/0013189X033008003.[2] M. S. Garet et. al., "What makes professional development effective?: Results from a national sample of teachers," American educational research journal, vol. 38, no. 4, pp. 915-945, 2001, doi: 10.3102/00028312038004915.[3] A. Kodey, J. Bedard, J. Nipper, N. Post, S. Lovett, and A. Negreros. "The US Needs More Engineers. What’s the Solution?" https://www.bcg.com/publications/2023/addressing-the-engineering-talent-shortage (accessed 1/14/25.[4] D. Collins, J. D. Olson, M. Kotche, E. Taylor, and J. Mendez, "Transforming Science Teacher Practice through an Intentional Summer Research Opportunity: A Case Study of two Urban Science Educators," presented at the American Educational Research
the students’ own community can better support students to develop relationships andaffect with the community within limited time and resources, so they can better learn and dosociopolitical engineering. For future work, I plan to study how other elements in the learningecology (e.g., Ashford city, students’ families) impact students to do and learn engineering.Besides their acquisition of knowledge and skills, I would also like to study how therelationships and affect impacted the students’ identity development and how they seethemselves related to engineering in the long term.References[1] E. Reddy, M. S. Kleine, M. Parsons, and D. Nieusma, “Sociotechnical Integration: What Is It? Why Do We Need It? How Do We Do It?,” in 2023 ASEE
disclose what has been done to themduring their employment.State lawsOver 20 states have approved laws either banning NDAs or making them unenforceable undervarious conditions, such as sexual assault, sexual harassment, retaliation, or discrimination, andmultiple bills are in legislative process. Washington’s Silenced No More Act, sponsored byRepresentative Liz Berry and referenced below, is exemplary, and requires the party trying toenforce an NDA to pay a $10,000 fine and cover the other party’s legal expenses. Listings andbrief descriptions of state laws are published by Lift Our Voices [18] and Can’t Buy My Silence[19].Lack of awareness of state and federal lawIn 2019, New Jersey passed one of the first laws restricting NDAs, S. 121, which
: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change:[PR Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley,(eds.: Cambridge University Press, 2022, p. 503.[3] NSF, "STEM Education for the Future," 2020, Available: https://www.nsf.gov/edu/Materials/STEM%20Education%20for%20the%20Future%20- %202020%20Visioning%20Report.pdf.[4] NSF, "Women, Minorities, and Persons with Disabilities in Science and Engineering " in "National Center for Science and Engineering
attendance of a PA or FA to ASCE PFATW.BackgroundBenefit for studentsIn the current climate of higher education student success is a key conversation topic. Asstudents participate in activities beyond the classroom, they have a stronger educationalexperience and higher institutional satisfaction [11]. Student success can be attributed to theconnection and access students have to faculty advisor(s) and other student members [12], [13].These students develop practical competencies (i.e. soft or power skills) and build leadership thatmay not be included in their academic curriculum [14]. These skills can be practiced throughstudent organizations. These organizations provide an opportunity to grow future leaders byoffering opportunities to balance
Evaluation (-PE), which informs program administration. The student survey (S-)consists of four categories of questions, detailed in Appendix A: SSB, SCP, SPC and SPE. Thementor survey (M-) consists of three categories, Appendix B: MCP, MPC and MPE.Responses to questions use either a numerical five-point Likert scale or are open-endedresponse-based. The intern surveys are administered at the beginning, midpoint, and end of theinternship period. Mentor surveys are administered at the midpoint and end. Midpoint surveyshave fewer questions, and mainly serve as a check-in point. The majority of the SB, CP and PCquestions are posed at the beginning and end points of the internship period. Some CP questionsare posed only at the end point. PE questions are
