well as the total number of tokens (operatorsand operands combined) [3]. In our study, we applied Halstead Measures within the Expertiza code toestablish a baseline for software complexity. Additionally, we examined how code refactoring impactsoverall complexity. n1 = number of distinct operators n2 = number of distinct operands N1 = total occurrences of operators N2 = total occurrences of operandsHalstead Measures estimate programming difficulty D byand programming effort E byProgramming time is an estimate of the time it takes to implement or understand fully the given software[3]. This metric is calculated bywhere f = 60 (seconds per minute) and S = 18 (moments per second). S is
. 12, 2025. [Online]. Available: https://www-scopus-com.proxy.lib.ohio-state.edu/record/display.uri?eid=2-s2.0-851061909 29&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=%28TITLE%28transgender%29 +AND+ABS%28engineering%29%29&sessionSearchId=489192ebf1b727a0671573e86d5 969db&relpos=9[4] A. L. Pawley, C. Schimpf, and L. Nelson, “Gender in Engineering Education Research: A Content Analysis of Research in JEE, 1998-2012,” J. Eng. Educ., vol. 105, no. 3, pp. 508–528, Jul. 2016, doi: 10.1002/jee.20128.[5] “Education,” US Trans Survey. Accessed: Jan. 13, 2025. [Online]. Available: https://ustranssurvey.org/report/education/[6] “Course + Curriculum | Engineering For US All
3.This board, along with three Analog Devices AD627 instrumentation amplifiers, will be housedinside one of the handles of the handheld tool. Power to the microcontroller board will beprovided via an onboard MicroUSB port connected to the host computer. The M0 will send3.3V to each of three Wheatstone bridges, comprised of four Micro Measurements 240UZA-series strain gauges, and to each of the three AD627 amps. Each Wheatstone bridge will capturedeformations in one of three modes: axial, torsional, and flexural bending. The analog signalsfrom each bridge will first route to the AD627’s to be amplified, then pass to the M0 to becomedigitized by the microprocessor, and finally sent to the host computer via the same MicroUSBcable providing
. Satisfaction scores ranged from 3.5 to 3.9 on a 5-point scale, with nosignificant difference between mentors and mentees. Overall, participants were satisfied, citingacademic and non-academic benefits, such as emotional support and improved relationships. Foxand Stevenson [11] reported similar results when examining the effectiveness of peer mentoringamong accounting and finance students, where third-year mentors assisted First-Year mentees. TheFox and Stevenson [11] program aimed to enhance academic performance and develop transferableskills through semi-formal tutorials and meetings. Results indicated that mentoring positivelyimpacted academic performance, with mentors and mentees reporting significant benefits. Bhatiaand Amati [12]’s study of a
data from the survey was analyzed in aggregate.While the survey asked about take-home labs, the great majority of respondents (n=40/43,93.0%) indicated that their department does not offer take home labs, and thus we do not havesufficient data to report these results.Table 1. Assessing accessibility of unit operations laboratories survey.Table 1a: Survey Description, Purpose, and Consent QUESTION RESPONSE OPTION(S)/TYPE Do you consent to participate in this study by ● Yes, I consent. taking this anonymous survey? ● No, I do not consent.Table 1b: General Institution and Unit Operations (UO) Lab Course InformationPlease fill out the following information about your
. Linnes, “Work in Progress: Engineering Health Equity: Perspective and Pedagogy of Interdisciplinary Teaching and Learning and Impact on Learners’ Social Identity,” in 2023 ASEE Annual Conference & Exposition Proceedings, Baltimore , Maryland: ASEE Conferences, Jun. 2023, p. 44234. doi: 10.18260/1-2--44234.[13] M. Nezafati, J. LeDoux, K. Pierre, and K. Shook, “WIP: Integration of Inclusive Mindset in a Middle-Year Biomedical Engineering Course: a Study Over Healthcare Disparities via Story-Driven Learning,” in 2021 ASEE Virtual Annual Conference Content Access Proceedings, Virtual Conference: ASEE Conferences, Jul. 2021, p. 38091. doi: 10.18260/1- 2--38091.[14] S. Barker, K. Crosson, V. Goodrich, and J. Jarrett
