(0.36 [degree]) signals (A, B) $50 45mm, 180g position detection Widely used Incremental / 3 1000 CPR Approx. φ40 x forE6B2-CWZ6C Omron signals (A, B, (0.36 [degree]) $150 39mm, 100g educational Z) robots
. 9References[1] Accreditation Board for Engineering and Technology, "Criteria for accrediting engineeringprograms, 2024-2025," ABET, 2024. [Online]. Available:https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2024-2025/[2] D. A. Martin, E. Conlon, and B. Bowe, "A multi-level review of engineering ethicseducation: Towards a socio-technical orientation of engineering education for ethics," Sci. Eng.Ethics, vol. 27, no. 5, pp. 60, 2021. https://doi.org/10.1007/s11948-021-00333-6[3] National Academy of Engineering, "Grand challenges for engineering," Washington, DC:National Academies Press, 2008.[4] M. Berge, E. Silfver, and A. Danielsson, "In search of the new engineer: Gender, age, andsocial class in
expectations of the design review (i.e., what makes it useful vsunhelpful) that was derived from Scott Hamilton’s study [2]. Each design review is a small,graded assignment (10 points), so that students put in effort. Not all learning activities need tobe graded, but students are motivated to engage in it if it is incorporated into their final grade [5].A second page was added to the design review form to incorporate reflection practices asdescribed in Part I (Page 2, Appendix B) [8]. This has been adapted from a peer reviewworkshop evaluation form used for team-based project reporting that has been shown to work inan industrial and systems engineering course [3]. The second page ensures students have readtheir feedback and internalized it, thereby
scales [13].Instrument Design MethodologyRGS is an approach to scale development for progressive constructs. RSG is guided by Raschmeasurement principles [23] and Guttman facet theory design [24], [25], [26], which togetherguide the generation of scenarios to measure progressive phenomena. Rasch measurementprinciples serve as an a priori foundation to identify constructs and guide scale development.Rasch principles dictate that scale items must (a) measure a single construct, (b) measure a rangeof levels of the construct, (c) spread uniformly across the construct continuum, (d) measure theincreasing progress of the construct, (e) have the same relationship to the construct, (f) that oneresponse to an item is not dependent on the response of
). LGBTQ Inequality in Engineering Education. Journal of Engineering Education, 107(4), 583–610. https://doi.org/10.1002/jee.20239[2] Hughes, B. E. (2018). Coming out in STEM: Factors affecting retention of sexual minority STEM students. Science Advances, 4(3), eaao6373. https://doi.org/10.1126/sciadv.aao6373[3] Hughes, B. E. (2017). “Managing by Not Managing”: How Gay Engineering Students Manage Sexual Orientation Identity. Journal of College Student Development, 58(3), 385– 401. https://doi.org/10.1353/csd.2017.0029[4] Haverkamp, A., Butler, A., Pelzl, N. S., Bothwell, M. K., Montfort, D., & Driskill, Q. (2019). Exploring Transgender and Gender Nonconforming Engineering Undergraduate Experiences through
. African Journal of Research in Mathematics, Science and Technology Education, 2016. 20(2): p. 154-164.6. L.W. Anderson and D. R. Krathwohl. A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Completed. 2001, New York: Longman.7. R. A. Sperling, B. C. Howard, L. A. Miller, and C. Murphy. “Measures of Children’s Knowledge and Regulation of Cognition,” Contemporary Educational Psychology, vol. 27, no. 1, pp. 51–79, Jan. 2002, doi: 10.1006/ceps.2001.1091.8. J. R. Sablan. “Can You Really Measure That? Combining Critical Race Theory and Quantitative Methods,” American Educational Research Journal, vol. 56, no. 1, pp. 178–203, Feb. 2019, doi: 10.3102/0002831218798325.9. E
