analyses. These approaches wouldprovide a more comprehensive understanding of the impact of SBL on engineering educationand expand its applicability in different educational contexts.AcknowledgmentThe authors gratefully acknowledge the leadership and financial support of the School ofEngineering at the Universidad Andres Bello, Chile. We also thank the Educational Researchand Academic Development Unit (UNIDA) for its mentorship and guidance in developingresearch skills for higher education faculty.References[1] L. Najev Čačija, M. Lovrinčević, and S. Pivčević, "Exploring the service-learning program in the transition
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 engineer’s talent through on-the-job training. The first few modern tools of mathematics, science, engineering, decades of the 1900’s 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
thank Alan Dominguez and Juan Olivier,their excellent teaching assistants, for providing invaluable support with student instruction andfeedback during this first semester of their engineering graphics course. Bowen would also liketo thank Prof. Sharona Krinsky for pedagogical and practical support on the implementation ofmastery-based grading.Teaching assistant support for Bowen, Menezes, and Panwar was provided by the NationalScience Foundation under Grant No. 2122941. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation.References[1] D. H. Baxter, “Engineering Graphics And Computer Aided Design: A Foundation
sizes.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.DUE-IUSE-2116226. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References [1] Autodesk Inc., “The Essentials of IoT for Modern Engineers,” https://www.autodesk.com/industry/manufacturing/resources/mechanical-engineer/iot- internet-of-things-essentials-for-engineers, 2016. [2] W. Mahmoud and N. Zhang, “Disrputive technologies: An educational prespective.” 2018 ASEE Mid-Atlantic Section Spring Conference, Washington DC. [3] A. Huderson, E. Peiffer, S. Shamsi, F. Plazaand, and E. Collins
English Writing Assistant: Students’ Alternative for English Writing," Journal of English Language Literature and Teaching, vol. 5, no. 1, pp. 65-78, 2021.[4] P. I. Fusch, L. R. Ness, J. M. Booker, and G. E. Fusch, "The Ethical Implications of Plagiarism and Ghostwriting in an Open Society," Journal of Social Change, vol. 9, no. 1, pp. 55-63, 2017, doi: DOI: 10.5590/JOSC.2017.09.1.04[5] Y. Dai, "Why Students use or not use generative AI: Student conceptions, concerns, and implications for engineering education," Digital Engineering, November 5, 2024 2024, doi: https://doi.org/10.1016/j.dte.2024.100019.[6] C. Shaw, L. Yuan, D. Brennan, S. Martin, N. Janson, and G. Bryant, "GenAI in Higher
employability: stakeholder perceptions on the connection,” High. Educ., vol. 59, no. 5, pp. 599–613, May 2010, doi: 10.1007/s10734-009-9268-z.[3] H. Li, A. Öchsner, and W. Hall, “Application of experiential learning to improve student engagement and experience in a mechanical engineering course,” Eur. J. Eng. Educ., vol. 44, no. 3, pp. 283–293, May 2019, doi: 10.1080/03043797.2017.1402864.[4] S. F. Pamungkas, I. Widiastuti, and Suharno, “Kolb’s experiential learning for vocational education in mechanical engineering: A review,” AIP Conf. Proc., vol. 2114, no. 1, p. 030023, Jun. 2019, doi: 10.1063/1.5112427.[5] S. F. Pamungkas, I. Widiastuti, and Suharno, “Vocational Student’s Attitude and Response Towards Experiential
University of New York Amber Simpson is an associate professor of Mathematics Education in the Teaching, Learning and Educational Leadership Department at Binghamton University. Her research interests include (1) examining individual’s identity(ies) in one or more STEM disciplines, (2) understanding the role of making and tinkering in formal and informal learning environments, and (3) investigating family engagement in and interactions around STEM-related activities. Before joining BU, she completed a post-doctoral fellowship at Indiana University-Bloomington. She earned a Ph.D. in mathematics education from Clemson UniversityNicole Scarlett Fenty, Binghamton University State University of New York Dr. Nicole S. Fenty is
