institution (HSI), during the Spring 2023 semester.Given the rigorous nature of the course, the authors implemented an alternative pedagogicalstrategy: 1) providing step-by-step solutions for each homework assignment concurrently with theassignments themselves; and 2) students were instructed that a homework problem, or a lecturenotes problem(s), would be incorporated on each of the four semester in-class exams.Similar to the approach incorporated by Hertz and Chinn, the focus of this study was to reusehomework questions and lecture problems on the exam to enhance students' comprehension of thematerial, promote academic success, increase passing rates (~70%) from previous semesters, andmitigate the temptation to violate academic integrity by engaging
://onlinelibrary.wiley.com/doi/pdf/10.1111/ejed.12599. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/ejed.12599 [3] J. G. Meyer, R. J. Urbanowicz, P. C. N. Martin, K. O’Connor, R. Li, P.-C. Peng, T. J. Bright, N. Tatonetti, K. J. Won, G. Gonzalez-Hernandez, and J. H. Moore, “ChatGPT and large language models in academia: opportunities and challenges,” vol. 16, no. 1, p. 20. [Online]. Available: https://doi.org/10.1186/s13040-023-00339-9 [4] W. F. Foundation, “ChatGPT used by teachers more than students, new survey from walton family foundation finds.” [Online]. Available: https://www.waltonfamilyfoundation.org/chatgpt-used-by-teachers-more-than-students- new-survey-from-walton-family-foundation-finds [5] S. Weiner
% Hispanic, Black, Asian and Native races (e.g., non-whiteraces) [2]. Table 1: Percentage of Engineering Bachelor’s Degrees Disaggregated by Gender and Race, Summary of ASEE data from 2020 Race total %s 2020 %s Female Male (both Genders) White 11.6 41.3 52.9 Multi-racial 4.2 13.6 17.8 Hispanic 2.7 8.9 11.6 Black 1.1 2.7 3.8
empathy in engineering design.Dr. Miiri Kotche, The University of Illinois at Chicago Miiri Kotche is the Richard S. Hill Clinical Professor of Biomedical Engineering at the University of Illinois Chicago and currently serves as Associate Dean for Undergraduate Affairs in Engineering. Her research interests center on experiential learning, interdisciplinary collaboration, and promoting biomedical engineering through high school science teacher professional development.Prof. Houshang Darabi, University of Illinois Chicago Dr. Houshang Darabi is a Professor of Industrial and Systems Engineering in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. Dr. Darabi’s research focuses
semester.References[1] J. Hattie and G. C. R. Yates, Visible Learning and the Science of How We Learn. London: Routledge, 2013. doi: 10.4324/9781315885025.[2] N. P. Subheesh and S. S. Sethy, “Learning through Assessment and Feedback Practices: A Critical Review of Engineering Education Settings,” Eurasia J. Math. Sci. Technol. Educ., vol. 16, no. 3, p. em1829, Jan. 2020, doi: 10.29333/ejmste/114157.[3] J. Qadir et al., “Leveraging the Force of Formative Assessment and Feedback for Effective Engineering Education,” presented at the 2020 ASEE Virtual Annual Conference Content Access, Jun. 2020. Accessed: Jan. 15, 2025. [Online]. Available: https://peer.asee.org/leveraging-the-force-of-formative-assessment-and-feedback-for- effective
authors and literaturesuggest the dangers of not engaging meaningfully with communities when working onhumanitarian engineering projects. Considering that the success of the project is related to activecommunity participation and long-term commitment, the parties enrolled should follow the fiveprinciples presented by Mazzurco and Jesiek. These principles facilitate the communityparticipation, planning and management. Therefore, it is important to plan and establishstrategies to ensure the success of the project and promote engagement between engineers andcommunity members to achieve the project´s objectives. The literature provides guidelines thatenables engineers, educators, and community members to work more effectively.The Co-Design is “The
talk about astronautics, I completely understand that a lot of the concepts that were discussed in the book are going to go over my head. Reading about Mary Jackson and her work in the Supersonic Pressure Tunnel was very interesting because of how many times we discussed wind tunnels in class, and reading about how integral they are to aerospace engineering was also extremely interesting. • Obviously, with aerospace engineering being such an important role in the Space Race in the late 1950's and early 1960's, society was being impacted by whether or not we were going to beat the Russians into space. Another major application of aerospace