future role of generative AI in creativity and design, how you might utilize what you have learned in the course, and reflections on what you are learning in the course and the creative process. You should plan on writing at least a few paragraphs in this section every week.Figure 2. Descriptions for the sections that constitute the Foundational Creativity part of the Creativity Portfolio. Part 2: AI + Creativity Now is the time to use AI. For this section, please use your preferred generative AI tool (examples include Microsoft Copilot, Gemini, and ChatGPT) and write down which one(s) you used. Please record every prompt and output (yes, these sections will be long). Make sure your prompts are
persistence and engagement. The program’smethods offer a replicable model for addressing the underrepresentation of economicallydisadvantaged students in engineering, contributing to a more diverse and inclusive workforce.AcknowledgementWe would like to acknowledge the support of the NFS SSTEM (NSF 22-527) for their Award ID2221623 which has created the opportunity for this work.References[1] Tonso, K. L. (2006a). Teams that work: Campus culture, engineer identity, and social interactions. Journal of Engineering Education, 95(1), 25.[2] Tonso, K. L. (2006b). Student Engineers and Engineer Identity: Campus Engineer Identities as Figured World. Cultural Studies of Science Education, 1, 273-307.[3] Corcoran, S., Birkner, J., & Brooking, G
is indicative of a potential answer to RQ2 but further work isneeded to both demonstrate the lack of understanding of centroids across the wider student body,and to trial mechanisms for promoting metacognition during problem solving.AcknowledgementsSupport for this work was provided by the National Science Foundation under Award No.2301341. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation. Research work was conducted under institutional IRB protocols, IRB#1965654.References1. Bransford, J. D. & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of
UniversityResearch and Evaluation and Assessment Services, which we especially acknowledge.References[1] K. L. Meyers, S. E. Silliman, N. L. Gedde, and M. W. Ohland, “A Comparison of Engineering Students' Reflections on Their First‐Year Experiences.” Journal of Engineering Education, 99(2), 169-178. 2010.[2] L. Santiago, Retention in a first-year program: Factors influencing student interest in engineering. In 2013 ASEE Annual Conference & Exposition, 2013. pp. 23-1045.[3] I. V. Ramteke, and J. Ansari, “Stress and Anxiety among first-year and final-year engineering students.” Stress, 3(4), 17-21. 2016.[4] Q. Tahmina, “Does Peer Mentoring Help Students be Successful in an Introductory Engineering Course?” In 2019 ASEE Annual Conference
: results from the project to assess climate in engineering," Journal of Engineering Education, vol. 101, no. 2, pp. 319-345, 2012.[6] National Center for Education Statistics (NCES), "Percentage of 2011-12 first time postsecondary students who had ever declared a major in an associate's or bachelor's degree program within 3 years of enrollment, by type of degree program and control of first institution," Institute of Education Sciences, U.S. Department of Education, Washington, D.C., 2017.[7] M. Ohland, C. Brawner, M. Camacho, R. Layton, R. Long, S. Lord and M. Wasburn, "Race, gender, and measures of success in engineering education," Journal of Engineering Education, vol. 100, no. 2, pp. 225-252, 2013.[8] J. Roy, A
-represented among those whocompleted the questionnaires and released data, compared to the course demographics. Of thosewho self-identified race, 161 (76%) were Asian, 50 (24%) were White, 10 (5%) wereLatino/Latina, 5 (3%) were Middle Eastern, 4 (2%) were Black, and 7 (3%) self-identified asmixed race or of another race(s). Of those who indicated the highest level of education completedby a parent(s), 26 (11%) were first-generation college students, defined as neither parent havingcompleted a bachelor’s degree.Questionnaire validation–ProcedureParticipants completed all measures in a Learning Management System over a 1-week periodbeginning the day their course grades on the first semester exam were released to them. Theyanswered all questionnaires