outside P26's existing academic environment, led them to adopt amore student-driven, interactive classroom dynamic. As McMillan and Chavis [10] suggest,diverse professional communities foster innovation through shared perspectives. While within-institution mentoring can provide familiarity and alignment with local policies, the externalvoices in this program encouraged mentees to adapt EBIPs creatively to their unique settings.This collaborative approach was enriched by mentors' efforts to understand the deeper aspects ofimplementation. As M02 explained: "I wanted to understand not just the methods they used, butwhat was actually going on in terms of the thought processes and decision-making processes...what made them adopt certain EBIPs or what
provided (incorrect and incomplete) Python code todetermine the categorization of the standards instead of performing the categorization itself. WeFigure 2: Llama performance. Key: H → human, AI → Llama, I → identical, D → different, S →similar, B → based on.reprompted it to generate usable results.Figures 2, 3, and 4 summarize the performance of each LLM. Mismatches with the human coderoccurred in about half of the instances: Llama (n = 46), Claude (n = 52), and ChatGPT(n = 41). However, when the LLM had a match with the human coder, it usually had the sameverdict as the human coder because it categorized the level of similarity in the same way as thehuman expert. However, and perhaps surprisingly, there were some instances where the
Development for adoption and adaptation of new instructional practices. In S. Linder, C. Lee, S. Stefl, & K. High (Eds.), Handbook of STEM Faculty Development (pp. 3–13). IAP.Birt, J. A., Khajeloo, M., Rega‐Brodsky, C. C., Siegel, M. A., Hancock, T. S., Cummings, K., & Nguyen, P. D. (2019). Fostering agency to overcome barriers in college science teaching: Going against the grain to enact reform‐based ideas. Science Education, 103(4), 770–798. https://doi.org/10.1002/sce.21519 19Estaiteyeh, M., & DeCoito, I. (2023). Planning for Differentiated Instruction: Empowering Teacher Candidates in STEM Education
platform to obtain direct feedback on system performance. It would also be useful to compare this system with other methods of validating academic credentials that are already on the market to assess the effectiveness and efficiency of the solution against existing alternatives.References[1] O. S. Saleh, O. Ghazali, and N. B. Idris, “Enhancing Academic Certificate Privacy with a Hyperledger Fabric Blockchain-Based Access Control Approach,” SN Comput. Sci., vol. 4, no. 5, p. 602, Aug. 2023, doi: 10.1007/s42979-023-02060-0.[2] E. Wolz, M. Gottlieb, and H. Pongratz, “Digital Credentials in Higher Education Institutions: A Literature Review,” in Innovation Through Information Systems, F. Ahlemann, R. Schütte, and S
flexible choice for applicationslike cookie classification and wildcard matching in cybersecurity.3.3.3 Flan-T5Flan-T5 is an enhanced version of the T5 model that incorporates instruction fine-tuning 15 . By training on a mixture of tasks phrasedas instructions, Flan-T5 improves its ability to follow task descriptions and generalize to new tasks. This makes Flan-T5 particularlyeffective in zero-shot and few-shot learning scenarios, where the model needs to perform well on tasks it has not explicitly beentrained on. In the context of identifying wildcard matches in cookies, Flan-T5’s improved understanding of instructions can lead tomore accurate and reliable classification results.4 Results4.1 Experimental SetupThe experimental
built their owndefinitions of leadership [12]. For instance, one of the more popular definitions “borrowed” fromcommunication research defines leadership as follows: “Leadership is not defined by a title orposition, but rather as a process that takes place between leaders, followers, and/or teammembers” [13]. The field of business has defined leadership as “the process of interactiveinfluence that occurs when, in a given context, some people accept someone as their leader toachieve common goals.” [14] Winston and Patterson [15] from organizational studies defineleadership in the following way: A leader is one or more people who selects, equips, trains, and influences one or more follower(s) who have diverse gifts, abilities, and
