rankings, theengineering program at Cal Poly Pomona is ranked #10 among public universities in the nationamong public universities where a doctorate is not offered [2]. It is also ranked top 15Nationally in Social Mobility according to The Wall Street Journal [3]. What makes Cal PolyPomona unique is the culture of “learn by doing” which emphasizes hands-on learning inclassrooms and laboratories. The reputation of being a top engineering school is well-deserved.The goal of this study is to build upon the successful culture of “learn-by-doing” by exploringhow a project-based approach in a traditional engineering course can improve student knowledgeof the subject matter. In addition, having a variety of teaching methods aligns with the broadvariety
for a more highlyskilled workforce equipped with programming skills for the analysis of the huge amount of data thatcan be generated on construction sites, particularly with respect to the prediction of the properties ofmaterials for useful insight generation as well as rapid and informed decision making. In this study,construction students were introduced to artificial intelligence (AI) techniques and how they can beused for predicting the properties of construction materials in a construction course. First, thestudents were presented with a basic knowledge of AI for predicting the strength of constructionmaterials. A hands-on programming laboratory session was designed to get students started with theimplementation of AI knowledge through
field trips, laboratory work, and traveling to museums, aswell as the distances between rural schools and these resources, challenge schools in providingSTEM education. [2]In West Virginia, a predominately rural state (34 of 55 counties are rural), more STEM outreachefforts are concentrated in urban counties, as shown in Figure 1 and Table 1 from Coltogironeand Kuhn et al.[1]Here, we see that STEM outreach is poor in rural areas and that average STEM initiatives inurban areas of the state are about 3 times that of rural counties. Many of the rural counties haveless than two STEM initiatives.In addition, rural students are 10 times more likely to prefer working in rural settings[3], whichcreates a win-win situation in training rural students in
from instructors and collaborate with peers, enhancing their overall learningexperience. In environmental engineering, practical skills are crucial. A study [5] showed thatstudents with higher attendance rates performed better in laboratory components of the course,suggesting that attendance facilitated the acquisition of practical skills essential for the field.Several factors can impact attendance rates, including student motivation, course design, andexternal commitments. Studies have shown that flexible course delivery methods, such as hybridmodels, can improve attendance by accommodating diverse student needs [6]. Despite the clearlink between attendance and performance, some studies have faced challenges such as self-reporting bias in
can build innovative and impactful educational programs that betterprepare students for careers in this evolving sector.Literature ReviewThe integration of industry expertise into engineering education, especially in medical devicedesign, is essential for bridging the gap between academic theory and practical applications.When combined with active learning, industry collaboration becomes even more impactful,enabling students to address real-world challenges [3]. Active learning, endorsed byorganizations like SEFI and ABET, enhances student engagement and performance byconnecting theoretical concepts to practical experiences [1][2]. It also improves understanding,retention, and problem-solving skills [3], and when combined with laboratory
thatengineering education while ensuring its effectiveness combining structured rubrics with qualitative feedbackand sustainability [4] . One of the primary obstacles in provides the most effective assessment model [4].implementing PBL is the high demand for resources, Another significant challenge of implementing PBL isespecially in institutions with limited budgets. Many faculty adaptation. Many educators are accustomed tohands-on projects require specialized equipment, raw traditional lecture-based instruction and may lack thematerials, and laboratory spaces, which may not hands-on experience required for guiding studentsalways be readily available [7]. CNC-based projects, through PBL projects [8]. A professor
members of the National Renewable EnergyLaboratory (NREL) for their encouragement. This work issupported by the National Renewable Energy Laboratory(NREL) under grant SUB-2024-10424.References[1]. Mackenzie Dennis, An Overview of Heliostats and Concentrating Solar Power Tower Plants, National Renewable Energy Laboratory, March 2022[2]. Joshua Weissert, Yu Zhou, Dongchuan You, and Hameed Metghalchi, Current Advancement of Heliostats, Journal of Energy technology, Vol. 144 / 120801 -7, 2022[3]. Zhang, Y., & Wei, M., "Concentrated solar power (CSP) technology and its potential in China: A review." Renewable and Sustainable Energy Reviews, Vol. 113, pp.109-124, 2019.[4]. Yogesh, K., & Bhushan, P., "A comprehensive
Obispo Brian Self obtained his B.S. and M.S. degrees in Engineering Mechanics from Virginia Tech, and his Ph.D. in Bioengineering from the University of Utah. He worked in the Air Force Research Laboratories before teaching at the U.S. Air Force Academy for seven years before joining Cal Poly, San Luis Obispo in 2006. ©American Society for Engineering Education, 2025 Adaptive Learning Modules in Introductory Engineering CoursesAbstractDynamics is a foundational engineering course, however, students often find it challenging dueto their limited prior experience and preconceptions. Conventional teaching methods in thiscourse frequently fall short of connecting main principles in ways that improve
project demonstrates mastery ofmaterial through the appropriate use of statistical methods and interpreting their results. Beyondthis, the students must further communicate these findings clearly to a diverse audience (who havetheir own, and often very different, projects).Project selection involves the students choosing a topic; these are available first-come, first-served;however, the courses assume that the students will develop their own topics (with instructorsupervision, not direction) and the students are not provided with a list of ideas. Students areencouraged to look to laboratory experiments in literature, or even science fair project ideas 1.Given the possibility that many concepts are not practical, the students are expected to
encourage student preparation.Course FormatEach course is briefly outlined to provide context for implementing web-based pre-class readingresponses. This approach was trialed across various engineering courses to assess its impact onstudent preparedness and performance.Introduction to Geotechnical Engineering is a three-credit course which is offered in the fallsemester that meets three times a week (50 minutes each). The course focuses on engineering useof soils; laboratory and field determination of soil properties; determination of phaserelationships; engineering soil classification; soil-water interaction; stress effects of loading onsoils at depth; consolidation, compaction, shear strength, bearing capacity theory, and severalspecial
test apparatus for an engineering laboratory course.” Computer Applications in Engineering Education, 2024. DOI: 10.1002/cae.22773 12. M. Chen. “Facilitating aerospace engineering senior design: Integrating lab curriculum redesign with student project and new technologies.” Engineering Reports, 2024. DOI: 10.1002/eng2.12938AcknowledgementsThis material is based in part upon work supported by the National Science Foundation underGrant No. 2152218. Disclaimer: Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views of theNational Science Foundation.
