotics, and Industry 4.0 systems. He was named Honorary International Chair Professor for National Taipei University of Technology in Taipei, Taiwan, for 2015-21. Dr. Hsieh received his Ph.D. in Industrial Engineering from Texas Tech University, Lubbock, TX. ©American Society for Engineering Education, 2025 Incorporating Hybrid Virtual Simulators and Physical Tools for Angle Measurement in High School GeometryAbstractUnderstanding geometric angles is crucial for students, as angles are the basis for more advancedmathematical concepts and real-world
Students,” Preminente.[17] Barell, J. (1995). Critical issue: Working toward student self-direction and personal efficacy as educational goals. Naperville, IL: North Central Regional Educational Laboratory. Retrieved September 12, 2005.[18] Huitt, W. (1999). Conation as an important factor of mind. Educational psychology interactive, 9.[19] Boyatzis, R. E. (2006). An overview of intentional change from a complexity perspective. Journal of management development.[20] Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81(2), 267-301.
requires future engineers to learn and master the essential elements of thesedomains during their undergraduate curriculum. However, the electrical and computerengineering curricula is still catching up with the rapid growth in technology. Many institutionsof higher education lack adequate laboratory facilities and expert faculty in this area. It isessential that the emerging field of machine learning be integrated into the electrical andcomputer engineering curricula. The following are examples of how various universities areintegrating machine learning into their curricula.Loyola Marymount University (LMU)At LMU, to introduce ML concepts to freshman engineering students, they have combined activelearning and authentic learning into an
variability on groundwater rechargeand depletion, identifying regions at high risk of water scarcity.[3]The STEM initiative combines theoretical knowledge with hands-on experimentation to deepenstudents' understanding of water systems and their management. Practical activities, such asmodeling aquifer recharge and measuring water flow rates, allow students to simulate naturalprocesses like infiltration, capillary action, and groundwater movement. These experimentsdemonstrate engineering principles in action and encourage students to design prototypes for waterconservation technologies, including artificial recharge systems and irrigation networks. Byworking with laboratory equipment and field tools, students develop problem-solving andanalytical
secure external funding to support student research,industry-driven projects, and state-of-the-art laboratory facilities. Partnerships with state andfederal agencies will further enhance opportunities for students to engage in research thatdirectly impacts agricultural innovation. Summary and ConclusionsThe ET-AG program at WTAMU represents a forward-thinking approach to agriculturaleducation, integrating engineering and technology to meet the challenges of modern foodproduction. Program development requires hands-on efforts supported by faculty and industrycollaborations. The ET-AG program is a new interdisciplinary initiative that will be expanded asboth undergraduate and graduate student populations continue to
. References1. Ni, Jianyun, and Jing Luo. "Microcontroller-based engineering education innovation." 2010 International Conference on Educational and Information Technology. Vol. 3. IEEE, 2010.2. Bolanakis, D. E. (2019). A survey of research in microcontroller education. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 14(2), 50-57.3. Hur, B. (2019, June). ARM Cortex M4F-based, Microcontroller-based, and Laboratory-oriented Course Development in Higher Education. In 2019 ASEE Annual Conference & Exposition.4. Leon, J., Hill Price, A., & Kuttolamadom, M. (2019). Developing a Graduate Master's Degree Program in Engineering Technology: Overview of Program Objectives, Structure and Impact. American Society for
a class project.In the past, two-wheeled robots were used as a class project. Students need to do some more work tobuild and operate physical two-wheeled robots. This is a good approach to students’ learning.However, there were some problems that were recognized primarily due to the large size of thestudents. Students tend to damage the robots and need a replacement during their class project hours.Since many physical robot units were needed to manage during the laboratory, the laboratoryinstructors may find that this method is becoming challenging.For this reason, the hardware components were reduced as shown in this boat class project in thispaper. And, the robot control method was changed to a virtual mode using a GUI program. However
-MACHINE SYSTEMS COURSE AND ROLE OF AI Along with the assignment write-up, students were asked tooutline the advantages and drawbacks of using AI for such A. HMS Course Profileacademic work. Of the 56 respondents, there were 115 open-ended responses indicating the merits of using Gen AI for this Human-Machine Systems (HMS) is a 5-credit senior-leveltype of project and 121 responses outlining the less effective and engineering course at Northeastern University, with multipleconcerning aspects of its use. The primary categories of positive assignments and laboratory sessions over a 15-week semester.responses reflected how students felt AI benefited them in This course focuses on the science behind safe