-grade classes at two different schools in a small urban city in the Northeast United States,which we call Pepperville. Mr. J taught the course at school A and Mr. S taught the curriculum atSchool B. Mr. J and Mr. S, both white male teachers, have students from ethnically andlinguistically diverse backgrounds. Most of the students spoke English, and many spoke,understood, or were learning another language. At the end of the curriculum, learners made avideo journalism artifact for specific audiences about climate tech in Pepperville. Both siteshosted a screening day to view the students’ final journalism artifacts, which, for the Spring 2024implementation,were journalism videos. This paper focuses on data from two of Mr. J’s classes.In our
used. Future workshould expand this approach to multiple courses and institutions, incorporating direct assessmentsof experiential learning and exploring its long-term impact on graduates’ professional competence.7. References[1] Z. Tao and G. Xu, “Digital Twin Technology in the Field of Education—Take the Management of the HTC Vive as An Example,” in Resilience and Future of Smart Learning, J. Yang, D. Liu, Kinshuk, A. Tlili, M. Chang, E. Popescu, D. Burgos, and Z. Altınay, Eds., Singapore: Springer Nature, 2022, pp. 49–59. doi: 10.1007/978-981-19-5967-7_6.[2] R. Dai and S. Brell-Çokcan, “Digital twins as education support in construction: a first development framework based on the Reference Construction Site Aachen West,” Constr
organizationalpractices that make such change possible. HSIs and computing or engineering departments cansupport students by recognizing the strengths of the local community, hiring staff and NTTs withshared cultural backgrounds, and elevating the important work of staff and NTTs in creatinginclusive educational environments and expanding students’ access to opportunities.References[1] Committee On Underrepresented Groups And The Expansion Of The Science And Engineering Workforce Pipeline (U.S, Expanding underrepresented minority participation. Washington, D.C.: National Academies Press, 2011.[2] K. P. Cobian, S. Hurtado, A. L. Romero, and J. A. Gutzwa, “Enacting inclusive science: Culturally responsive higher education practices in science
the heteronormativity of engineering: The experiences of lesbian, gay, and bisexual students,” Eng. Stud., vol. 3, no. 1, pp. 1–24, Apr. 2011, doi: 10.1080/19378629.2010.545065.[2] B. E. Hughes, “‘Managing by not managing’: How gay engineering students manage sexual orientation identity,” J. Coll. Stud. Dev., vol. 58, no. 3, pp. 385–401, Apr. 2017, doi: 10.1353/csd.2017.0029.[3] K. J. Cross, S. Farrell, and B. E. Hughes, Eds., Queering STEM culture in US higher education: Navigating experiences of exclusion in the academy. New York: Taylor & Francis Group, 2022. doi: 10.4324/9781003169253.[4] A. Paul and R. S. Lewis, “Understanding the workplace transition experiences of undergraduate queer
generative AI.References1. A. Wilhelmsen, D.H. Hanberg, I.B. Siversten, O.A. Alsos and S. Solvoll, “Generative AI in Design Education: Business As Usual, A Troublemaker or a Game Changer?,” in Proceedings of 26th Interbational Conference on Engineering and Product Design Education, Birmingham, United Kingdom, Sept 5-6, 2024.2. B. McMurtrie, “The Future is Hybrid: Colleges Being to Reimagine Learning in an AI World,” The Chronicle of Higher Education, Oct. 2024. Available: https://www.chronicle.com/article/the-future-is-hybrid?sra=true3. X. Wang, Q. Liu, H. Pang, S.C. Tan, J. Lei, M. P. Wallace, and L.Li, “What Matters in AI- supported learning: A Study of Humna-AI Interactions in Language Using Cluster Analysis and Epistemic