code, term of first enrollment, cumulative credit hours, intention to transfer, andmajor.Data Analysis Guided by the research questions, this study utilized three primary regression models toexamine (1) sense of belonging, (2) STEM pathways, and (3) gender discrepancies. To predicteach outcome, a multiple regression model was optimized using all participants in the dataset,ensuring that the data met the necessary assumptions for regression analysis, including linearity,independence, and normality. A central focus was understanding how the experiences ofmarginalized students differed from the broader STEM student population at Blue Lake College.Accordingly, six additional subgroups were analyzed: (a) Latinx students, (b) students
Paper ID #46639First-Year Student Interest in Hands-On Final Project with an AutonomousRobotDr. James E. Lewis, University of Louisville James E. Lewis, Ph.D. is a Professor in the Department of Engineering Fundamentals in the J. B. Speed School of Engineering at the University of Louisville. His primary research focus is Engineering Education and First-Year Programs. He also has interests in cryptography, and parallel and distributed computer systems.Dr. Nicholas Hawkins, University of Louisville Nick Hawkins is an Assistant Professor in the Engineering Fundamentals Department at the University of Louisville. He
) motivation to improve their performance, and (3) better decision-making when it comes tochoices on what to focus on and when to seek help. However, not all students may have the skillsto correctly interpret their data. Misinterpretation could lead to misguided actions. Studentsmight focus on the wrong areas of improvement or experience unnecessary stress. For ethical useof the collected data, students need to be provided with the resources to make sense of it in aconstructive way. Also, constant monitoring of feedback can psychologically impact students bycreating more anxiety and potentially unhealthy competition among students.References[1] Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts
. [3] C. Ahnert, D. Cuervo, N. Garcia-Herranz, J. M. Aragones, O. Cabellos, E. Gallego, “E. Minguez, A. Lorente, D. Piedra, L. Rebollo, and J. Blanco, “Education and training of future nuclear engineers through the use of an interactive plant simulator,” in International Journal of Engineering Education, vol. 27, no. 4, pp. 722-732, 2011. [4] U.S. Nuclear Regulatory Commission, “Computer codes,” NRC.gov, 2024. [Online]. Available: https://www.nrc.gov/aboutnrc/regulatory/research/safetycodes.html. [5] P. K. Romano, N. E. Horelik, B. R. Herman, A. G. Nelson, B. Forget, and K. Smith, “OpenMC: a state-of-the-art Monte Carlo code for research and development,” in Annals of Nuclear Energy
distribution ofone owner and engineering individual respectively, other role distributions are three (3) mid-level management, two (2) upper-level management, and one (1) owner. This implies that almost90% of the participants are in a management position or higher roles that are considered as thosewho make major strategic decisions and oversee directions in the organization. The surveyquestion that guided this response is “What is your current role within your organization”. In Fig.1.b., the survey question was “What is your company’s specialty”. As seen below the responserepresents that multiple specialties exist within a single organization, and the data result areMachinery & Equipment (3), Energy and Aerospace (2) respectively, and one (1) for
, Pew Research Center. https://Www.Pewresearch.Org/Social- Trends/2018/01/09/Diversity-in-the-Stem-Workforce-Varies-Widely-across-Jobs/.[3] Schuster, P., Cooper, L., Elghandour, E., Rossman, E., Harding, S., & Self, B. (2020, June). Senior capstone team formation based on project interest: Team selection by students compared with team selection by instructors. 2020 ASEE Virtual Annual Conference Content Access Proceedings. https://doi.org/10.18260/1-2--35187[4] Aller, B. M., Lyth, D. M., & Mallak, L. A. (2008). Capstone project team formation: Mingling increases performance and motivation. Decision Sciences Journal of Innovative Education, 6(2), 503–507. https://doi.org/10.1111/j.1540