relatively accessible because of its availability and relatively to entry and difficulty in incorporating the associated topics low cost. The image of a specimen obtained using a flatbed in the classroom and educational laboratory. To overcome this scanner can then be used to perform a digital image analysis, barrier, an algorithm and user-friendly Matlab application was developed to examine and quantify the constituent phases in which is then used to examine the properties and health of a samples of concrete. This tool performs color-based segmentation specimen. of the phases of concrete, including the computation of the Digital image analysis of concrete has been performed a area fraction of each
. https://necsi.edu/powerlaw#:~:text=A%20power%20law%2Stress was primarily localized at the wing-shell connectors, 0is%20a,the%20length%20of%20its%20side. (accessed Mar. 3, 2025).[9] Overview and results from the Mars 2020 PerseveranceRover’s first science campaign on the Jezero Crater Floor -sun - 2023 - journal of geophysical research: Planets - wileyonline library,https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022JE007613.[10] U.S. Department of Defense, MIL-STD-810G: TestMethod Standard for Environmental EngineeringConsiderations and Laboratory Tests, Oct. 2008.[11] C. Haodong, Y. Li, H. Zhang, and W. Sun, “Modelingand thermal analysis of Step 2 GPHS for a larger safeoperating temperature range,” Thermal Science andEngineering
, Qualitative Data Analysis with NVivo, 2nd Technicians: A Workforce Development Metric,” Modern Economy, ed. London, UK: Sage, 2013. vol. 14, no. 10, pp. 1–15, 2023. [32] J. Saldaña, The Coding Manual for Qualitative Researchers, 3rd ed.[7] U.S. Department of Transportation (DOT), “EV Workforce London, UK: Sage, 2016. Development Initiatives.” Available: [33] D. L. Morgan, "Focus groups as qualitative research," Qual. Res. https://www.transportation.gov/ev-workforce Meth. Series, vol. 16, 1997.[8] National Renewable Energy Laboratory (NREL), “EV Infrastructure [34] M
platform's efficacy. Additionally, the artificialindicating increased cognitive effort due to the AIHT’s reduced laboratory setting may not fully capture the complexity of real-reliability. Simultaneously, performance accuracy of the world clinical decision-making environments that traineeparticipant dropped by 50%, suggesting that diminished AI nurses will encounter [1], [7]. Despite these limitations, thisaccuracy weakened trainee nurses’ trust, negatively affecting study represents an important step toward developing objectivetheir performance (Fig. 3b). measures of trust in healthcare AI. The AIHT platform's
, “Epistemology, socialization, help seeking, and gender-based views in in-person and online, hands-on undergraduate physics laboratories,” Phys. Rev. Phys. Educ. Res., vol. 16, no. 2, p. 020116, Aug. 2020, doi: 10.1103/PhysRevPhysEducRes.16.020116.[11] J. Saldana, The Coding Manual for Qualitative Researchers, 3rd ed. SAGE Publications, 2016.
Kafer, G. “The roles of mentoring and motivation in student teaching assistant interactions and in improving experience in first-year biology laboratory classes.” J. of Coll. Sci. Teaching, vol. 44 no. 4, pp. 88-98, 2015. https://www.jstor.org/stable/43631870[3] Riese, E. and Kann, V. “Training teaching assistants by offering an introductory course.” Special Interest Group on Computer Science Education, 2022.[4] Ujir, H. Salleh, S.F. Marzuki, A.S.W., Hashim, H. F. and Alia, A.A. “Teaching workload in 21st century higher education learning setting.” Int. J. of Eval. and Research in Ed., vol. 9, no. 1, pp. 221-227, 2020. DOI:10.11591/ijere.v9i1.20419[5] Fong, C. J., Gilmore, J., Pinder-Grover, T., and
resources fortransference learning.in Figure 2, user inputs—typed questions or spoken prompts—are processed locally for immediate,low-latency tasks, while more computationally demanding queries (such as generating a simula-tion of prior robot experiences from another laboratory) are offloaded to cloud-based AI modules.This topology ensures a responsive user experience that still retains access to advanced analyt-ics and broader knowledge repositories. The pilot setup featured short activity blocks in which ahuman user interacted with the system to solve engineering tasks. Learners could request demon-strations of a robotic arm movement, followed by AI-generated textual or spoken explanationsof the underlying principles in a classroom setting. At
Control design review 18 Angular velocity controller design for UAV 19 Attitude/Altitude controller design for UAV 20 Translational position controller design for UAV 21 UAV sensors and their mathematical models 22-23-24 Vehicle installation and instructionsApplicationAccording to The National Research Council’s definition of learning in a laboratory [17], physicalsimulations or applications of the theory generates many opportunities for the students to gainfield experience, using various tools and equipment, conducting experiments under differentconditions, acquiring data, analyzing and presenting the results [17].During the application phase of this course, a