Paper ID #48995Be an entrepreneur: Empowering with Data-Driven DecisionsProf. Juan Sebasti´an S´anchez-G´omez, Universidad ECCI ©American Society for Engineering Education, 2025 Be an entrepreneur: Empowering with Data-Driven Decisions Juan Sebastián Sánchez-Gómez1*, Luz Adilia Giraldo Vargas y Viviana Giraldo Vargas2 1 Universidad ECCI, Bogotá, Colombia 2 Politécnico Grancolombiano, Bogotá, Colombia *Corresponding author: jusesago@gmail.comAbstractIn the
: Society 5.0 and industry 5.0 as driving forces of future universities,” Journal of the Knowledge Economy, vol. 13, no. 4, pp. 3445–3471, 2022. [5] D. G. Broo, O. Kaynak, and S. M. Sait, “Rethinking engineering education at the age of industry 5.0,” Journal of Industrial Information Integration, vol. 25, p. 100311, 2022. [6] N. Kraus, K. Kraus, O. Manzhura, I. Ishchenko, and Y. Radzikhovska, “Digital transformation of business processes of enterprises on the way to becoming industry 5.0 in the gig economy,” WSEAS Transactions on Business and Economics, vol. 93, no. 20, pp. 1008–1029, 2023. [7] A. del Real Torres, D. S. Andreiana, A.´ Ojeda Rold´an, A. Hern´andez Bustos, and L. E. Acevedo Galicia, “A review of deep reinforcement
, A., R. Welch, S. Ressler, N. Dennis, D. Larson, C. Considine, T. Nilsson, J. O'Brien, and T. Lenox. 2008. “Exceed Teaching Workshop: Tenth Year Anniversary.” ASEE Annual Conference & Exposition, (June). 10.18260/1-2--3963.[2] Estes, A., Ressler, S., Saviz, C., Barry, B., Considine, C., Coward, D., Dennis, N., Hamilton, S., Hurwitz, D., Kunberger, T., Lenox, T., Nilsson, T., Nolen, L., O’Brien, J., O’Neill, R., Saftner, D., Salyards, K., and Welch, R. 2018. “Celebrating 20 Years of the ExCEEd Teaching Workshop.” ASEE Annual Conference & Exposition, (June). 10.18260/1-2--30180.[3] Hamilton, S. R., C. L. Considine, T. Kunberger, T. L. Nisson, L. Nolen, D. A. Saftner, and C. M. Saviz. 2023. “Developing faculty leaders
competencies of electrical and computingengineers considering market demand," in 2014 IEEE Frontiers in Education Conference (FIE)Proceedings, Oct. 2014, pp. 1-4, IEEE.[3] S. Lappin and S. M. Shieber, "Machine learning theory and practice as a source of insight intouniversal grammar," Journal of Linguistics, vol. 43, no. 2, pp. 393-427, 2007.[4] V. Braun and V. Clarke, "Using Thematic Analysis in Psychology," Qualitative Research inPsychology, vol. 3, no. 2, pp. 77-101, 2006.[5] M. Zhang, K. N. Jensen, R. van der Goot, and B. Plank, "Skill extraction from job postingsusing weak supervision," arXiv preprint arXiv:2209.08071, 2022.M. Zhang, K. N. Jensen, S. D. Sonniks, and B. Plank, "SkillSpan: Hard and soft skill extractionfrom English job postings
Paper ID #49141Integrating Research Experience into Industry Sponsored Capstone DesignProjects in Mechanical and Manufacturing Engineering TechnologyDr. Irina Nicoleta Ciobanescu Husanu, Drexel University Irina Ciobanescu Husanu, Ph. D. is Associate Clinical Professor and Director of the Engineering Technology Program, Drexel University, Philadelphia, PA. She received her PhD degree in mechanical engineering from College of Engineering at Drexel University and her BS/MS in Aeronautical Engineering from Aerospace Engineering College at Polytechnic University of Bucharest, Romania. Dr. Husanu’ s educational background is in
the CFAs (Social, Knowledge, and Encounter) organized chronologically.In contrast, Group B only had access to an unsorted spreadsheet of activities within the LMS.Group B, thus, did not receive any weekly reminders or LMS-posted information.Group A & B students registered for the CFAs through Google Forms (the links were provided inthe spreadsheet/LMS). Students were asked to provide information about their section, how theyheard about the event, why they wanted to attend it, and any specific question(s) they hoped toget an answer to. Upon completing the form, they received a Google Calendar invite to confirmtheir registration.After each event, students completed a reflection form to document their experiences andconfirm
