+( ) đ¸2 đA confidence interval (đ§) of 95% is chosen, yielding Z-values of 1.96, with a margin of error (đ¸)of 4%-8%, which corresponds to the 95% confidence interval [13]. The standard deviation (đ) 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 Ď 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 Cronbachâs 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 teachersâeffectiveness in one form or another since the 1920âs. 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
Proteins and DNA are Poorly Correlatedâ, Mol Biol Evol. 2023 Apr 4;40 (4).with the student population. There are about 5 physics majors, [2] L. Teekas, S. Sharma, and N. Vijay, âTerminal regions of a protein are a50 engineering majors, and 500 ET majors in which 20 of them hotspot for low complexity regions and selectionâ, Open Biol. 2024are considering transition to engineering. None of the Jun;14(6):230439.participants in this report is majoring in physics. In fact, all the [3] B. Leung, unpublished data, Year of 2025 Great Neck
potential, respectively,from ATHENA. The current paper describes the implementation of the DACE process for the Summer2024 project, some findings, and the lesson plans developed by Zagozda to share more broadly to theASEE Community. MethodsAs described in Thomason et al.2, the DACE process provides an approach that middle/high schoolteachers can follow and translate to their classrooms. As a brief summary, DACE consists of thefollowing steps: 1. Calibration of the computer model(s) for the application of interest. 2. Design experiments to organize a set of computer model input parameter settings. 3. Execution of the computer model(s) to generate performance metric outputs. 4
-efficacy, statisticalanalyses were conducted on pre- and post-intervention scores. The focus of the analysis was todetermine whether significant changes occurred in self-efficacy levels after the intervention andwhether these changes differed by gender. Paired t-tests were employed to evaluate within-groupdifferences in self-efficacy over time, while independent t-tests were used to comparegender-based differences in the interventionâs effect. The following sections detail the results ofthese analyses. Gender Factor Pre-intervention Post-interventio t-statistic p-value Mean (SD) n Mean (SD) s Female CPSES 3.71 (1.41) 5.48 (1.15) 4.95 <0.05
users. Proceedings of the 2025 ASEE North Central Section Conference Copyright Š 2025, American Society for Engineering EducationAcknowledgmentThis research was funded by the Civil-Military Innovation Institute (CMI2) through Grant #224117.We sincerely thank CMI2 for their generous support and commitment to advancing this project.References [1] R. Orr, R. Pope, T. J. A. Lopes, D. Leyk, S. Blacker, B. S. Bustillo-Aguirre, and J. Knapik, âSoldier load carriage, injuries, rehabilitation and physical conditioning: An international approach,â International Journal of Environmental Research and Public Health, vol. 18, 2021. [2] J. Ramsay, C. L. Hancock, M. P. OâDonovan, and T. Brown, âSoldier-relevant body borne
paths.References[1] G. Heydt and V. Vittal, "Feeding our profession [power engineering education].," IEEE Power and Energy Magazine, vol. 1, no. 1, pp. 38-45, 2003.[2] I. OpriĹ, D. GogoaČe Nistoran, S. CostinaĹ and C. Ionescu, "Rethinking power engineering education for Generation Z.," Computer Applications in Engineering Education, vol. 29, no. 1, pp. 287-305, 2021.[3] H. Chai, J. Ravishankar, S. Krishnan and M. Priestley, "Work-in-Progress: A Holistic Approach to Bridging the Gap between Power Engineering Education and Electric Power Industry," IEEE Global Engineering Education Conference (EDUCON), pp. 2044-2048, 2022.[4] N. Zeybek and E. SaygÄą, "Gamification in education: Why, where, when, and how?âA systematic review.," Games and
-policy-and-procedure-manual-appm-2024-2025/[6] ABET, âBig 10+ Universities Deans of Engineering Letter of Support, Diversity, Equity &Inclusion, Mar. 31, 2021.,â 2021. Accessed: Sep. 02, 2024. [Online]. Available:https://www.abet.org/about-abet/diversity-equity-and-inclusion/[7] M. Borrego, J. Froyd, C. Henderson, S. Cutler, and M. Prince, âInfluence of EngineeringInstructorsâ Teaching and Learning Beliefs on Pedagogies in Engineering Science Courses,âInternational Journal of Engineering Education, vol. 29, pp. 1456â1471, Jan. 2013.[8] E. A. Canning, K. Muenks, D. J. Green, and M. C. Murphy, âSTEM faculty who believeability is fixed have larger racial achievement gaps and inspire less student motivation in theirclasses,â Sci Adv, vol. 5, no