- Moderately effective 4 - Very effective 7 5 - Extremely effective What value does your project provide, and to whom? Who are the potential users or beneficiaries of your project? (Open-ended response)Revenue Streams/Cost Structure: Did your team consider the financial aspect of your project, such as potential costs and revenues? Yes/No Briefly develop a cost structure for your project.Final Reflection: What were the most significant lessons you learned from this project? (Open-ended response)3.2 Project – Design and Fabricate an S-Binder with Additive Manufacturing forMaximum Strength-to-Weight Ratios (Additive Manufacturing)The
methodology. Table 2. Review of technologies being used in STEM education for SLWD.Author(s) Country Technologies Purpose Target Group Education Designedand Year Level Solution/MethodologyIatraki et al., Greece Virtual Investigate the design issues Intellectual Primary Employed a focus group(2021) [21] Reality/Augmented in the development of digital disability (ID) methodology to explore the Reality (VR/AR) learning environments for
and expanded to include a more nuanced understandingof disabilities between the early 1920’s and the passage of the Rehabilitation Act of 1973. Thislandmark legislation combined and expanded upon all prior acts of rehabilitative legislation andforms the foundation of the current legal framework [5]. Notably, the Rehabilitation Act of 1973enshrined protection for those with disabilities seeking educational opportunities, guaranteeingotherwise qualified individuals are not excluded from educational opportunities at publiclyfunded institutions [5]. The Individuals with Disabilities Education Act (IDEA) was first passedtwo years later in 1975 as the Education for All Handicapped Children Act (EHA) [6]. It wasreauthorized and revised to its
crucial role in developing innovative solutions to public health challenges asexemplified during the COVID-19 pandemic where STEM professionals designed anddistributed personal protective equipment and vaccines and made significant advancements intelemedicine, predictive models and diagnostic tests (Braund, 2021, Fork & Koningstein, 2021).Unfortunately, the US is falling behind in STEM fields, a trend exacerbated by equity gaps in K-12 and higher education. The US no longer leads in science and engineering researchpublications or patents, and it graduates fewer STEM Ph.D.’s compared to countries like China(National Science Board, National Science Foundation, 2021, Zwetsloot, et al, 2021). These gapsbegin early, with significant disparities in
responsible AI more effectively, the complexity and rigor of participants’ discussionson each theme–referred to as theme depth–were also assessed. Theme depth was measured usinga 4-point scale adapted from Baker-Brown et al.’s conceptual/integrative complexity framework[30]. This scale assesses the sophistication of students’ engagement with ethical considerations inAI: • No mention (0): The ethical theme is completely absent from the response. • Superficial mention (1): The ethical issue is briefly acknowledged without substantive dis- cussion. For example, a student might simply state “privacy is a concern” without explaining why or how it applies to the AI system in question. • Detailed description (2): The ethical issue is
a 28% improvement in persistence throughchallenging coursework.Lave and Wenger's (1991) Situated Learning Theory provides the third theoretical pillar, asemphasized in Brown et al.'s (2017) research showing how AI-supported authentic learningenvironments increased student engagement by 45% and improved transfer of theoreticalknowledge to practical applications by 38%. The integration of these theories creates a robustframework for understanding how AI tools can simultaneously reduce cognitive barriers, buildstudent confidence, and provide authentic learning experiences.Figure 1 illustrates the integration of these theoretical perspectives, demonstrating how theywork together to support comprehensive learning outcomes
. Craven, F. W., & Slatter, R. R. (1988). An overview of advanced manufacturing technology. Applied ergonomics, 19(1), 9-16. 3. Vichare, P., Nassehi, A., Flynn, J. M., & Newman, S. T. (2018). Through life machine tool capability modelling. Procedia Manufacturing, 16, 171-178. 4. Adeleke, A. K., Montero, D. J. P., Olu-lawal, K. A., & Olajiga, O. K. (2024). Statistical techniques in precision metrology, applications and best practices. Engineering Science & Technology Journal, 5(3), 888-900. 5. Hartikainen, S., Rintala, H., Pylväs, L., & Nokelainen, P. (2019). The concept of active learning and the measurement of learning outcomes: A review of research in engineering higher education
wildfires on the monitoring of said wildfires. 7Bibliography[1] H. An, J. Gan, and S. Cho, “Assessing Climate Change Impacts on Wildfire Risk in the United States,” Forests,vol. 6, no. 12, pp. 3197–3211, Sep. 2015, doi: 10.3390/f6093197.[2] A. Mohapatra and T. Trinh, “Early Wildfire Detection Technologies in Practice—A Review,” Sustainability, vol.14, no. 19, p. 12270, Sep. 2022, doi: 10.3390/su141912270.[3] J. D. Coop, S. A. Parks, S. R. McClernan, and L. M. Holsinger, “Influences of Prior Wildfires on VegetationResponse to Subsequent Fire in a Reburned Southwestern Landscape,” Ecological Applications, vol. 26, no. 2, pp.346–354, Mar. 2016, doi
