et al.(2024), Shorey et al. (2024), and Hsu & Silalahi (2024) to name a few, with the focus broadlyon ChatGPT, bots, and their societal effects without specific ties to education or laboratorycontexts.Considerable amount of literature aligns more closely with educational applications from theeducators’ perspective. Du et al. (2024), explore using NLP and large language models(LLMs) to automatically evaluate student project reports. Similarly, Caccavale et al. (2024) intheir article towards education 4.0, investigate the potential of LLMs as virtual tutors inchemical engineering. Tate et al. (2024)’s study examines the extent to which AI providesholistic essay scoring, while White et al. (2023) research focuses on assessing
. She is also serving as the Principal Investigator on the college’s NSF S-STEM grant, Building an Academic Community of Engineering Scholars.Carrie Kortegast, Northern Illinois University ©American Society for Engineering Education, 2025 Guides on the transfer journey: A qualitative study exploring the academic and social supports of community college transfer studentsIntroductionThis research brief explores the community college student’s transfer journey guided by thetransfer student capital and engineering identity frameworks. Academic supports, socialrelationships, and experiential learning are common programmatic approaches to fostering asense of belonging and engineering identity
democratizing generative AI solutions and bridging the gap between theoretical research and practical applications using AWS technologies. ©American Society for Engineering Education, 2025 Canary in the Mine Canary in the Mine: An LLM Augmented Survey ofDisciplinary Complaints to the Ordre des ingénieurs du Québec (OIQ) Abstract This study investigates disciplinary incidents involving engineers in Quebec, shed- ding light on critical gaps in engineering education. Through a comprehensive review of the disciplinary register of the Ordre des ingénieurs du Québec (OIQ)’s disciplinary register for 2010 to
computing education and its longitudinal impact on ethical decision making. Futurestudies could also measure the framework’s impact on students’ problem-solving abilities,especially when dealing with even more complex, real-world security or privacy challenges.AcknowledgementThis research is supported by the National Science Foundation (Award #: 2335681).AppendixS. Shin, J. Lee, S. Lim, and S. Shin. “Draft of ethical motivation and behavioral intention surveyin engineering education,” American Society for Engineering Education Annual Conference,June 22-25, 2025, Montreal, Canada, 2025.Sample Survey Items for Ethical Motivation and Behavioral IntentionWe used a 6-point Likert scale (including “Don’t Know” as an option) for this survey. Thesurvey is
and Health-related Outcomes in a National Sample of College Students,” Am. J. Health Educ., vol. 51, no. 6, pp. 383–394, 2020, doi: 10.1080/19325037.2020.1822242.[4] S. K. Lipson, J. Raifman, S. Abelson, and S. L. Reisner, “Gender minority mental health in the US: Results of a national survey on college campuses,” Am. J. Prev. Med., vol. 57, no. 3, pp. 293–301, 2019.[5] E. De Pillis and L. De Pillis, “Are engineering schools masculine and authoritarian? The mission statements say yes,” Journal of Diversity in Higher Education, vol. 1, no. 1. p. 33, 2008.[6] J. C. Garvey and C. V. Dolan, “Queer and Trans College Student Success,” in Higher Education: Handbook of Theory and Research: Volume 36, L. W. Perna, Ed., in
. Theexosystem follows, encapsulating indirect environments (e.g., experiences of roommate ingraduate school but in a different degree program and conversations with the individual aboutthese experiences). The macrosystem level includes social and cultural values, whereas the finalchronosystem level pertains to transitions in environment(s) over time, respectively [21]. It isimportant to note that there is a bidirectional relationship between a person and theirenvironment; that is, they both can impact one another (discussed via the process-person-context-time [PPCT] language) [5].Godfrey & Parker’s Culture of Engineering Education Framework (CEEF)CEEF was used to provide context to the environment and systems engineering graduatestudents
Mathematics (STEM): Current Knowledge, Implications for Practice, Policy, and Future Directions," Educational Psychology Review , vol. 29, pp. 119-140, 2017.[2] G. S. Weissmann, R. A. Ibarra, M. Howland-Davis and R. A. I. M. H.-D. &. Gary S. Weissmann, "The Multicontext Path to Redefining How We Access and Think about Diversity, Equity, and Inclusion in STEM," Journal of Geoscience Education, vol. 67, no. 4, pp. 320-29, 2019.[3] J. S. Brotman and F. M. Moore, "Girls and Science: A Review of Four Themes in the Science Education Literature," JOURNAL OF RESEARCH IN SCIENCE TEACHING, vol. 45, no. 9, pp. 971-1002, 2008.[4] H. I. Scutt, S. K. Gilmartin, S. D. Sheppard and S. R. Brunhaver, "Research-Informed Practices for Inclusive
