Paper ID #45650Relating Kinetic Energy Changes to Power Generation in a Mechanical EngineeringWind Turbine LabDr. Chuck H. Margraves, University of Tennessee at Chattanooga Dr. Chuck Margraves is a UC Foundation Associate Professor and Graduate Coordinator of Mechanical Engineering at the University of Tennessee at Chattanooga. His current research focus is on STEM Education, particularly in the area of energy sustainability, at the collegiate and high school levels.Prof. KIDAMBI SREENIVAS, University of Tennessee at ChattanoogaTrevor S. Elliott, University of Tennessee at ChattanoogaLance Isaac Rose, University of Tennessee at
, certificates in Organizational Leadership and Technical Project Management, and a Bachelor of Science in Business Administration from Strayer University.Dr. Andrew B. Williams, The Citadel Andrew B. Williams, Ph.D. is the Dean of Engineering and the Louis S. LeTellier Chair at The Citadel School of Engineering. Dr. Williams is an alumni of the National Academy of Engineering Frontiers in Engineering Symposium and the National GEM Consortium Ph.D. in Engineering Program. He received both his Ph.D. in Electrical Engineering with an emphasis in AI and his BSEE from the University of Kansas.Dr. Kevin Skenes, The Citadel Kevin Skenes is an associate professor at The Citadel. His research interests include non-destructive
b since using “Add Trendline” cannot Table 1: Record the time for specific heights of the water during an experiment Time (s) Height (cm) 12 11 10 9 8 7 6 5 4 3
’ comprehension of NLP, preparing them forfuture developments in the subject and developing the practical skills necessary for their jobs.Keywords: Natural Language Processing (NLP), Undergraduate Education, Interactive Tools, PythonLibraries, Interdisciplinary Case Studies.1 IntroductionThe rapid advancement of digital technology, especially in artificial i ntelligence ( AI), i s s ignificantly re-shaping the landscape of higher education. Traditional lecture-centered teaching is increasingly being sup-plemented by dynamic, technology-enhanced approaches. In today’s education, AI-powered platforms andvirtual learning environments have become essential, leading to a new emphasis on adaptable, personalizedlearning experiences that cater to diverse
entire MLprocess, fostering computational thinking and problem-solving [18]. Kajiwara et al. employed agamified ML role-playing game, simplifying concepts for high school students [15]. Ethicalconsiderations were integrated through projects like VotestratesML, which explored AI's societalimpacts in democratic contexts [20], and Kong et al.’s collaborative projects addressing fairnessand bias in AI systems [16].3.5 Results for RQ4: Which of the AI4K12 Five Big Ideas frameworks are being included?The AI4K12 Five Big Ideas rubric assessed studies on Perception, Representation & Reasoning,Learning, Natural Interaction, and Societal Impact, scoring from 0 (not addressed) to 4 (thoroughintegration). Results highlighted strengths in Learning
conclusions.Reference[1] Martin, M.J., S.J. Diem, D.M.A. Karwat, E.M. Krieger, C.C. Rittschof, B. Bayon, M.Aghazadeh, O. Asensio, T.J. Zeilkova, and M. Garcia-Cazarin, The climate is changing.Engineering education needs to change as well. Journal of Engineering Education, 111:740-746,2022.[2] Milovanovic, J., T. Shealy, and A. Godwin, Senior engineering students in the USA carrymisconceptions about climate change: Implications for engineering education. Journal ofCleaner Production, 345, 131129, 2022.[3] Sinatra, G.M., and P.R. Pintrich (Eds.), International Conceptual Change. Lawrence Erlbaum,Mahwah, NJ, 2003.[4] P. Molthan-Hill, L. Blaj-Ward, M. F. Mbah, and T. S. Ledley, “Climate Change Education atUniversities: Relevance and Strategies for Every Discipline
Paper ID #45761A Gender-based Comparative Analysis of Motivations and Challenges in ConstructionEducationDr. Saeed Rokooei, Mississippi State University Saeed Rokooei is an associate professor in the Department of Building Construction Science at Mississippi State University. Dr. Rokooei’s primary research interests include community resilience, engineering education, simulation and serious games, project management methodologies, data analytics, creativity and innovation, and emerging technologies.Mr. George D Ford P.E., Mississippi State University Dr. George Ford P.E. is the Director of Mississippi Stateˆa C™s Building
studies could also address the impacts of team dynamics such assize, communication and leadership on the application of requirements tools and evolution [18],[19]. These studies would enable further assessment of the impact of QFD on requirementsevolution in capstone product design.References[1] D. G. Ullman, The Mechanical Design Process, 6th ed. Independence, Oregon: David G. Ullman, 2018.[2] G. Pahl and W. Beitz, Engineering Design: A Systematic Approach, 2nd ed. London: Springer, 1995.[3] B. Morkos, S. Joshi, and J. D. Summers, “Investigating the impact of requirements elicitation and evolution on course performance in a pre-capstone design course,” Journal of Engineering Design, vol. 30, no. 4–5, pp. 155–179