) (b) (c) Figure 1The effect of series resistance and temperature on the performance of solar cell. (a) MATLAB Simulink setup (b) The I-V curve of the solar cell as series resistance changes from 0 to 0.08 (c) The I-V curve as temperature changes from 0oC to 75oCThrough this exercise, students gained a deeper understanding of the concepts covered inlectures. By analyzing Figure 1b, they reinforced key ideas, including: • Solar cells with the same Voc and Isc may not produce the same Pmp, highlighting the 𝑃 importance of the fill factor, F, where 𝐹 = 𝑉 𝑚𝑝 . 𝐼 𝑜𝑐 𝑠𝑐 • Parasitic
before stepping foot in the laboratory.Four new RDC learning outcomes (LO-C to LO-F) were established to support this goal thatmapped directly to our department-level learning outcomes for the undergraduate curriculum.The revised course learning outcomes prepared students to: LO-A. Describe various techniques used to synthesize nanomaterials and justify their use LO-B. Explain how to characterize important properties of nanomaterials LO-C. Summarize the important objectives, methods, findings, and conclusions of a scientific report LO-D. Perform a literature search on a nanomaterials topic that interests you LO-E. Identify an important research question or gap in scientific knowledge LO-F. Design a logical set of experiments
theyencountered, and subsequently analyzing and interpreting the collected data to derive meaningfulinsights.Survey questionnaireThe survey questions were designed to investigate how specific aspects of online learning, such asengagement and feedback, influence learning outcomes. By incorporating a mix of rating-scaleand open-ended questions, the study gathered both quantitative data and in-depth qualitativeinsights, providing a holistic view of student experiences. The questions were organized into fourcategories: (a) Engagement with online courses assessed students’ overall perceptions of online learning and their prior experience with such courses (b) Factors influencing engagement focused on students’ interactions with course materials and
: 10.1561/2000000111.[10] E. Yong, A Popular Algorithm Is No Better at Predicting Crimes Than Random People, en, Section: Technology, Jan. 2018. [Online]. Available: https://www.theatlantic. com / technology / archive / 2018 / 01 / equivant - compas - algorithm / 550646/.[11] B. Templeton, An 8-Car Pileup Started By A Tesla In Autopilot Opens Up Many Com- plex Issues, Jan. 2023. [Online]. Available: https : / / www . forbes . com / sites / bradtempleton/2023/01/11/an-8-car-pileup-started-by-a-tesla- in-autopilot-opens-up-many-complex-issues/?sh=2fae2f0212be.[12] O. Gencoglu, M. van Gils, E. Guldogan, et al., “HARK side of deep learning – From grad student descent to automated machine learning,” en, Apr. 2019. [Online
variance.The largest increment is associated with Supervision Experience (R2 = .219; F(1,148) =47.889, p < .01). Adding Research Group Experience makes a smaller, yet significantcontribution (R2 = .036; F(1,147) = 8.289, p < .01). In Model 3, both Supervision Experience(b = .418, t = 5.943, p < .01) and Research Group Experience (b = .210, t = 2.879, p < .01) aresignificantly associated with the perceptions of belonging to the professional community.Discussions and limitationsOur preliminary results show that it makes sense to consider simultaneously the supervision andresearch group experiences when evaluating graduate research training. As expected, the two arerelated, yet play a varying role depending on the outcomes being examined
. The instructor volunteered to teach the mechanical/civilengineering section to begin to explore offering a combined course with all three disciplines.TextbooksFigure 2 shows the covers of the textbooks used for the two disciplines. The content is verysimilar, but the organizations of the books are different. Table 1 compares the chapter titles forthe two textbooks. Most topics are covered in both books, but the difference is how the courseswere structured for the different disciplines. (a) (b) Figure 2: (a) Engineering Fluid Mechanics 12th Edition adopted for the mechanical and civil engineering course (b) Fluid Mechanics for Chemical Engineers adopted for