work andthe experiences that prepared them for their global job tasks (RQ3).AcknowledgementsThis material is based upon work supported by the National Science Foundation (EEC-2308607).Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of NSF.References[1] J. M. Grandin and E. D. Hirleman, “Educating engineers as global citizens: A call for action / A report of the national summit meeting on the globalization of engineering education,” Online J. Glob. Eng. Educ., vol. 4, no. 1, pp. 1–28, 2009.[2] K. A. Davis and D. B. Knight, “Comparing students’ study abroad experiences and outcomes across global contexts,” Int. J. Intercult. Relat
Foundation.References 1. Canney, N. E., & Bielefeldt, A. R. (2016). Validity and reliability evidence of the engineering professional responsibility assessment tool. Journal of engineering education, 105(3), 452-477. 2. Murthy, J. N., Lavanya, C., & Kosaraju, S. (2020). Ethics in engineering profession: Pedagogy and practices. In K. Kumar & J. P. Davim (Eds.), Methodologies and outcomes of engineering and technological pedagogy (pp. 296-318). IGI Global. https://doi.org/10.4018/978-1-7998-2245-5.ch0143. Niles, S., Contreras, S., Roudbari, S., Kaminsky, J., & Harrison, J. L. (2020). Resisting and assisting engagement with public welfare in engineering education. Journal of Engineering Education, 109(3), 491
Development, vol. 58, no. 3, pp. 385-401, 2017.[6] E. Cech and T. Waidzunas, "“Engineers Who Happen To Be Gay”: Lesbian, Gay, And Bisexual Students’ Experiences In Engineering," in 2009 ASEE Annual Conference & Exposition, 2009.[7] E. Cech and T. Waidzunas, "Navigating the heteronormativity of engineering: the experiences of lesbian, gay, and bisexual students," Engineering Studies, vol. 3, no. 1, pp. 1-24, 2011.[8] A. Haverkamp, "The Complexity of Nonbinary Gender Inclusion in Enigneering Culture," in ASEE Annual Conference and Exposition, Salt Lake City, 2018.[9] A. Haverkamp, A. Butler, N. S. Pelzl, M. K. Bothwell, D. Montfort and Q.-L. Driskill, "Exploring Transgender and Gender Nonconforming Engineering Undergraduate Experiences
approaches in engineering education.Future research should examine how these different approaches to spatial reasoning might beeffectively combined in engineering education to prepare students for both technical precisionand practical problem-solving. Additionally, investigating how these findings translate acrossdifferent craft activities and engineering contexts could provide valuable insights forcurriculum development.ReferencesBailey, S. K. T., & Sims, V. K. (2014). Self-reported craft expertise predicts maintenance of spatial ability in old age. Cognitive Processing, 15(2), 227-231. https://doi.org/10.1007/s10339-013-0596-7Casey, B. M., Andrews, N., Schindler, H., Kersh, J. E., Samper, A., & Copley, J. (2008). The
10 through 12 students for data analytics education,” IEE Frontiers in Ed- ucation Conference, Oklahoma City, OK, pp. 916-918, 2013. 2. Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7, 1-29. 3. Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020). Big data analytics: Computational intelligence techniques and application areas. Technological Forecasting and Social Change, 153, 119253. 4. Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., Fallert, J.,Feussner, H., Giannarou, S., Mascagni, P., Nakawala, H., Park, A., Pugh
of theNational Science Foundation.References[1] A. Sullivan and Z. Beers, "Early exposure to computational thinking concepts in K-12education," Journal of STEM Education, vol. 19, no. 3, pp. 45-52, 2018[2] T. A. Signorella, R. Frieze, and J. Hershey, "Single-sex versus mixed-sex classes and genderschemata in children and adolescents: A longitudinal comparison," Journal of EducationalPsychology, vol. 85, no. 2, pp. 386-394, 1993[3] S. Metz, "Attracting the engineering of 2020 today," Journal of Engineering Education, vol.96, no. 3, pp. 1-4, 2007[4] C. Steele, "A threat in the air: How stereotypes shape intellectual identity and performance,"American Psychologist, vol. 52, no. 6, pp. 613-629, 1997[5] P. Davies, J. Spencer, and C. Steele
experiment instructions and related worksheets to give to the students.1. IntroductionWe seek primarily to describe the test fixture in sufficient detail for others to construct. Secondly,we will also describe a few of the experiments that one can conduct with the fixture. Finally, wewill illustrate some of the difficulties that students have conducting these experiments.The value of this simple apparatus is that we can build many and thus have students work in groupsof 3 to 4. Typically, we will have 4 groups in a lab period with an instructor and student assistant(s)(who did these experiments the previous year). The need for smaller groups of students to workin direct contact with the equipment has been recognized for many years. For example, see
education. She holds a Ph.D in computer science and specializes in a broad area of pervasive health technologies, and computer science education.Prof. Bryan Kim, Syracuse University Bryan S. Kim is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. His research interests center around building performant, reliable, and scalable memory and storage systems for data-intensive applications using emerging hardware technologies. His work has appeared in top computer systems venues such as FAST, ATC, OSDI, and EuroSys, and his research is supported through various projects, including the NSF CAREER award. ©American Society for Engineering
