-O.-13 Rep. Ryan, Chips and Science Act. 2022. Accessed: May 09, 2024. [Online]. Available: https://www.congress.gov/bill/117th-congress/house-bill/4346[3] “Chipping Away: Assessing and Addressing The Labor Market Gap Facing the U.S. Semiconductor Industry,” Semiconductor Industry Association & Oxford Economics, Jul. 2023. Accessed: May 09, 2024. [Online]. Available: https://www.semiconductors.org/chipping-away-assessing-and-addressing-the-labor- market-gap-facing-the-u-s-semiconductor-industry/[4] S. Kurinec, M. Jackson, T. Schulte, N. Kane, E. Lewis, and S. Gupta, “Microelectronic Engineering And Nanotechnology Education For Undergraduates And Pre College Students Through Curriculum
? The Implications of Large Language Models for Medical Education and Knowledge Assessment,” JMIR Med Educ, vol. 9, 2023, doi: 10.2196/45312.[10] D. M. Katz, M. J. Bommarito, S. Gao, and P. Arredondo, “GPT-4 passes the bar exam,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 382, no. 2270, Apr. 2024, doi: 10.1098/rsta.2023.0254.[11] M. Kaushik, P. Baligar, and G. Joshi, “Formulating An Engineering Design Problem: A Structured Approach,” 2018.[12] B.-A. Schuelke-Leech, “A Problem Taxonomy for Engineering,” IEEE Transactions on Technology and Society, vol. 2, no. 2, pp. 105–105, Apr. 2021, doi: 10.1109/tts.2021.3072213.[13] D. H. Jonassen
mechanical engineering: A guidebook for teaching and learning. Springer Nature Switzerland AG, 2022. DOI 10.1007/978-3-030-85390-7[5] J. Murray, L.C. Paxson, S. Seo and M. Beattie, “STEM-Oriented Alliance for Research (SOAR): An educational model for interdisciplinary project-based learning”. 2020 ASEE Virtual Annual Conference Content Access. DOI:10.18260/1-2—35206[6] A. Carvalho Alvesa, F. Moreiraa, M.A. Carvalhoa, S. Oliveiraa, M.T. Malheiroa, I. Britoa, C. Pinto Leãoa and S. Teixeiraa, S. “Integrating STEM contents through PBL in an Industrial Engineering and Management first year program”. Scientific Electronic Library Online, Brazil. Production (29). 2019. Article e20180111
Laboratory Course Development Story Matthew S. Kuester Computer Science, Engineering, and Physics Department University of Mary Hardin-Baylor AbstractFluid mechanics laboratory is a common component of mechanical engineering curricula, becausehands-on experiments allow students to experience key fluid mechanics principles (such as fluidstatics, Bernoulli’s equation, and conservation of energy) in a meaningful way. Establishing a newlaboratory course provides a unique set of challenges (building and selecting new equipment) andpossibilities (creating engaging, practical learning experiences for students).This paper
vehicle may alsobe conducted. Lastly, units other than the Gaged GX9 may be adapted and used on the system,and these results compared to those presented in this work.VII. eferences [1] O. Aaen, Olav Aaen's Clutch Tuning Handbook, Aaen Performance, 2007. [2] C. R. Willis, A Kinematic Analysis and Design of a Continously Variable Transmission, Blacksburg, VA: Virginia Polytechnic and State University, 2006. [3] S. S. Skinner, Modeling and Tuning of CVT Systems for SAE Baja Vehicles, Graduate Theses, Dissertations, and Problem Reports, no. 7590, 2020. [4] J. H. Gibbs, Actuated Continously Variable Transmission for Small Vehicles, Akron, OH: University of Akron, 2008. [5] K. Dobaj, W. Szczypiński-Sala and A. Kot, Frictional Problems
. The output (yi) of each neuron (excluding the input neuron)C. Robot Test Bed Firmware can be summarized by Eq. [9]: At the highest level, the firmware in the dsPIC33 containeda simple looped task structure, directing the robot to perform xj [s]=A ( Σi ( xj [s-1]× wji [s] )) (1)whichever task was being requested. Tasks were called basedon internal and external robot events (i.e. serial packets, where wji is the weight value from the previous layer to thedetected objects). Even though the robot was wirelessly current layer (which is different for each synapse), xj is theconnected to a computer running the robot control program, value of the jth