. Pallitt and K. Wolff, "Learning to teach STEM disciplines in higher education: A critical review of the literature," Teaching in Higher Education, vol. 24, no. 8, pp. 930-947, 2019.[2] D. Varas, M. Santana, M. Nussbaum, S. Claro and P. Imbarack, "Teachers’ strategies and challenges in teaching 21st century skills: Little common understanding," Thinking Skills and Creativity, vol. 48, p. 101289, 2023.[3] H. Jang, "Identifying 21st century STEM competencies using workplace data," Journal of Science Education and Technology, vol. 25, no. 2, pp. 284-301, 2016.[4] D. Tan, "The Significance of Integrating Engineering Design-Based Instruction in STEM Education," Science Insights Education Frontiers, vol. 24, no. 1, pp. 3827-3829, 2024
a wider range of structural elements and incorporating interactive features likequizzes and feedback would further enhance its educational value. Comparative studies withcontrol groups using traditional learning methods would also help clarify the specific advantagesof AR-based learning in civil engineering education.References[1] ACI, Building code requirements for structural concrete (ACI 318-08) and commentary. American Concrete Institute, 2008. Accessed: Nov. 08, 2024. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=c6yQszMV2- EC&oi=fnd&pg=PT10&dq=Building+Code+Requirements+for+Structural+Concrete+and+ Commentary&ots=nZOlIXZCKL&sig=KMB7MQU6EE9dIpxctdYQvpox8Ws[2] S. A. Sorby
, investment and technology can reduce these expenses over time,while ash byproducts from combustion are repurposed, further minimizing landfill waste.2.4 Byproducts of BiomassWhen wood-based Biomass is burned, fly ash is the primary byproduct, along withemissions like CO₂, CO, CH₄, NOx, VOCs, PM, and trace gases [25]. Fly ash hassignificant potential as a cement substitute in concrete. Rummen et al. demonstrated thatadding 15% wood-based fly ash (WFA) improves concrete durability and compressivestrength due to pozzolanic reactions forming calcium silicate hydrate (C-S-H) gel, a keystrength component [26]. Similarly, John Zachar's study found that replacing 30% ofcement with fly ash in construction reduced material costs by $23,000 and avoided
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
thedata. Anomaly scores are computed based on the number of splits required to isolate a data point. E(x)Mathematically, the anomaly score s(x) for a data point x is calculated as: s(x) = 2− c(n) , whereE(x) is the average path length for x, and c(n) is the average path length of a randomly selectedpoint in a sample of size n.Predictive Analytics: AI models like Long Short-Term Memory (LSTM) networks forecast futuresoil conditions. LSTMs are a type of recurrent neural network (RNN) that can learn long-termdependencies. The LSTM network uses gates to control the flow of information. The update rulefor the LSTM cell state Ct at time t is given by
growth.In conclusion, introducing Lean Systems tools within the ENGR 1210 course proved to be avaluable approach for helping students analyze their challenges systematically. By continuing toimplement these problem-solving frameworks, alongside targeted support services, the universitycan better equip freshmen to succeed academically and personally.References[1] S. McKay, “Quality Improvement Approaches: Lean for Education,” Carnegie Foundation forthe Advancement of Teaching (Blog/Improvement in Action), 2017.[2] S. Ihsan and O. Khalifa, “Continuous Quality Improvement Strategies in EngineeringCurriculum,” Proceedings of 2nd International Conference on Professional Ethics and Educationin Engineering, Kuala Lumpur, Malaysia, 2011.[3] V. Narulaa, and
strategies toboost preparation and participation, thereby enhancing learning outcomes across engineeringsubjects.Due to the small sample size, it is challenging to make conclusive recommendations based on theobservations. The results of this study, limited to the data from 2022-2023, should not begeneralized to broader conclusions. Further data collection and analysis over several more courseofferings are necessary to draw informative conclusions. Future studies should encompassdifferent courses with larger sample sizes. Engineering faculty can create a more engaging andeffective learning environment in their courses by incorporating the strategy used in this study.References[1] Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N