-2933, 2018.[2] F. Jamil, "On the electricity shortage, price and electricity theft nexus," Energy Policy, pp. 267-272, 2013.[3] I. N. Kessides, "Chaos in power: Pakistan's electricity crisis.," Energy Policy, vol. 55, pp. 271-285, 2013.[4] A. Tanveer, "Non-technical loss analysis and prevention using smart meters," Renewable and Sustainable Energy Reviews, pp. 573-589, 2017.[5] T. Bihl and A. and Zobaa, "Data-mining methods for electricity theft detection.," in Big Data Analytics in Future Power Systems, CRC Press, 2018, pp. 107-124.[6] T. Abdelhamid, "Six Sigma in lean construction systems: opportunities and challenges," Proceedings of the 11th Annual Conference for Lean Construction, pp. 22-24, 2003.[7] T. J. Bihl and S
delay in Peres and URG-based reversible circuits reveals crucial trade-offs between energy efficiency and performance [7]. Peres-based circuits demonstrate lower power consumption and quicker (g) switching times making them well-suited for low-power applications [21]. TABLE 4: COMPARISON OF POWER, DELAY AND PDP POWER DELAY(s) PDP (W)Existing Work[1] 2.082e-05 3.015n 6.27e-24[SISO]Existing Work [1] 2.082e-05 12.34p 25.6e-17[SIPO]Existing Work [2] 2.259e-05 __ __[SISO
State University (Ph.D.).Ellen Wang Althaus, University of Illinois at Urbana - Champaign Ellen Wang Althaus, PhD (she/her) is a collaborative and innovative leader forging new initiatives and building alliances to foster diversity, equity, and inclusion (DEI) in science, technology, engineering, and mathematics (STEM) disciplines. In her current role as Assistant Dean for Strategic Diversity, Equity, and Inclusion Initiatives in the Grainger College of Engineering at the University of Illinois Urbana-Champaign she • Leads the strategy enhancing the Grainger College of Engineering (GCOE)’s commitment to diversity, equity, inclusion, and access. • Develops robust structures to support faculty and staff appropriately
’ incoming major GPA toaccount for the effect of student ability.AcknowledgmentsThe authors wish to thank the students who participated in the study for their valuable feedbackand guidance as well as COL Christa Chewar for the inspiration and encouragement. The authorsalso wish to thank LTC Mike Powell who helped them with using RStudio, the method ofevaluation for the quantitative results, and the evaluation of the quantitative results. The viewsexpressed here are those of the authors and do not reflect the official policy or position of theDepartment of the Army, Department of Defense, or the U.S. Government.References [1] S. Z. Bennett-Manke and M. R. Ebling. “GraySim: An OS Scheduling Simulator”. In: J. Comput. Sci. Coll. 39.8 (May 2024), pp
–275. https://doi.org/10.1016/j.jvb.2018.05.007DeFillippi, R. J., & Arthur, M. B. (1994). The boundaryless career: A competency‐based perspective. Journal of organizational behavior, 15(4), 307–324. https://doi.org/10.1002/job.4030150403Dolan, S. L., Bejarano, A., & Tzafrir, S. (2011). Exploring the moderating effect of gender in the relationship between individuals’ aspirations and career success among engineers in Peru. International Journal of Human Resource Management, 22(15), 3146–3167. https://doi.org/10.1080/09585192.2011.560883Dries, N. (2011). The meaning of career success: Avoiding reification through a closer inspection of historical, cultural, and ideological contexts. Career
). The undecided college student: An academic and career advisingchallenge (2nd. Ed.) Springfield, IL: Charles C. Thomas.[10] Hathaway R.S., Nagda B.A., Gregerman S.R. The relationship of undergraduate researchparticipation to graduate and professional education pursuit: an empirical study.[11] Kremer J.F., Bringle R.G. The effects of an intensive research experience on the careers oftalented undergraduates. J. Res. Dev. Educ. 1990;24:1–5.[12] Lin, L., & Atkinson, R. K. (2011). Using Animations and Visual Cueing to Support Learningof Scientific Concepts and Processes. Computers & Education, 56(3), 650-658.[13] Marquez, E., Garcia Jr., S. Nurturing Brilliance in Engineering: Creating Research Venuesfor Undergraduate Underrepresented