. Upon re-coding the data, the researchers would meet again to compare whichcode(s) were applied to which responses. In the case of disagreement regarding code assignment,a third researcher would arbitrate and ultimately decide which code(s) were applicable.The researchers determined the inter-rater reliability percentage (i.e., the percentage of agreementon assigned final codes between the researchers) for each survey question and focus group whenapplicable. The percentages for the survey questions can be found in Tab. 1, and the focus groupquestions resulted in values of 97.35%, 97.62%, 100%, and 100% for Q1, Q2, Q3, and Q4,respectively. Table 1: Student Survey Participation by QuestionQuestion n Course Response
: 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), 79–89.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), 69–91.http://dx.doi.org/10.1177/07399863910131002.[7] Rincon, B. E., & George-Jackson, C. E. (2016). STEM intervention programs: fundingpractices and challenges. Studies in
Psychological, Academic, and Economic Impact of COVID- 19 on College Students in the Epicenter of the Pandemic,” Emerging Adulthood, vol. 10, no. 2, pp. 473–490, Apr. 2022, doi: 10.1177/21676968211066657.[2] S. Abelson, S. K. Lipson, and D. Eisenberg, “Mental Health in College Populations: A Multidisciplinary Review of What Works, Evidence Gaps, and Paths Forward,” in Higher Education: Handbook of Theory and Research: Volume 37, L. W. Perna, Ed., in Higher Education: Handbook of Theory and Research. , Cham: Springer International Publishing, 2021, pp. 1–107. doi: 10.1007/978-3-030-66959-1_6-1.[3] J. R. Deters, J. A. Leydens, J. Case, and M. Cowell, “Engineering culture under stress: A comparative case study of undergraduate
of belonging, motivation, and academic performance. The following is anexemplar statement from Participant 2’s 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
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
PacTransRegion 10 University Transportation Center, the University of Washington, and the WashingtonState Department of Transportation. All students and their parents signed consent forms to grantpermission to be photographed and recorded by the camp sponsor. Students and parents also givepermission for photos, videos, and the project work to be shared through various mediums andplatforms.Reference[1] The Workforce Challenge, Special Report 275, Transportation Research Board, 2003[2] Arık, M., & Topçu, M. S. (2020). Implementation of engineering design process in the K-12science classrooms: Trends and issues. Research in Science Education, 1-23.https://doi.org/10.1007/s11165-019-09912-x[3] National Academy of Sciences, National Academy of
in education andopens the door to new opportunities for personalization and adaptability in virtual environments.Integrating advanced technologies with robust pedagogical approaches is essential to transformteaching and learning in the digital age.References[1] S. Martín, E. López-Martín, A. Moreno-Pulido, R. Meier, and M. Castro, “A Comparative Analysis of Worldwide Trends in the Use of Information and Communications Technology in Engineering Education,” Ieee Access, 2019, doi: 10.1109/access.2019.2935019.[2] O. Kuzu, “Digital Transformation in Higher Education: A Case Study on Strategic Plans,” Vysshee Obrazovanie v Rossii = Higher Education in Russia, 2020, doi: 10.31992/0869-3617-2019-29-3- 9-23.[3] B. R. Aditya
systematically address the research question. STAGE 1: Identifying the research question(s) The research question was formed in this stage that guided our scoping review study: What is the current landscape of literature on the financial well-being of engineering graduate students at master’s degree level, with a focus on financial anxiety and financial stress related to student loans? Based on this research question, we defined our Population-Concept-Context (PCC) framework, which further guided the inclusion and exclusion criteria of our study. We defined the precise terms structured within this framework. Table 2 outlines the PCC framework that we used to define our concept lines