used a Meta Quest 2before. Table 5: Outline of the general course structure for Aeroverse. Theme Module Nickname Description VR Group Aircraft Module A: Explore a Jet Plane Custom module Group 1 Week Module B: Fly a Jet Plane Microsoft Flight Simulator Group 1 Spacecraft Module C: Explore Mars with a Custom module Group 2 Week Remote-Controlled Vehicle Module D: Explore Mars with an Custom module Group 2 Autonomous Vehicle Astronaut Module E: Human-Machine Mission: ISS scavenger hunt
: 10.18260/p.26947.[12] S. Zekavat, “A Preliminary Study On The Shortcomings Of The Interdisciplinary Course Electrical Engineering For Nonmajors,” presented at the 2004 Annual Conference, Jun. 2004, p. 9.86.1- 9.86.17.[13] R. M. Marra, K. A. Rodgers, D. Shen, and B. Bogue, “Women Engineering Students and Self- Efficacy: A Multi-Year, Multi-Institution Study of Women Engineering Student Self-Efficacy,” J. Eng. Educ., vol. 98, no. 1, pp. 27–38, Jan. 2009, doi: 10.1002/j.2168-9830.2009.tb01003.x.[14] G. Trujillo and K. D. Tanner, “Considering the Role of Affect in Learning: Monitoring Students’ Self-Efficacy, Sense of Belonging, and Science Identity,” CBE—Life Sci. Educ., vol. 13, no. 1, pp. 6–15, Mar. 2014
used to power thesensors and communicate signals through the system. The middle school units containmicro:bits and sensors for humidity and temperature in conjunction with heat lamps, fans, andhumidifiers contained within an integrated terrarium environment. All of these components arerouted through Dataflow, a custom data pipeline programming language that allows students toeasily connect with hardware components and introduce filtering and control logic as shown inFigure 1.Figure 1. A screen capture of CLUE software showing (a) the inline curriculum (left) with aphotograph of the hardware kit, and (b) WYSIWIS collaborative student view (right) wheregroups can compare and reuse solutions as they evolve. Note digital twins in the upper
of Chicago Press, 2012.[3] M. Besterfield-Sacre, C. J. Atman, and L. J. Shuman, "Characteristics of freshman engineering students: Models for determining student attrition in engineering," J. Eng. Educ., vol. 90, no. 2, pp. 139–150, 2001.[4] B. N. Geisinger and D. R. Raman, "Why they leave: Understanding student attrition from engineering majors," Int. J. Eng. Educ., vol. 29, no. 4, pp. 914–925, 2013.[5] 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.[6] R. M. Felder and L. K. Silverman, "Learning and teaching styles in engineering education," Eng. Educ., vol. 78
Learning Curriculum of the Aircraft Maintenance Training Organization 147 for Avionic Study Program as a Basis in Meeting the Need of the Aviation Industry," Journal of Engineering Education Transformations, vol. 34, 2020.[6] T. W. Jiang, C.-t. Lu, H. Fu, N. Palmer, and J. Peng, "An Inductive Approach to Identify Aviation Maintenance Human Errors and Risk Controls," The Collegiate Aviation Review International, vol. 40, no. 1, 2022.[7] H. Padil, M. Said, and A. Azizan, "The contributions of human factors on human error in Malaysia aviation maintenance industries," in IOP Conference Series: Materials Science and Engineering, 2018, vol. 370, no. 1: IOP Publishing, p. 012035.[8] B. Denizhan and A
, Z. Andrijasevic, and B. Pejovic, “STEM Education and Growth in Europe,” J. Knowl. Econ., vol. 13, no. 3, pp. 2348–2371, 2022, doi: 10.1007/s13132-021-00817-7.[2] S. Olson and D. G. Riordan, “Engage to excel: producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Report to the president.,” Exec. Off. Pres., 2012.[3] M. White, E. Legg, B. Foroughi, and J. Rose, “Constructing past, present, and future communities: Exploring the experiences of community among last‐dollar scholarship students,” J. Community Psychol., vol. 47, no. 4, pp. 805–818, 2019.[4] “Building a Sense of Community.” https://serc.carleton.edu/lsamp/community.html (accessed Dec