impact of flipped classrooms on student achievement in engineering education: A meta-analysis of 10 years of research," Journal of Engineering Education, vol. 108, no. 4, pp. 523-546, 2019.[3] R. Castedo, L. Lopez, M. Chiquito, J. Navarro, J. Cabrera and M. Ortega, "Flipped classroom—comparative case study in engineering higher education," Computer Application in Engineering Education, vol. 27, no. 1, pp. 206-216, 2018.[4] M. Chen, "Synergizing computer‐aided design, commercial software, and cutting‐edge technologies in an innovative nozzle test apparatus for an engineering laboratory course," Computer Applications in Engineering Education, vol. 32, no. 5, p. e22773, 2024.[5] C. Chen, "Flipped classroom with case-based learning
Paper ID #45828Transforming Teaching Evaluations One Department at a TimeDr. Adam Piper, Mississippi State University Dr. Piper serves as a Teaching Professor in Industrial & Systems Engineering at Mississippi State University. He has instructed more than 100 courses and laboratory sections across Industrial & Systems Engineering, Engineering Management, and Biomedical Engineering at four institutions in the Southeastern and Midwestern United States. His primary interest lies in the modeling and enhancement of processes, including those related to the assessment of teaching and learning within the engineering
Electronics course, we decided to design this MTS 102 3-position toggle switches 8puzzle box. Such a project not only demonstratedNortheastern’s emphasis on experiential learning by having us TTL Logic chips 4invent, design, refine, and physically implement our ideas, but Arduino (Uno R3) 1also allowed us to create a product that can benefit the next Variable DC Power Supply 1generation of students. (GW INSTEK GPS-3030DD DC Our design extensively utilizes transistor-transistor logic Laboratory Power Supply)(TTL) chips
consisting of process engineers to upper management and from multinationalcompanies to start up companies. This allowed the “instructors” to determine which KSA’s to focuson in the course. IntroductionMainstream graduate STEM education programs are traditionally designed to train students foracademic careers as they focus on knowledge and skills related to laboratory research practices,writing technical journal papers, and presenting results at conferences to academic peers. Thismethod of education has value in preparing students for academic careers but falls short in Proceedings of the 2025 ASEE Gulf-Southwest Annual Conference The University of Texas at
their teaching throughout theprogram. However, only one graduate student was a TA and had full access to undergraduaterecitation sessions; two other graduate students were teaching assistants but were involved withgrading and minimal classroom instruction. For this reason, the focus of the program was shiftedtoward learning about and discussing inclusive teaching, and away from implementation andformative feedback. In future iterations, consistent classroom, laboratory, and/or recitationinstruction will be a criterion to participate. It is expected that the TAs’ real-time classroomexperiences and the feedback cycle will generate rich discussion, challenge TAs’ thinking aboutinclusion and equity in STEM, and enhance TA and undergraduate
Science and Computer Engineering research and researchcapacity.VI. Current StatusThroughout 2024, both Morehouse College Computer Science and Georgia Tech ComputerEngineering program directors met monthly with specific program stakeholders to discussvarious related activities. Since the fall 2023 funding of the grant, a newly formed alliance led byGeorgia Tech has emerged to support HBCUs pursuing semiconductor manufacturing fundingopportunities. Additionally, in January 2024, the program directors and the program postdoctoured the Georgia Tech cleanroom laboratory, the largest in the southeastern US. Regardingconference and event travel, the Georgia Tech Computer Engineering program director attendedthe 7th Annual Collaborative Network for
in 2019 and is currently pursuing his M.S.in Mechanical Engineering at UTSA focusing on robotics and control systems under the direction of Dr. Cody Gonzalez.Mr. Hicks-Ward’s professional career has led to over four years of experience leading technical operations with threeyears of experience in design optimization for manufacturability and serviceability of mobile robotics.KEVIN NGUYENMr. Nguyen is an undergraduate research assistant at the University of Texas at San Antonio where he is currentlypursuing his B.S. in Mechanical Engineering. He is also the Treasurer of the Design of Actuators, Robotics, andTransducers Laboratory under the direction of Dr. Cody Gonzalez.CODY GONZALEZDr. Gonzalez is an Assistant Professor in the UTSA
outcomes ensure graduates are well-prepared for professionalengineering practice and societal needs.Our ABET report detailed how the program integrated ABET-defined student outcomes into itscurriculum to meet interdisciplinary demands. A mapping (Appendix C) showed how eachoutcome supported at least two program educational objectives. The report outlined thecurriculum structure, blending seminars and laboratories to reinforce hands-on problem-solving.We highlighted part-time instructors who brought industry expertise, enhancing courserelevance. To broaden perspectives, we detailed initiatives such as alumni panels, mentorshipprograms, and guest lectures by industry and academic professionals. The report emphasizedinternships and undergraduate
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
. 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