Mean SD disagre t disagree y agree s s disagre agree e e Comfortabl 2.4 5.5 12.4 37.2 42.4 290 4.12 0.984 -1.192 1.112 e in class Part of the 2.1 4.5 18.3 35.2 40 290 4.06 0.981 -0.961 0.524 class 7Supported by 2.4 5.2 12.8 36.6 43.1 290 4.13
survey on the use of technologies for Collaborative Work Analyzed Results Strengths Relationshi Respondent responses Aspect p with BIM Interperson Improve Efficiency in It allows It allows for quick and direct al Skills s the you to collaboration with the people constant assignment assign and involved, thus avoiding rework or communi of roles and visualize interference that could affect both cation promotion roles clearly the schedule and the results. and of collective in a shared
Success Case method to determinewhich participants to interview in these case studies [23].VI. ACKNOWLEDGEMENTSThis paper builds upon the work-in-progress paper presented at the 2023 ASEE AnnualConference and Exposition, “Board 53: Engagement in Practice: Strengthening Student’s STEMIdentity Through Service,” [24, p. 53] and funded by the National Science Foundation underGrant No. DUE-1832553. Any opinions, findings, conclusions, or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. We would like to acknowledge the researchers from whom we inherited thisproject: Selvin Yovani Tobar, Bara Maisara Zalloum, Anna N. Le, Yessenia Nicacio-Rosales,Adam Moine
LearningSciences, 2005, pp. 1–16.[2] S. Doroudi, “What happened to the interdisciplinary study of learning in humans andmachines?,” J. Learn. Sci., vol. 32, no. 4–5, pp. 663–681, Oct. 2023, doi:10.1080/10508406.2023.2260159.[3] S. Ritter and S. B. Blessing, “Authoring Tools for Component-Based LearningEnvironments,” J. Learn. Sci., vol. 7, no. 1, pp. 107–132, Jan. 1998, doi:10.1207/s15327809jls0701_4.[4] J. Siemer-Matravers, “Intelligent Tutoring Systems and Learning as a Social Activity,”Educ. Technol. Publ. Inc, vol. 39, no. 5, pp. 29–32, 1999.[5] J. Wertch, “PSYCHOLOGY: L. S. Vygotsky’s ‘New’ Theory of Mind,” Am. Sch., vol. 57,no. 1, pp. 81–89, 1988.[6] X. Li, Y. Sun, and Z. Sha, “LLM4CAD: Multimodal Large Language Models for Three
obtain complete solutions to their coding assignments. References[1] A. R. Ellis and E. Slade, “A new era of learning: considerations for ChatGPT as a tool to enhance statistics and data science education,” Journal of Statistics and Data Science Education, vol. 31, no. 2, pp. 128–133, 2023.[2] S. Bubeck et al., “Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv,” arXiv preprint arXiv:2303.12712, 2023.[3] N. Cooper, A. T. Clark, N. Lecomte, H. Qiao, and A. M. Ellison, “Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution,” Methods in Ecology and Evolution, 2024.[4] R. Yilmaz and F. G. Karaoglan Yilmaz, “Augmented intelligence in programming
522E. InstrumentWe considered 7 popular instruments meant for studying attitudes towards STEM subjects. TheFennema Sherman Mathematics Attitude Scale developed in the 1970’s is a survey with 108items that looks at 9 scales of attitudes towards the learning of Mathematics [16]. TheMathematics Attitude Scale for Adults looks at the Affective, Behavioural and Cognitivedimensions of dispositions towards Mathematics [17]. The Abbreviated Mathematics AnxietyScale [18] particularly looks at mathematics anxiety related to learning and testing. There is aSingle Item Math Anxiety Scale [19] looking at self-reported general anxiety towards thesubject, while the Math Anxiety Questionnaire for Adults [20] looks at anxiety towards problemsolving.In 2006
analyzing new responses to the survey, which was revised andredistributed in the fall 2024 semester to examine the impact of sociopolitical changes such asthe overturning of race-conscious college admissions.References[1] S. Zweben, J. L. Tims, C. Tucker, and Y. Timanovsky, “ACM-NDC study 2021--2022: tenthannual study of non-doctoral-granting departments in computing,” ACM Inroads, vol. 13, no. 3,pp. 38–54, 2022.[2] S. Zweben and B. Bizot, “2022 Taulbee Survey Record Doctoral Degree Production; MoreIncreases in Undergrad Enrollment Despite Increased Degree Production,” 2023.[3] J. Forbes, A. Kennedy, M. Martonosi, and F. Pembleton, “Expanding the Pipeline: Roadmapof CISE’s Efforts to Broaden Participation in Computing Through the Years