. 549, no. 7671, pp. 257–260, Sep. 2017, doi: 10.1038/nature23878.[2] A. Schulz, C. Greiner, B. Seleb, C. Shriver, D. L. Hu, and R. Moore, “Towards the UN’s Sustainable Development Goals (SDGs): Conservation Technology for Design Teaching & Learning,” in American Society of Engineering Education, Mar. 2022.[3] E. W. Davis, J. M. Lakin, V. A. Davis, and P. K. Raju, “Nanotechnology Solutions to Engineering Grand Challenges,” presented at the 2016 ASEE Annual Conference & Exposition, Jun. 2016. Accessed: Jan. 15, 2025. [Online]. Available: https://peer.asee.org/nanotechnology-solutions-to-engineering-grand-challenges[4] S. Strachan and A. Greig, “PEDAGOGICAL APPROACHES TO THE GREEN SKILLS GAP
(b) Hollow Cylinder Fig. 1, Tolerance Zones Produced by Circularity and Cylindricity2. Measurement Fixture for Circularity and CylindricityCircularity or roundness is a cross-sectional check resulting in a planar (2D) tolerance zone [2]. Thetolerance zone is the space between two concentric circles (the annulus shown in Fig. 1a) and if thesurface of the feature’s cross-section is contained within this space, the feature will pass inspection.If the surface exceeds the space, the feature will be rejected. (a) Parts and tools needed to check circularity (b) 3D printed part showing the rotation and and cylindricity cross-section guides. (c
program’s consistent effectiveness in enhancing teacherknowledge of semiconductor concepts. Together, the data from both years underscores theprogram’s impact in equipping teachers with the skills necessary to integrate semiconductoreducation into their classrooms. (a) (b) Figure 1: Mean percent change from pre to post assessment. (a): Year One, (b): Year TwoChallenges and RecommendationsBased on data collected throughout the implementation of the RET program, key areas forimprovement have been identified to enhance the Chip-RET program’s impact. Adding morestructure, such as clear goals, regular mentor meetings, and scaffolding for challenging content,could better support
, the assignment was segmented and explicitly required completion before the semester’send to allow faculty to consult with campus colleagues. See Appendix B for details of the pre-workshop assignment deployed for the third offering. These changes led to improvedengagement during the in-person workshop and a stronger focus on entrepreneurial-mindedefforts.Lastly, the coaching model evolved substantially. In the first offering, a clear separation existedbetween facilitators and coaches, with only one of the three coaches attending the in-personsessions. For the second offering, coaches were more integrated into the in-person workshop,attending reporting sessions either in person or via Zoom. By the third offering, the teamexpanded to include
Camp, “Freshman Engineering Student Perceptions of Engineering Disciplines,” in Proceedings of the ASEE Southeast Section Annual Conference, 2010, pp. 1–11.[3] S. Zahorian, M. Elmore, and K. J. Temkin, “Factors that Influence Engineering Freshman to Choose Their Engineering Major,” in 120th ASEE Annual Conference and Exposition, Atlanta, GA, 2013, pp. 1–13.[4] M. A. Jacobs and Z. Shahbazi, “Best practices in Encouraging STEM Majors Among 6- 12 Students,” in 2019 ASEE Annual Conference & Exposition, Tampa, Florida, 2019.[5] M. B. Sarder, “Designing STEM Curriculum for K12 Students,” in 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia, 2013.[6] G. Hein, K. Torrey, J. Hertel, D
experiences workingon a design team. Each participant provided reflective statements regarding their experiences withteamwork, leadership, and project management. Reflections were structured to include insights onchallenges faced, leadership styles, group dynamics, and personal contributions. Though not specificallyasked for, this data was analyzed for the level of engagement pertinent to team dynamic concepts. Theanalysis utilized a systematic qualitative approach, incorporating iterative validation, refinement, andcategorization strategies informed by best practices in qualitative research and emerging AI-basedtechniques[20], [21], [22], [23].Data Handling and AnalysisThis report compares Cohorts A, B, C, and F Datasets, summarized in Table 1. The