, Maryland: ASEE Conferences, Jun. 2023.[2] L. Espinosa, âPipelines and pathways: Women of color in undergraduate STEM majors and the college experiences that contribute to persistence.â Accessed: Aug. 26, 2024. [Online]. Available: https://psycnet.apa.org/record/2011-13330-004[3] L. Foltz, S. Gannon, and S. Kirschmann, âFactors That Contribute to the Persistence of Minority Students in STEM Fields,â Plan. High. Educ. J., vol. 42, pp. 1â13, Sep. 2014.[4] S. Deitz and R. Henke, âHigher Education in Science and Engineering,â NSF - National Science Foundation. [Online]. Available: https://ncses.nsf.gov/pubs/nsb202332/characteristics-of-s-e-degree-recipients#s-e-degrees-by -race-and-ethnicity[5] E. Lichtenberger and C. George
societal implications if this AI-driven [2] B. Arslan, B. Lehman, C. Tenison, J. R. Sparks, A. A. LĂłpez, L.system is deployed? Kasneci et al. [6] conclude commentary Gu, and D. Zapata-Rivera, âOpportunities and challenges of usingwith a call for âresponsible and ethicalâ use of AI in generative AI to personalize educational assessmentâ, Front. Artif.education, advocating strategies like continuous human Intell., vol. 7, art. 1460651, 2024.oversight and critical evaluation. [3] N. J. Francis, S. Jones, and D. P. Smith, âGenerative AI in higher education: Balancing innovation and
, the research team has also gathered quantitative data related to how the studentsengage with campus resources and personnel, as well as data on the character and composition ofthe studentsâ social support networks.Program descriptionThe SEED program was initiated in 2021 with support of the NSF S-STEM program which hasthe goal of recruiting and retaining financially-needy, academically-talented students to STEMcareers. The SEED program is open to students majoring in computer science or an engineeringdiscipline and the financial need requirement is satisfied by eligibility for the federal Pell grant.While not a requirement, students from backgrounds historically underrepresented in STEM areactively recruited to the program. Cohorts of
-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 NSFâs views.References[1] Z. Sun, X. Zhang, T. Wang, and Z. Wang, âEdge computing in Internet of Things: A novelsensing-data reconstruction algorithm under intelligent-migration
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
,assemblies, and drawings for the tracked vehicle.After the team uploaded their product data into Teamcenter, they began working with it in NX tocontinue designing the digital twin. Another significant feature of Teamcenter that the team utilizedin their design process was workflows. A workflow is a collaborative, step-by-step process thatmodels many basic business and design functions. Workflows can route an item for design reviewand approval, oversee the change management process, and track manufacturing and designprocesses. However, for this case study, the team employed a basic workflow that includedassigning a task to either a group or individual user, completing that task by the assigned user(s),and finalizing the workflow. A block diagram of
a description of how the budget will be used. a. For continuing project proposals: How does your project build on last yearâs project? (Recommended: use your previous projectâs 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
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
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), 449â460. 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
,she drafted a hiring handbook to guide search committees in engineering through the process.The content in the next section is content from this hiring handbook Pierrakos prepared andcontinuously improved from one year to the next.V. HIRING PRACTICES AT WFU ENGINEERINGIn this section, the hiring process Pierrakos instituted and implemented in hiring a diverse WakeForest Engineering team is showcased. Content herein is adopted from the WFU EngineeringHiring Handbook that Pierrakos prepared as Founding Engineering Chair to guide hiring. TheWFU Engineering Hiring Handbook was shared with all search committee members. Every steprequired intentionality around minimizing bias which is inherent in hiring processes.Step 1. Getting the Job(s