, 2010, Forthcoming.[2] S. Cheryan, S. A. Ziegler, A. K. Montoya, and L. Jiang, “Why are some STEM fields more gender balanced than others?” Psychological bulletin, 143(1), 1-35, 2017, https://doi.org/10.1037/bul0000052[3] J. M. Jebsen, K. Nicoll Baines, R. A. Oliver, and I. Jayasinghe, “Dismantling barriers faced by women in STEM,” Nature Chemistry, 14(11), 1203-1206, 2022, https://doi.org/10.1038/s41557-022-01072-2[4] C. O’Connell, and M. McKinnon “Perceptions of barriers to career progression for academic women in STEM,” Societies, 11(2), 27, 2021, https://doi.org/10.3390/soc11020027[5] M. Swafford and R. Anderson, “Addressing the Gender Gap: Women's Perceived Barriers to Pursuing STEM Careers,” Journal of Research in
education, and 4) an emphasis ofavailable medical and STEM education resources for SVSM. Then, as a team, we formedupdated categories that became the structure of the training itself: Background, transition,resources, and a call to action.Beginning with a general overview section, the purpose behind the training along with settingdesired intentions with the audience, along with a pre-survey to gauge initial participantunderstanding sets a tone and creates a sense of direction. This section can also be utilized tointroduce the speaker(s), rationale for why the training is being presented to the given audience,and positionality of those presenting, although time is also reserved for the speaker(s) tointroduce positionality as an activity via the
understanding how AI-powered tools can enhance learning experiences, particularlyin fields requiring tailored support, such as engineering education.AcknowledgementsThis material is based upon work supported by the National Science Foundation under MCAGrant No. 2120888. The first author (MV) was supported by an NSF Research Traineeship(TRANSCEND) under Grant No. 2152202 at the time this research was conducted. 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.The authors greatly appreciate the support of Trent Alsup, Jada Vercosa, Brian Hance, andAbhiram Gunti in the initial development of the GPT platform.Finally
laboratoriesacross different CEM programs could facilitate broader adoption and continuous improvement ofthese teaching methods. Figure 11. Rebar tying gunAcknowledgementThis work was supported by a grant from the Collier Building Industry Association (CBIA) andCollier Building Industry Foundation (CBIF). The authors gratefully acknowledge CBIA andCBIF's support in developing and implementing the hands-on laboratory components of thestructures for construction course.Reference[1] J. C. Hayes and D. J. M. Kraemer, “Grounded understanding of abstract concepts: The case of STEM learning,” Cognitive Research: Principles and Implications, vol. 2, no. 1, p. 7, Jan. 2017, doi: 10.1186/s41235-016-0046-z.[2] S. Persaud and M
to assess the frequency ofeducational technology use in the classroom and the instructors' beliefs related to learner-centered instruction. Houseknecht et al. [20] adopted Walters et al.’s [21] PostsecondaryInstructional Practices Survey (PIPS) to measure the teaching practices of university chemistryinstructors. Compared to the inventory, the survey instruments are designed over a theoreticalframework and can be organized into factors related to the assessed constructs. While inventoriescan characterize classroom teaching, surveys can be more specific and include constructs such asself-efficacy or teaching beliefs in their design. Classroom Observation: Classroom observation instruments referred to the set ofobservation protocols to
Paper ID #47009BOARD # 71: Integrating Machine Learning into Middle and High SchoolCurricula using Alzheimer’s Disease Prediction ModelsDr. Tayo Obafemi-Ajayi, Missouri State University Dr. Tayo Obafemi-Ajayi is an Associate Professor of Electrical Engineering at Missouri State University in the Engineering Program, a joint program with Missouri University of Science and Technology (S&T). She obtained her B.S and MS in Electrical Engineering and a PhD in Computer Science from Illinois Institute of Technology.Dr. Naomi L Dille, Missouri State UniversityDhanush Bavisetti, Missouri State UniversityMrs. Sherrie Ilene Zook