. “The renaissance foundry: A powerful learning and thinking system to develop the 21st century engineer,” Critical Conversations in Higher Education, 1(2), 2015, 176-202. 6. V. Matthew, S. Lipkin-Moore, P. E. Arce, A. Arce-Trigatti, N. Lavoine, L. Lucia, E. Selvi, M. Eggermont, M. Tiryakioglu, J. Hall, R. Edelen, and J. Plumblee. “A Roadmap for the Design and Implementation of Communities of Practice for Faculty Development,” Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 2022, https://peer.asee.org/40564 7. K. Pabody, C. Wilson, A. Arce-Trigatti, P. E. Arce, S. H. Buer, A. Haynes, R. Chitiyo, J. R. Sanders, and T. Smith. “The Renaissance Foundry Model and culturally
Engineering Education,” 2022. Retrieved from https://engineeringforoneplanet.org/wp- content/uploads/2022_EOP_Framework_110922.pdf 4. P. E. Arce, J. R. Sanders, A. Arce-Trigatti, L. Loggins, J. Biernacki, M. Geist, J. Pascal, and K. Wiant. “The renaissance foundry: A powerful learning and thinking system to develop the 21st century engineer,” Critical Conversations in Higher Education, 1(2), 2015, 176-202. 5. V. Matthew, S. Lipkin-Moore, P. E. Arce, A. Arce-Trigatti, N. Lavoine, L. Lucia, E. Selvi, M. Eggermont, M. Tiryakioglu, J. Hall, R. Edelen, and J. Plumblee. “A Roadmap for the Design and Implementation of Communities of Practice for Faculty Development,” Paper presented at 2022 ASEE Annual Conference
+( ) 𝐸2 𝑁A confidence interval (𝑧) of 95% is chosen, yielding Z-values of 1.96, with a margin of error (𝐸)of 4%-8%, which corresponds to the 95% confidence interval [13]. The standard deviation (𝜎) isestimated to be 1, given the small population size. After n surveys have been completed, thesample standard deviation (s) will be calculated and a confidence interval for σ will bedetermined to ensure that the correct number of samples are take. N is the total population size ofall the students exposed to the flipped classroom with alternate instructors. Three statisticalmethods of means, materiality, statistical relations, and Cronbach’s Alpha, illustrated in Equation(2), will be used to analyze and understand the results of the survey
evaluations ofteaching, course surveys, or simply teaching evaluations) have been used for assessing teachers’effectiveness in one form or another since the 1920’s. In many cases, though, modernassessment has relied far too heavily on student opinions as though it were a comprehensiveassessment of teaching effectiveness and student learning [2], when in fact, there are numerousapproaches to evaluate teaching more holistically. Other common strategies for teachingevaluation include peer observation (by fellow faculty members), external review (often byexperienced teaching and learning professionals), and self-evaluation. In each case, modernapproaches center on evidence-based evaluation practices [3], and several examples arediscussed herein.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
in the Systems Engineering program in the department ofEngineering at the University of South Alabama. The six students that comprise the pilot classhave backgrounds in Computer Science, Artificial Intelligence, Electrical Engineering, CivilEngineering, Process and Control Engineering, and Forensics. Table 1 - Course Structure and ContentWeek Topic Sub-topic(s) Objectives 1 Motivation/Application Vocabulary Student shall be able to distinguish and Readings define the differences between Short Course intelligent digital twin
writing In-class activity2.1 Week 1: First In-person Meeting Activity: Setting Up Your Goal2.1.1 Use of MentimeterIn the first in-person class, the course expectations are introduced. A Mentimeter is used to makethe session interactive and engaging. The following questions are asked during the first meeting,allowing students to see their responses in real-time: How are you today? Use one word todescribe how you feel now. How do you rate your current writing skill? (0-100 points). Howmany journal articles (not including conference presentations) have you published so far? Whatare your expectations for this course? Have you used AI (e.g. ChatGPT) in your academic work?Which area(s) do you find challenging when starting to write? How are
non-traditional active military and Veteran student groups.References[1] S. E. Lewis, "Retention and Reform: An Evaluation of Peer-Led Team Learning," Journal of Chemical Education, vol. 88, no. 6, pp. 703-070, 2011.[2] L. Gafney and P. Varma-Nelson, Peer-Led Team Learning Evaluation, Dissemination, and Institutionalization of a College Level Initiative, Springer Science & Business Media, 2008.[3] J. Liou-Mark, A. E. Dreyfuss and L. Younge, "Peer Assisted Learning Workshops in Precalculus: An Approach to Increasing Student Success," Mathematics & Computer Education, vol. 44, no. 3, p. 249, 2010.[4] M. Hernandez-de-Menendez, A. V. Guevara, J. C. T. Martinez, D. H. Alcantara and R. Morales-Mendez, "Active learning in