engineering students as well as other engineering education efforts.Dr. Patricia A Ralston, University of Louisville Patricia A. S. Ralston is Professor in the Department of Engineering Fundamentals and directs J.B. Speed School’s Center for Teaching and Learning Engineering. She teaches undergraduate engineering mathematics and is currently involved in various educational research projects focused on the retention of engineering students as well as faculty development. ©American Society for Engineering Education, 2025Predicting academic behaviors of first-year engineering students by modeling non-cognitive factors and their interactionsIntroductionA common reason for many first-year
C.M. Firetto, "Development of an intervention to improve students’ conceptual understanding of thermodynamics," in 2013 ASEE Annual Conference & Exposition, Atlanta, GA, USA, June 23-26, 2013.[18] S. Kesidou and R. Duit, "Students’ conceptions of the second law of thermodynamics – An interpretive study," Journal of Research in Science Teaching, vol. 30 (1), pp. 85-106, Jan. 2006.[19] W. Dempster, C.K. Lee, and J.T. Boyle, "Teaching thermodynamics and fluid mechanics using interactive learning methods in large classes," in 2002 ASEE Annual Conference & Exposition, Montreal, Quebec, CA, June 16-19, 2002.[20] K.J. Nasr and B. Ramadan, "Implementation of problem-based learning into engineering
opted for a tool that is called Google NotebookLM. Copilot Document Google Perplexity ChatGPT Azure AI Guru Zendesk LandbotFeature Studio 360 NotebookLMUser Learning Curve L L-M H M L-M L M L L(Low, Medium, High)Dependency of Operation S S I I S S/I I S
literature suggests anopportunity to improve persistence through calculus by improving the connection betweenmathematics and their real-world applications.1.2. Revisiting Bloom :S- Taxonomyfor Modern LearningEffective teaching and learning are guided through well-strnctured objectives. Leamingobjectives outline the composition of lessons and practice problems by establishing the contentof focus, specific student activities, and fo1ms of assessment [8]. Similarly, they have beendiscussed in best practices texts for how they contribute to learning [9], [10].Bloom's Taxonomy has frequently been referenced in developing learning objectives [5], [11]. Arevision to Bloom's Taxonomy includes two dimensions: Knowledge Typology and CognitiveProcess [12
orqualities do students identify in their peer’s work? We demonstrate that the framework can be usedto effectively capture students cognitive and affective responses and propose how student’s valueassignments of their peers’ work align with their own motivation(s) for success. By examiningstudent reflections on Engineering Studio experiences, we aim to identify participation drivers andstrategies to enhance engagement and learning outcomes in these collaborative spaces.Theoretical and Conceptual Frameworks. Our framework draws on the Framework of StudentAffect in Field Biology, adapted and applied to the unique context of our BME studios [10]. Builton the Model of the Affective Domain in the Geosciences, it explores how motivation, emotion,and
a Localized Engineering in Displacement (LED) ProgramAbstractThis work-in-progress paper explores how queer youth experiencing housing insecurity navigateidentity and agency through participation in an alternative engineering education program calledLocalized Engineering in Displacement (LED). This study stems from a three-year Design-BasedResearch (DBR) initiative that developed the LED curriculum, integrating community-drivenproblem-solving, digital tools, and microelectronics to empower LGBTQIA+ youth experiencinghousing insecurity. Drawing on Holland et al.'s [1] theory of figured worlds, we investigate howthe LED program creates a space where queer identities are not only welcomed but also informengineering engagement. Using semi