phases, integrating industry/engineeringstandards at each design step, paying attention to health and safety of the public, maintainingethical standards, and proper documentation of the capstone design process must be criticalcomponents of any capstone design model. Missing or inadequacy of addressing those criticalcomponents may result in negative evaluation by ABET program evaluators (PEV s). Therefore,it is important for any engineering program to adopt a proper capstone model to satisfy ABETprogram assessment requirements.In view of these contexts, this paper discusses the capstone model used by the engineeringprogram at the Southern Arkansas University (SAU). The model has been developed to providean industry level design experience in
: Create an initial inventory of specific EdTech (e.g., AutoCAD), rather than broad EdTech categories (e.g., “CAD tools”). o Step 2. Define Educator Selection Criteria: Identify the factors relevant to educators when choosing which tool(s) to try among many potential options. o Step 3. Expand the EdTech Dataset: Gather or supplement data for each EdTech based on the selection criteria (established in Step 2 of Phase 1), ensuring all relevant attributes are captured. Augment the dataset to include comprehensive data for a set of tools (i.e., capturing all significant attributes for each EdTech) in at least one specific EdTech
importance of hands-on training, mentorship,and community-driven learning in fostering technical expertise and engagement. By addressingidentified areas for improvement, future iterations of the program can further enhance its impact,ensuring that it meets the diverse needs of its participants and contributes to building a robusthardware security workforce.AcknowledgementsThis work was supported by the National Science Foundation under Grant No. #2322465. Anyopinions, findings, and conclusions or recommendations expressed in this material are those ofthe author(s) and do not necessarily reflect the views of the National Science Foundation.References[1] W. Hu, H. C. Chang, A. Sengupta, S. Bhunia, R. Kastner, and H. Li, “An overview ofhardware
Identity measures and the Research-Science/tist Identity measures here.Recognition measurements between all three identities were particularly strongly correlated(r>0.999), suggesting these constructs largely measure the same (or very similar) underlyingidentity component(s) in engineering doctoral students.The relative contribution of identity subconstructs to overall identity strength was examinedthrough ridge regression against implicit association scores. This approach differs from previouswork with this instrument [24] where recognition, performance, and interest were weightedequally in identity calculations. The regression revealed recognition as the dominant predictor,with performance contributing moderately and interest showing a
. Differ., vol. 103, p. 102274, Apr. 2023, doi: 10.1016/j.lindif.2023.102274.[2] A. Goslen, Y. J. Kim, J. Rowe, and J. Lester, “LLM-Based student plan generation for adaptive scaffolding in game-based learning environments,” Int. J. Artif. Intell. Educ., Jul. 2024, doi: 10.1007/s40593-024-00421-1.[3] M. M. Rashid et al., “Humanizing AI in Education: A Readability Comparison of LLM and Human-Created Educational Content,” Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 68, no. 1, pp. 596–603, Sep. 2024, doi: 10.1177/10711813241261689.[4] S. Avogadri and D. Russo, “On opportunities and challenges of large language models and gpt for problem solving and TRIZ education,” in World Conference of AI-Powered Innovation and
will enable students to visually exploreand interact with muscle segmentation processes, including keypoint selection, boundarytracking, and 3D reconstruction. This hands-on approach aims to foster a deeper, more intuitiveunderstanding of the algorithm’s functionality and its practical application in real-world medicalimaging scenarios.AcknowledgmentThis project was funded in part by the Northeastern TIER 1 seed grant.References [1] J. Zhu, B. Bolsterlee, B. V. Chow, C. Cai, R. D. Herbert, Y. Song, and E. Meijering, “Deep learning methods for automatic segmentation of lower leg muscles and bones from mri scans of children with and without cerebral palsy,” NMR in Biomedicine, vol. 34, no. 12, p. e4609, 2021. [2] R. Ni, C. H. Meyer, S. S
, 2012.[5] S. Emmott and S. Rison, “Towards 2020 science,” Science in Parliament, vol. 65, no. 4, pp. 31– 33, 2008.[6] “Criteria for Accrediting Engineering Programs, 2025 - 2026 - ABET.” Accessed: Jan. 14, 2025. [Online]. Available: https://www.abet.org/accreditation/accreditation-criteria/criteria-for- accrediting-engineering-programs-2025-2026/[7] E. Riese and S. Stenbom, “Engineering Students’ Experiences of Assessment in Introductory Computer Science Courses,” IEEE Transactions on Education, vol. 66, no. 4, pp. 350–359, 2023.[8] A. Forte and M. Guzdial, “Motivation and nonmajors in computer science: identifying discrete audiences for introductory courses,” IEEE Transactions on Education, vol. 48, no