Total (m/s) (W/m2 ) (KW) (KW) (KW) (KW) (KW) (KW) Psource Pload 5 800 0 9.62 21.23 8.97 11.94 9.94 30.85 30.85 5 600 0 7.08 23.77 8.97 11.94 9.94 30.85 30.85 5 300 0 3.35 27.50 8.97 11.94 9.94 30.85 30.85 10 300 4.32 3.35 23.18 8.97 11.94 9.94 30.85 30.85 12 300 6.90 3.35 20.60 8.97 11.94 9.94 30.85 30.85 15 300 10.03 3.35 17.47 8.97
curriculum toprovide a well-rounded education. Moreover, leadership training should extend beyondtheoretical instruction to include practical applications that demonstrate its relevance inreal-world contexts. As highlighted in the literature, integrating structured leadershipdevelopment programs into the curriculum can significantly enhance the comprehensiveeducation of engineering professionals, particularly by strengthening their leadership skills.References[1] D. Magrane, P. S. Morahan, S. Ambrose, and S. A. Dannels, "Competencies and Practices in Academic Engineering Leadership Development: Lessons From a National Survey," Social Sciences, vol. 7, no. 10, p. 171, Sept. 2018, doi: 10.3390/socsci7100171.[2] S. J. Perry, E. M. Hunter, S
://files.eric.ed.gov/fulltext/EJ1273006.pdf.[2] Supernak, J., Ramirez, A., & Supernak, E. (2021). COVID-19: how do engineering students assess its impact on their learning?. Advances in Applied Sociology, 11(1), 14-25. https://doi.org/10.4236/aasoci.2021.111002.[3] Baltà-Salvador, R., Olmedo-Torre, N., Peña, M., & Renta-Davids, A. I. (2021). Academic and emotional effects of online learning during the COVID-19 pandemic on engineering students. Education and information technologies, 26(6), 7407-7434. https://doi.org/10.1007/s10639-021-10593-1.[4] Asgari S, Trajkovic J, Rahmani M, Zhang W, Lo RC, Sciortino A (2021) An observational study of engineering online education during the COVID-19 pandemic. PLoS ONE
understanding of how Python programs communicate withhardware for execution. Additionally, this novel test case evaluated the MTR staff’s learning outcomesby applying their knowledge in practice. There are two main difference s: first, the use of a new robotarm with a different configuration—characterized by six degrees of freedom, mechanical limits, andvarying lengths of each link—affects reachability and requires adaptations in the movement programs.Second, the software package created by the university students necessitated that the cohort modifytheir existing code to utilize the functions provided by this new package. Despite these challenges, thecohort successfully completed the tasks in just one morning, demonstrating their ability to apply
codeswere collaboratively compared and discussed, and final coding was unanimously agreed upon.The results and associated discussion focus on three participants from this 16-student cohort,focusing on the academic system context(s) that are potentially malleable by universities. Theselected participants include an African-American female, an African-American male, and aCaucasian female. These students were chosen per the following criteria: a) member of anunderrepresented group in engineering, b) a substantial number of codable comments from theinterview transcript, and c) cumulative coded transcript comments that are overall representativeof comments from the larger, 16-member cohort.ResultsResults are presented for the three participants
encounter along with a tendency to engage inengineering-like tasks from an early age [10]. These attitudes are likely to positively affectstudents’ engagement with their engineering studies. More research is needed to betterunderstand whether these factors play a similar role for transfer students and which other assetsand challenges are relevant to their unique engineering journeys.Thus, the current study aims to explore the challenges and obstacles along with the invaluablepersonal assets of engineering transfer students enrolled in an NSF S-STEM scholarship programfor academically talented, community college transfer students with unmet financial needs fromdiverse backgrounds. More specifically, we analyzed essays from 122 engineering