careers.Acknowledgment: The authors thank the U. S. National Science Foundation for sponsoring theresearch through a grant NSF-ITEST-1949493.References[1] S. Lucci, S. M. Musa, and D. Kopec, “Artificial Intelligence in the 21st Century,” pp. 1– 850, 2022, Accessed: Nov. 04, 2024. [Online]. Available: https://www.torrossa.com/en/resources/an/5671176[2] I. Lee, S. Ali, H. Zhang, D. DiPaola, and C. Breazeal, “Developing Middle School Students’ AI Literacy,” in Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, Virtual Event USA: ACM, Mar. 2021, pp. 191–197. doi: 10.1145/3408877.3432513.[3] T. Kurz, S. Jayasuriya, K. Swisher, J. Mativo, R. Pidaparti, and D. T. Robinson, “Investigating Changes
their usein other courses.In conclusion, integrating assessment-based strategies like peer evaluations and bonuspoint rubrics can significantly enhance student engagement and academic performancein challenging subjects. These approaches offer a more comprehensive evaluation ofstudents' efforts and contributions, promoting a more inclusive and effective educationalexperience.AcknowledgementsSpecial thanks to: UW Stout Provost’s Office, OPID, Valerie Barske, Heather Pelzel,Sylvia Tiala, all my OPID peers.References[1] Tinto, Vincent. "Enhancing student success: Taking the classroom success seriously." Student Success 3.1 (2012). https://doi.org/10.5204/intjfyhe.v3i1.119[2] Hu, S., Kuh, G.D. Being (Dis)Engaged in Educationally Purposeful
S. M. Sait, “Rethinking engineering education at the age of industry 5.0,” J Ind Inf Integr, vol. 25, 2022, doi: 10.1016/j.jii.2021.100311.[4] J. Sonnenberg-Klein, E. J. Coyle, and K. Saigal, “How ‘Multidisciplinary’ is it? Measuring the Multidisciplinarity of Student Teams,” in 2023 Annual Conference & Exposition, Baltimore, MD: ASEE, Jun. 2023. doi: 10.18260/1-2--43350.[5] J. Mellor and S. McGoldrick, “A Multidisciplinary Team-Based Approach to Addressing Climate Change in Fall River,” in ASEE North East Section, Fairfield, CT, Apr. 2024. doi: 10.18260/1-2--45751.[6] M. H. Ahmadian, “Effective Practices in Multidisciplinary Teamwork,” in 2011 ASEE Annual Conference & Exposition, Vancouver, BC
the advising model for broaderimplementation, while also exploring additional strategies to deepen student engagement in bothacademic and extracurricular activities. It would also be very informative to compare the trendsin engineering to other programs. By fostering an inclusive, supportive educational environment,mechanical engineering programs can more effectively nurture, retain, and empower diversetalent—critical for driving innovation, promoting equity, and advancing progress in the field.References[1] 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: 10.1002/j.2168
Paper ID #45387A Mobile Wall Mockup (MWM) as a Learning Tool for Construction andEngineering EducationDr. Kenneth Stafford Sands II, Auburn University Kenneth S. Sands II is an Assistant Professor at Auburn University in the McWhorter School of Building Science.Andrew Hughes, Auburn University ©American Society for Engineering Education, 2025 A Mobile Wall Mockup (MWM) as a Learning Tool for Construction and Engineering EducationAbstractMockups play a critical role in construction and engineering, offering tangible ways tounderstand complex system components and improve quality
. 9590, pp. 859–877, Sep. 2007, doi: 10.1016/S0140-6736(07)61238-0.[2] S. Dattani, L. Rodés-Guirao, H. Ritchie, and M. Roser, “Mental Health,” Our World in Data, Dec. 2023, Accessed: Nov. 15, 2024. [Online]. Available: https://ourworldindata.org/mental-health[3] D. Bhugra, A. Till, and N. Sartorius, “What is mental health?,” Int J Soc Psychiatry, vol. 59, no. 1, pp. 3–4, Feb. 2013, doi: 10.1177/0020764012463315.[4] “American College Health Association-National College Health Assessment Spring 2007 Reference Group Data Report (Abridged),” Journal of American College Health, vol. 56, no. 5, pp. 469–480, Mar. 2008, doi: 10.3200/JACH.56.5.469-480.[5] J. Hefner and D. Eisenberg, “Social support and mental health
binary signed integers using sign magnitude, 1’s complement, and 2’s complement formats.2. Create a set of practice questions that will prepare a student for an exam that covers number systems including binary, octal, decimal, and hexadecimal, and BCD. It should also cover binary signed integers using sign magnitude, 1’s complement, and 2’s complement formats.3. Design an operational amplifier adder which will produce a non-inverted sum of two voltage inputs. Your design should have at least two stages, and should amplify the sum by a gain factor between 500 and 800 at DC. Use resistors on the order of 1k to 100k ohms in your design.4. Design a power converter that accepts an input between 110 volts AC and 130 volts AC, and outputs