determineif the results are based on the field of study or just this individual course. In addition, it would beinteresting to determine if the results are only based on semiconductor manufacturing topic or ifthey apply to other engineering areas.References:[1] S. Kurinec et al., "Microelectronic engineering education for emerging technologies," in 2010 IEEE Frontiers in Education Conference (FIE), 2010: IEEE, pp. T3J-1-T3J-6.[2] A. J. Muscat, E. L. Allen, E. D. Green, and L. S. Vanasupa, "Interdisciplinary teaching and learning in a semiconductor processing course," Journal of engineering Education, vol. 87, no. 4, pp. 413-421, 1998.[3] S. Suteerawattananon, D. Prasertsom, J. Benjanarasut, B. Janthong, W. Kaewnet, and C
. Kolmos, "Emerging learning environments in engineering education,"Austral. J. Eng. Educ., vol. 25, no. 1, pp. 3–16, 2020.S. P. Hong, "Different numericaltechniques, modeling, and simulation in solving complex problems," J. Mach. Comput., vol. 3,no. 2, p. 58, 2023.[4] S. Huda, S. Alyahya, L. Pan, and H. Al-Dossari, "Combining Innovative Technology andContext Based Approaches in Teaching Software Engineering," Int. J. Adv. Comput. Sci. Appl.,vol. 13, no. 10, pp. 123-130, 2022.[5] M. S. Kleine, K. Zacharias, and D. Ozkan, "A scoping literature review on contextualizationin engineering education," J. Eng. Educ., vol. 113, no. 4, pp. 894–918, 2024.[6] J. E. Mills and D. F. Treagust, "Engineering education—Is problem-based or project-basedlearning the
initiated, the following components were identified as the core for thedevelopment.3.1. Objectives of the Project • Educational Enhancement: Provide educators with high-quality, accessible resources to improve classroom and laboratory instruction. • Outreach and Recruitment: Use engaging, practical demonstrations to attract prospective students to STEM disciplines. • Sustainability Awareness: Promote environmentally conscious practices in manufacturing and design. • Equity in Education: Bridge gaps in access to resources by providing affordable, scalable solutions like "foundry-in-a-box."3.2. Pedagogical ImpactProject R2’s resources are structured to cater to a wide range of learners, from high schoolstudents
completed and In Newhall et al.’s work at Swarthmore, educators recog- first employed in classrooms in Fall 2021.nized that minority retention was statistically low compared In this section we will describe the methodology used toto the rest of the student body.[4] They instituted a well- understand the impact of the blueprint, as well as presentingorganized student mentorship program in their CS I course, the data from student performance. We collected data onand received immediate positive feedback on the additional students’ course grades from Fall 2016 to Spring 2024. Theresource. They extended mentorship to CS II when they found data is split into two time ranges – student performance beforethat
Sayeg-S´anchez, Tecnologico de Monterrey (ITESM) Dr. Gibr´an Sayeg-S´anchez is professor – consultant in the Science Department in Tecnologico de Monterrey, Puebla campus. He studied a PhD in Financial Science in EGADE Business School (2016), a MSc in Industrial Engineering in Tecnologico de Monterrey (2011), and a BEng in Industrial and Systems Engineering in Tecnologico de Monterrey (2006). Dr. Sayeg-S´anchez has more than 11 years of experience in teaching statistics, mathematics, and operations research; and more than 13 years of experience in Operational Excellence consulting. His current research interests are focused in educational innovation and educational technologies.Ing. Luis Horacio Hernandez Carrasco
favorof there being multiple cognitive schemas available to a person depending on the specificsituation they are considering, although there can be a preferred schema. Despite the shift intheoretical frameworks, the DIT remained a primary assessment tool for studying moralreasoning, although the interpretation of results changed.The original DIT required test takers to read six stories concerning moral dilemmas and then rateand rank items related to the stories. In the 1990’s, the DIT was revised, producing the DIT-2,with new stories that reflected the changing social context [2].The original DIT used a numerical index, the P-score, that measured the percentage of post-conventional responses to a moral dilemma. The DIT-2 also uses the P-score