://apcentral.collegeboard.org/about-ap/news-changes/ap-score-reports (accessed Dec 15, 2024).[7] T. Fernandez et al., "More comprehensive and inclusive approaches to demographic data collection," presented at the 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana, Jun 26-29, 2016. [Online]. Available: https://docs.lib.purdue.edu/enegs/60/.[8] D. Collier, J. Mahoney, and J. Seawright, "Claiming too much: Warnings about selection bias," Rethinking social inquiry: Diverse tools, shared standards, pp. 85-102, 2004.[9] S. Keeter, C. Kennedy, D. Michael, J. Best, and C. Peyton, "Gauging the Impact of Growing Nonresponse on Estimates from a National RDD Telephone Survey," The Public Opinion Quarterly, vol. 70, no
publication timeline andtrends, workforce populations studied, types of non-degree credential(s) implemented, industrysectors targeted, and NDC challenges and gaps identified. These preliminary findings providebroader insights for educators, industry leaders, and policymakers currently engaged in shapingthe future landscape of STEM workforce development and for identifying directions for futureresearch in this emerging area of importance to the STEM workforce.BackgroundHigher education degree programs have historically played a critical role in workforcedevelopment in the United States by providing a mechanism for knowledge and skill acquisitionthat is purported to lead to meaningful employment and higher salaries. NDCs play a valuablerole in STEM
at least 10 participants. In Summer 2025, we will analyzethe qualitative data and compare it with the quantitative results. In the final phase of our research(Fall 2025 – Spring 2026), we will focus on preparing publications, presenting at conferences,and planning future research initiatives to continue this work.AcknowledgementsFunding was provided by National Science Foundation grant EEC- 2306099.References[1] A. Godwin, “The development of a measure of engineering identity,” in 2016 ASEE annual conference & exposition, New Orleans, Louisiana, 2016.[2] J. P. Martin, D. R. Simmons, and S. L. Yu, “The Role of Social Capital in the Experiences of Hispanic Women Engineering Majors,” J. Eng. Educ., vol. 102, no. 2, pp. 227–243
significantly, as seen in Figure 2, demonstrating the limitations of generativeAI in solving problems in aerospace engineering. 100 90 80 70 60 50 40 30 20 10 0 Remember Understand Apply Analyze ChatGPT-4 Gemini Fig. 2. Percent correct by ChatGPT-4 and Gemini for each question type.The “Remember” level of Bloom’s taxonomy relies on recalling of previously learned facts andconcepts. Chat GPT-4’s
[5]. Studies have shown that GTAs feelunderprepared to evaluate communication aspects of technical assignments, and withoutstructured guidance, their feedback tends to focus primarily on technical accuracy rather than onthe clarity, structure, or effectiveness of the communication itself [6, 8, 16]. Additionally, thelack of communication-focused training for GTAs perpetuates the broader issue ofcommunication instruction being marginalized in engineering programs, as it reinforces theperception that communication is a secondary skill, rather than an integral part of engineeringpractice [6]. Rodger et al. (2014)’s analysis of their “TA’s [sic] feedback revealed that themajority of the TA’s written feedback was either copied directly from the
). Minder’s thesisfocused on how standards are published, indexed, advertised, and distributed. He did not addresshow libraries collect, organize, or use standards.A. S. Tayal of the Indian Standards Institution recommended in 1961 that libraries shouldclassify and catalog standards under the classification system they use for other materials andthat standards should be shelved in three groups: national standards, standards from governmentagencies, and standards from other organizations [29].Methodology and LimitationsThe author’s first task was to compile an inventory of CSA standards published from 1920through 1949. This proved challenging as CSA did not publish a catalog of standards until the1950s. The author reached out to CSA several times and