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
]. whose responsibility was to mentor and develop the junior ⢠An ability to apply knowledge, techniques, skills and engineerâs talent through on-the-job training. The first few modern tools of mathematics, science, engineering, decades of the 1900âs saw engineering students begin working and technology to solve well-defined engineering directly with mechanical machinery, test equipment and problems appropriate to discipline. undertaking design drafting roles. Dedicated lab space with specialized equipment was slowly being introduced in ⢠An ability to design solutions for well-defined
of belonging, motivation, and academic performance. The following is anexemplar statement from Participant 2âs final reflective writing: The [program] has encouraged me to adopt a more empathetic and student-centered approach. Recognizing the psychological and emotional dimensions of student learning has led me to consider how academic policies and teaching practices can sometimes inadvertently contribute to student stress and disengagement. This shift towards a more empathetic pedagogy aims to create a learning environment that fosters student well- being and academic engagement.Participant 2 also described an actionable plan for his intended practices for providing feedbackto future students: I am
: Predictors and outcomes of heterogeneous science identitytrajectories in college. Developmental psychology, 54(10), 1977.[5] Eddy, S. L., & Brownell, S. E. (2016). Beneath the numbers: A review of gender disparitiesin undergraduate education across science, technology, engineering, and math disciplines.Physics Education Research Conference Proceedings, 13(3), 79â89.https://doi.org/10.1103/PhysRevPhysEducRes.13.020108.[6] Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion ofcommunity cultural wealth. Race, Ethnicity and Education, 8(1), 69â91.http://dx.doi.org/10.1177/07399863910131002.[7] Rincon, B. E., & George-Jackson, C. E. (2016). STEM intervention programs: fundingpractices and challenges. Studies in
ultrasonic waves," IEEE Transactions onthrough machine learning algorithms capable of predicting Ultrasonics, Ferroelectrics, and Frequency Control, vol. 42, no. 4, pp.irrigation needs based on historical data, integrating weather 619-629, 1995.forecasting for more adaptive water management, and [10] A. M. Kamal, S. H. Hemel and M. U. Ahmad, "Comparison of Linearincorporating solar power to improve sustainability and off-grid Displacement Measurements Between A Mems Accelerometer and Hc-functionality in remote agricultural settings. Sr04 Low-Cost Ultrasonic Sensor," in 2019 1st International Conference
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, Bernoulliâs 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. SzczypiĹski-Sala and A. Kot, Frictional Problems
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, âArtificial Intelligence in the 21st Century,â pp. 1â 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, âDeveloping Middle School Studentsâ AI Literacy,â in Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, Virtual Event USA: ACM, Mar. 2021, pp. 191â197. doi: 10.1145/3408877.3432513.[3] T. Kurz, S. Jayasuriya, K. Swisher, J. Mativo, R. Pidaparti, and D. T. Robinson, âInvestigating 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 Provostâs 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, âRethinking engineering education at the age of industry 5.0,â J Ind Inf Integr, vol. 25, 2022, doi: 10.1016/j.jii.2021.100311.[4] J. Sonnenberg-Klein, E. J. Coyle, and K. Saigal, âHow âMultidisciplinaryâ is it? Measuring the Multidisciplinarity of Student Teams,â in 2023 Annual Conference & Exposition, Baltimore, MD: ASEE, Jun. 2023. doi: 10.18260/1-2--43350.[5] J. Mellor and S. McGoldrick, âA Multidisciplinary Team-Based Approach to Addressing Climate Change in Fall River,â in ASEE North East Section, Fairfield, CT, Apr. 2024. doi: 10.18260/1-2--45751.[6] M. H. Ahmadian, âEffective Practices in Multidisciplinary Teamwork,â in 2011 ASEE Annual Conference & Exposition, Vancouver, BC