Horizon Project Sector Analysis. ERIC, 2013.[15] J. Miranda et al., "The core components of education 4.0 in higher education: Three case studies in engineering education," Computers & Electrical Engineering, vol. 93, p. 107278, 2021.[16] N. Blinn, M. Robey, H. Shanbari, and R. R. Issa, "Using augmented reality to enhance construction management educational experiences," in Proceedings 32nd CIB W078 Workshop, Eindhoven, The Netherlands, 2015, p. 8.[17] Z. H., "Using 3D Hologram to Improve Classroom, Project, and Laboratory Demonstration: A Proposal for 2017 Innovations in Teaching Using Technology Grant. ," Rowan University, College of Engineering, 2017.[18] T. Consoli, J. Désiron, and A. Cattaneo
Engineering. His research uses body-worn sensor networks to better quantify and understand human performance in many biomechanical contexts, outside of traditional laboratory environments.Dr. Lorna Cintron-Gonzalez, Francis Marion University Dr. Cintron-Gonzalez is an Associate Professor of Industrial Engineering at Francis Marion University in Florence, SC. Dr. Cintron-Gonzalez earned a BS degree in Industrial Engineering from the University of Puerto Rico at Mayag¨uez in 2005, a MS degree in Health Systems from Georgia Tech in 2006, and a PhD in Industrial Engineering from Penn State University in 2013. Her research interests include engineering education, workplace human factors and ergonomics, health systems
.[2] M. Evrat and R. Sharma, “Laboratory Modules For Wind Turbine Experiments Using theWindLab Facility At The University of Queensland”, School of Information Technology andElectrical Engineering, Power and Energy System, The University of Queensland, St. Lucia,QLD, 2015.[3] Ansys® Fluent with Fluent Meshing, Release R2, ANSYS, Inc., 2024.
the student body is receiving an education that approaches critical thinking in aholistic manner (e.g., formulating problems, working in a laboratory setting, mastery ofgraphical/written/verbal communication). Institutions collect a series of assessments targetingthese individual student outcomes (SOs) with the goal of determining how well the student bodycan achieve the goals prescribed by ABET. This process provides a thorough overview ofstudent attainment in the SOs from the perspective of the institution and its individual faculty,but it lacks any substantive measure of student self-efficacy.Self-efficacy is a term used to describe how well an individual believes they can accomplish atask [1]. Self-efficacy in a higher learning setting
manufacturer of the solar farm kit provided updated materials for assemblyand tests that enabled students who did not do the inventr.io courses to be successful in theconstruction of the physical system model. In the next iteration, the inventr.io courses will beomitted. Based on feedback, more time will be spent on communication between the twins andsample code for both serial and WiFi communications will be included. Additionally, time willbe spent in the process of data capture and analysis for predictive modeling with the DT.Given the concern that a remote education is missing a critical hands-on component, this courseclearly demonstrated that a hands-on laboratory experience can successfully be a part of a remoteclassroom. If this course were
, or pausing periodically (Adapted from Prince, 2004). Four participants form lecture-based sessions and 9 participants from active learning sessions reported lecturing combined with informal classroom activities. 3. Lectures combined with labs/studios pertain to a course consists of two different sessions: lectures and laboratories/studios. In labs or studios, students are expected to apply knowledge imparted during lectures through hands-on activities and projects (Adapted from Gelernter, 1988). For lectures combine with labs or studios, 3 participants of lectured-based sessions focused on lecture part, while 5 participants of active learning sessions talked more about labs or
partners,and regional innovation ecosystem organizations such as incubators and accelerators. Industrymentoring is performed as a volunteer activity with low demands on their time, and mostactivities are performed via video conferencing for greater reach and engagement.Customer Discovery Interview Utilizing a Flipped Classroom PedagogyAs part of the Innovation Fellows Program, the fellows receive specialized customer discoverytraining tailored for biomedical scientists and engineers. This training builds from the U.S. NSFI-Corps Program launched in 2011. I-Corps is to prepare “scientists and engineers to extend theirfocus beyond the laboratory to increase the economic and societal impact of NSF-funded andother basic research projects” [16]. This
future activities. The program adopted a multi-pronged approach to mentorship andresearch training, incorporating varied research environments to support students’ academic andprofessional development. In 2019, an additional faculty-student research model wasimplemented, where students were sent to national laboratories alongside faculty mentors for animmersive three-month research experience. This provided students with direct exposure tocutting-edge research, interdisciplinary collaboration, and real-world STEM applications.However, due to funding redirection, this component was discontinued in subsequent years.All mentors were selected based on their research expertise, mentoring experience, andwillingness to participate in the program. All