elementary school,” Sch. Sci. Math., vol. 119, no. 4, pp. 203–212, Apr. 2019, doi: 10.1111/ssm.12332.[8] P. Paugh, K. Wendell, and C. Wright, “Elementary Engineering as a Synergistic Site for Disciplinary and Linguistic Learning in an Urban Classroom,” Lit. Res. Theory Method Pract., vol. 67, no. 1, pp. 261–278, Nov. 2018, doi: 10.1177/2381336918786937.[9] S. Purzer and J. P. Quintana-Cifuentes, “Integrating engineering in K-12 science education: spelling out the pedagogical, epistemological, and methodological arguments,” Discip. Interdiscip. Sci. Educ. Res., vol. 1, no. 1, p. 13, Dec. 2019, doi: 10.1186/s43031-019-0010-0.[10]B. M. Capobianco, J. Radloff, and J. Clingerman, “Facilitating Preservice Elementary Science Teachers
rates of targeting. This result suggests that specific training may be necessary forengineers to successfully target variability in more complex scenarios.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.2138463.References[1] K. Hadley and W. Oyetunji, “Extending the Theoretical Framework of Numeracy to Engineers,” J. Eng. Educ., vol. 111, no. 2, pp. 376–399, Apr. 2022, doi: 10.1002/jee.20453.[2] K. Vo, A. Evans, S. Madan, and Z. del Rosario, “A Scoping Review of Engineering Textbooks to Quantify the Teaching of Uncertainty,” in ASEE Annual Conference and Exposition, 2023.[3] D. Bose, M. Segui-Gomez, ScD, and J. R. Crandall, “Vulnerability of Female Drivers Involved in
that I think makes ethical could belong on. Thinking about consequence, many engineering education so difficult because whether the phrasing can really researchers encounter ethical you're in a course, and they're distinguish the system-level issues that involve industrial telling you, no, here's an thinking and/or institutional-level partners or potential patents. example. You do this thing, but thinking that is behind some of it's really, it's really everywhere. these. Identifying None of the categories really These critical inciden[t]s, can I really
://www.shrm.org/topics-tools/news/employee- relations/employers-say-students-arent-learning-soft-skills-college[4] “Workplace Conflict Statistics 2024 | Pollack Peacebuilding.” Accessed: Jan. 05, 2025. [Online]. Available: https://pollackpeacebuilding.com/workplace-conflict-statistics/[5] P. Bahrami, Y. Kim, A. Jaiswal, D. Patel, S. Aggrawal, and A. J. Magana, “Information Technology Undergraduate Students’ Intercultural Value Orientations and Their Beliefs about the Influence of Such Orientations on Teamwork Interactions,” Trends High. Educ., vol. 2, no. 2, Art. no. 2, Jun. 2023, doi: 10.3390/higheredu2020014.[6] I. Hensista, S. Guddeti, D. A. Patel, S. Aggrawal, G. Nanda, and A. J. Magana, “Transformative Pedagogy as a
could supplement the mentoring experience giving mentees a broader range of expertise and access.3. Create a structure of incentives and recognition for both mentees and mentors such as certificates of completion, awards for active engagement, or sharing success stories. This may especially help to encourage mentee participation.References:[1] Howland Cummings, M., & Schupbach, W. T., & Altman, T., & Jacobson, M. S., &Goodman, K., & Darbeheshti, M., “Making Meaning through Mentorship: A Student-LedLayered Peer Mentorship Program,” Paper presented at 2023 ASEE Annual Conference &Exposition, Baltimore, Maryland, 2023, June.[2] Simon, G. E., & Darbeheshti, M., & Howland Cummings, M., & Schupbach, W. T
–47, 2011.[3] 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, doi: https://doi.org/10.1002/j.2168- 9830.2008.tb00978.x.[4] A. Viningrad, R. Goldsmith, and Y. Vorgan, “Engineering education in the higher education system and the workforce demand for engineers in Israel,” 2022.[5] I. Central Beauru of Statistics, “Students who completed their studies for a first degree among those who began studying in 2009 / 10 ( follow-up until 2017 ), by duration of study for a degree in,” vol. 10, p. 2017, 2017.[6] Y. Melzer and A. Kimhi, “Employment in
accelerated summer format.Second, it was found that success relied heavily on preparation, structure, and accountability.Participants noted that elements such as the consistent schedule, regular weekly and bi-weeklymeetings (held on the same day and time), milestones, and learning activities were instrumentalin ensuring a paper was drafted by the conclusion of the cohort session. Third, of the six toolsused in the SoTL Accelerator program (https://www.sotlaccelerator.com/), faculty participantsfound three tools particularly helpful. • Peer Feedback Tuning Protocol (https://www.sotlaccelerator.com/s/Tool-2-Peer-Feedback- Tuning-Protocol.pdf) • Assessment of Student Learning (https://www.sotlaccelerator.com/s/Tool-3-Assessment- of