engagement, conceptual understanding, andacademic performance. Preliminary observations of selected artifacts indicate that additional datapoints are needed to further validate these perceptions. These findings support the broaderadoption of active learning in engineering education to better prepare students for the complex,interdisciplinary challenges of the field.References[1] S. A. Abdul‐Wahab, M. Y. Abdulraheem, and M. Hutchinson, "The need for inclusion of environmental education in undergraduate engineering curricula," International Journal of Sustainability in Higher Education, vol. 4, no. 2, pp. 126-137, 2003.[2] K. Jahan, J. W. Everett, R. P. Hesketh, P. M. Jansson, and K. Hollar, "Environmental education for all
. Weisenfeld, “Leveraging faculty knowledge, experience, and training for leadership education in engineering undergraduate curricula,” Eur. J. Eng. Educ., vol. 47, no. 6, pp. 950–969, Nov. 2022, doi: 10.1080/03043797.2022.2043243.[11] D. R. Graham, D. E. Crawley, and B. R. Mendelsohn, “Engineering leadership education: A snapshot review of international good practice”.[12] S. A. Bjorklund and C. L. Colbeck, “The View from the Top: Leaders’ Perspectives on a Decade of Change in Engineering Education,” J. Eng. Educ., vol. 90, no. 1, pp. 13–19, 2001, doi: 10.1002/j.2168-9830.2001.tb00562.x.[13] B. Wambeke, J. Sloan, T. Frank, and D. DePorres, “Student-to-Industry Interaction in a Civil Engineering Field Course: Benefits for
a diverse student body. Lastly, implementing robustfeedback mechanisms to continuously gather and analyze participant input will help refineprogram components and ensure sustained, long-term impact.AcknowledgmentWe thank Reboot Representation Tech Coalition for their support in advancing diversity in tech.Their funding and partnership have been key to LIFT’s success, aligning with their goal ofdoubling Black, Latina, and Native American women in computing by 2025—a milestonereached early [21]. Their contribution fosters a more inclusive, skilled workforce. In addition,this material is based upon work supported partially by the National Science FoundationScholarships in Science, Technology, Engineering, and Mathematics (S-STEM) program
Conference & Exposition, Portland, OR, June 23-26, 2024[2] R. D. Knight, “The vector knowledge of beginning physics students”, The Physics Teacher,vol. 33, p. 74, Feb. 1995, doi: 10.1119/1.2344143[3] I.A. Halloun and D. Hestenes, “The initial knowledge state of college physics students”, Am.Jour. Phys., vol. 53, pp 1043-1055, Nov. 1985, doi: 10.1119/1.14030[4] J.E. Bell, C. Cheng, H. Klautke, W. Cain, D.J. Freer, and T.J. Hinds, “A study of augmentedreality for the development of spatial reasoning ability” in 2018 Annual Conference &Exposition, Salt Lake City, UT, June 24-27, 2018.[5] J.E. Bell, T. Lister, S. Banerji, and T.J. Hinds, “A study of an augmented reality app for thedevelopment of spatial reasoning ability” in 2019 Annual
(1977): 191–215. doi:10.1037/0033-295X.84.2.191[8] Palmer, D. H., “Sources of Self-efficacy in a Science Methods Course for Primary Teacher Education Students”, Research in Science Education, vol. 36, no. 4, pp. 337–353, 2006. doi:10.1007/s11165-005-9007-0.[9] Guzey, S. S., Tank, K., Wang, H., Roehrig, G., and Moore, T. “A High-Quality Professional Development for Teachers of Grades 3 – 6 for Implementing Engineering into Classrooms” 114, no. 3 (2014): 139 – 149. doi:10.1111/ssm.12061[10] Lottero-Perdue, P. and Parry, E. A. “Elementary Teachers’ Reflections on Design Failures and Use of Fail Words after Teaching Engineering for Two Years” 7, no. 1 (2017): 1-College Engineering Education Research.[11
. Verde and J. M. Valero, "Teaching and Learning Modalities in Higher Education During the Pandemic: Responses to Coronavirus Disease 2019 From Spain," Frontiers in Psychology, Original Research vol. 12, 2021.[2] D. Bevitt, C. Baldwin, and J. Calvert, "Intervening Early: Attendance and Performance Monitoring as a Trigger for First Year Support in the Biosciences," Bioscience Education, vol. 15, no. 1, pp. 1- 14, 2010/06/01 2010, doi: 10.3108/beej.15.4.[3] A. Rughoo and D. Thomas, "Does Attendance affect Academic Achievement? Empirical Evidence from a U.K. Business School," 01/26 2021.[4] V. Kassarnig, A. Bjerre-Nielsen, E. Mones, S. Lehmann, and D. Lassen, "Class attendance, peer similarity, and