-research question from theperspective of the faculty advisor.RQ1. What factors influence underserved [Ph.D. graduate student(s)/faculty advisor(s)] as theyengage in mentoring relationship? Sub-RQ1. What does it mean to be a [Ph.D. graduate student/faculty advisor] in amentoring relationship in your field? Sub-RQ2. How does hidden curriculum influence the role of a [Ph.D. graduatestudent/faculty advisor] in a mentoring relationship in your field?RQ2. What does it mean to address issues that may arise in a mentoring relationship between aPh.D. graduate student and faculty advisor in your field?Research Paradigm and Interpretive Framework This study is positioned from an epistemological philosophical perspective within
, “Misconceptions or p-prims: How may alternative perspectives of cognitive structure influence instructional perceptions and intentions?” Journal of the Learning Sciences, vol. 5, no. 2, pp. 97–127, 1996. [3] R. Even and T. Wallach, “What does it mean to interpret students’ talk and actions?” in Proceedings of the 30th International Conference for the Psychology of Mathematics Education, vol. 1. International Group for the Psychology of Mathematics Education, 2006, p. 355. [4] S. Vosniadou, “Reframing the classical approach to conceptual change: Preconceptions, misconceptions and synthetic models,” Second international handbook of science education, pp. 119–130, 2012. [5] R. Liu, R. Patel, and K. R. Koedinger, “Modeling common
education, ultimately preparing students for a rapidly evolvingtechnological landscape.References[1] M. R. Chavez, T. S. Butler, P. Rekawek, H. Heo and W. L. Kinzler, "Chat Generative Pre-trained Transformer: why we should embrace this technology," American Journal of Obstetrics and Gynecology, vol. 228, no. 6, pp. 706-711, 2023.[2] G. Debjania and J.-B. Souppeza R. G., "Generative AI In Engineering Education," in UK and Ireland Engineering Education Research Network Annual Symposium, Belfast, 2024.[3] A. Johri, A. S. Katz, J. Qadir and A. Hingle, "Generative artificial intelligence and engineering education," Journal of Engineering Education, vol. 112, no. 3, p. 572–577, 2023.[4] D. De Silva, O. Kaynak, M. El-Ayoubi, N. Mills, D
the manifestation of White supremacy and antiracism is the answer,” Journal of Engineering Education, vol. 109, no. 4, pp. 625–628, 2020, doi: 10.1002/jee.20362.[2] S. Secules, “Making the familiar strange: An ethnographic scholarship of integration contextualizing engineering educational culture as masculine and competitive,” Engineering Studies, vol. 11, no. 3, pp. 196–216, 2019, doi: 10.1080/19378629.2019.1663200.[3] D. Riley, “Engineering and Social Justice,” Synthesis Lectures on Engineers, Technology and Society, vol. 3, no. 1, pp. 1–152, 2008, doi: 10.2200/S00117ED1V01Y200805ETS007.[4] E. Seymour and A. Hunter, Talking about Leaving Revisited.[5] K. L. Tonso, “Teams that work: Campus culture, engineer identity
, and that they are specific to our college’s program content andgoals. Yet, the results of the present study can be informative to the assessment and value ofsimilar programs to student success in college.ReferencesArof, K. Z. M., Ismail, S., & Saleh, A. L. (2018). Contractor’s performance appraisal system inthe Malaysian construction industry: Current practice, perception andunderstanding. International Journal of Engineering & Technology, 7(3.9), 46–51.Ashley, M., Cooper, K. M., Cala, J. M., & Brownell, S. E. (2017). Building better bridges intoSTEM: A synthesis of 25 years of literature on STEM summer Bridge programs. CBE—LifeSciences Education, 16(1), es3.Baker, R. W., & Siryk, B. (1984). Measuring adjustment to college