Topological Data Analysis. Studies in Engineering Education, 2(1), 16–34. https://doi.org/10.21061/see.18[2] Laursen, B.P., & Hoff, E. (2006). Person-Centered and Variable-Centered Approaches to Longitudinal Data. Merrill-Palmer Quarterly 52(3), 377-389. https://dx.doi.org/10.1353/mpq.2006.0029.[3] Morin, A. J. S., Bujacz, A., & Gagné, M. (2018). Person-Centered Methodologies in the Organizational Sciences: Introduction to the Feature Topic. Organizational Research Methods, 21(4), 803-813. https://doi.org/10.1177/1094428118773856[4] Aflaki, K., Vigod, S., & Ray, J. G. (2022). Part I: A friendly introduction to latent class analysis. Journal of Clinical Epidemiology, 147, 168–170. https://doi.org/10.1016/j.jclinepi
Paper ID #49597A YOLO-Based Semi-Automated Labeling Approach to Improve Fault DetectionEfficiency in Railroad VideosDylan Lester, Marshall University Dylan Lester is a third-year Electrical and Computer Engineering student and research assistant at Marshall University, with a research focus on machine learning.Prof. Pingping Zhu, Marshall University Prof. Pingping Zhu is an assistant professor in the Department of Computer Sciences and Electrical Engineering at Marshall University.Dr. Husnu Saner Narman, Marshall University Dr. Husnu S. Narman is an Associate Professor in the Department of Computer Sciences and Electrical
a description of how the budget will be used. a. For continuing project proposals: How does your project build on last year’s project? (Recommended: use your previous project’s evaluations, outcomes, and/or impact.)4. Project Rationale: How does your project support broadening participation in engineering?5. Project Audience: Faculty, Staff, Undergraduate Students, Graduate Students, Community Partners, etc.6. Project Category: E.g., improved support of graduate or undergraduate education, departmental culture, understanding areas for improved student support, mentoring practices, and student recruitment practices7. Research Question(s): What question(s) do you seek to answer with this project?8. Metrics
improveoperations, reduce waste, and enhance innovation. This study highlights the power of decisionscience in manufacturing, helping firms make informed, strategic choices in a complex,competitive industry.References1. KEEN. (n.d.). Curiosity. Engineering Unleashed. https://engineeringunleashed.com/curiosity2. Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of Curiosity. Neuron, 88(3), 449–460. https://doi.org/10.1016/j.neuron.2015.09.0103. Walden, D. D., Shortell, T. M., Roedler, G. J., Delicado, B. A., Mornas, O., Yip, Y. S., & Endler, D. (Eds.). (2023). INCOSE systems engineering handbook (5th ed.). John Wiley & Sons Ltd4. McElroy, T., & Seta, J. J. (2023). Framing the frame: How task goals determine the
-human transference system encompasses user inter-action mechanisms, real-time control pathways, parameter sharing between local and cloud AImodels, and an ethical optimization process that integrates user satisfaction and privacy safeguards.This section outlines the principal equations that govern how user inputs and system states flowthrough the AI middleware, how control signals are assigned to local and cloud components, andhow experiential knowledge is updated across different domains.First, let us define the user interactions across multiple modalities, such as text or speech: (m) (m) S(t
optimal performance. These insights emphasize the importance ofengineering education programs that guide students in understanding how to select and integrateappropriate technologies based on specific application needs. In the long term, programs likeiEDGE will help in reshaping and cultivating a workforce that bridges the theory-practice divideand drives impactful advancements in edge computing and beyond.AcknowledgmentThis research is supported by National Science Foundation grants 2348711. Opinions expressedare those of the author(s) and do not necessarily reflect NSF’s views.References[1] Z. Sun, X. Zhang, T. Wang, and Z. Wang, “Edge computing in Internet of Things: A novelsensing-data reconstruction algorithm under intelligent-migration