learning module in a Jupyter Notebook showing interactive links, editable python code and data visualization [30]. Figure 5: Visualization of material property predictions from an ML model [31]. Table 2: Published examples of computer-science-driven approaches to teaching AI/ML topics. Programming Computer Science and/or AI/ML Language, MS&E Topic Ref. (s) Topics Library, and EnvironmentPlotting, curve fitting, functions and Python, Materials characterization [30
withstudents’ social identities to create barriers to computing identity development using Lunn etal.’s (2021) computing identity framework. This work uncovered how unclear expectations inrelation to scheduling, financial obligations, and pre-requisite knowledge inhibited identitydevelopment, especially for post-traditionally aged and low-income students. The combinedfindings in this most recent work, which include all research participants since the beginning ofthe project, highlight the need for intentional HSCC servingness and consideration of thevarious social identities (i.e. Latine, men of color, working full time, low income, posttraditionally aged). These student characteristics are more frequently found in communitycollege students than they
-veterans.html[3]. “VA College Toolkit, ‘Characteristics of Student Veterans.’ [Online].” Accessed: Feb. 07,2024. [Online]. Available: https://www.mentalhealth.va.gov/student-veteran/learn-about-student-veterans.asp[4]. B. G. Crawford and J. B. Burke, “Student Veterans: Tapping into a Valuable Resource,” inASEE Annual Conference and Exposition, New Orleans, LA: American Society of EngineeringEducation, Jun. 2016.[6] E. S. Abes, S. R. Jones, and M. K. McEwen, “Reconceptualizing the Model of MultipleDimensions of Identity: The Role of Meaning-Making Capacity in the Construction of MultipleIdentities,” J. Coll. Stud. Dev., vol. 48, no. 1, pp. 1–22, 2007, doi: 10.1353/csd.2007.0000.[7] J. P. Gee, “Identity as an Analytic Lens for Research in Education,” Rev
. Kayumova is a recent recipient of the National Science Foundation’s Early Career award. Shakhnoza’s work appears in journals such as Anthropology & Education Quarterly, Educational Philosophy and Theory, Democracy and Education, and Journal of Research in Science Teaching (JRST). ©American Society for Engineering Education, 2025 NSF S-STEM AccEL: SCHOLARSHIPS TO ACCELERATE ENGINEERING LEADERSHIP AND IDENTITY IN GRADUATE STUDENTSIntroductionThis paper presents the outcomes of the second year of the Accelerated Engineering Leadership(AccEL) program. The inception of the AccEL program responds to projections by the U.S. Bureauof Labor Statistics (BLS) indicating a
enablecommunication between engineers and educational stakeholders who use these technologies with students. This framework will then support the transition of designing affordable robotics technology fromresearch to practice in K-12 education.References: Pedre, S., Nitsche, M., Pessagc, F., Caccavelli, J., & De Cristóforis, P. (2014). Design of a multi-purpose low-cost mobile robotAhmed, H., & La, H. M. (2019, March). Education-robotics symbiosis: An evaluation of challenges and proposed for research and education. In Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014
: A survey. Heliyon, 4(11).6. Kaveh, A. (2024). Applications of artificial neural networks and machine learning in civil engineering. Studies in computational intelligence, 1168, 472.7. Wu, B., Xu, J., Zhang, Y., Liu, B., Gong, Y., & Huang, J. (2024). Integration of computer networks and artificial neural networks for an AI-based network operator. arXiv preprint arXiv:2407.01541.8. Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87-99). Cham: Springer International Publishing.9. Khan, A. A., Laghari, A. A., & Awan, S. A. (2021). Machine learning in computer vision: a review. EAI Endorsed Transactions on Scalable Information
challenges, including navigating academic support, finding mentors, and buildingself-efficacy, can negatively impact their academic success and sense of belonging. TheEMPOWER Program, supported by the NSF S-STEM Program, is a collaboration between UCSan Diego, Southwestern College, and Imperial Valley College and is developed to mitigatethese challenges by offering scholarships, mentoring, networking, and research opportunities tolow-income transfer engineering students. Grounded in Schlossberg’s Transition Theory, thisexploratory study investigates the impact of participation and engagement in various social andacademic support activities. Motivation–including self-efficacy, self-determination, intrinsicmotivation, career motivation, and goal
Grant that established the Center of Excellence in Signal Integrity at Penn State Harrisburg, a $440K MRI NSF grant, a Volvo industrial grant and DURIP grant.Dr. Sedig Salem Agili, Pennsylvania State University, Harrisburg, The Capital College Sedig S. Agili received his BS, MS, and Ph.D. in Electrical and Computer Engineering from Marquette University in 1986, 1989, and 1996, respectively. Currently he is a Professor of Electrical Engineering teaching and conducting research in signal integrit ©American Society for Engineering Education, 2025 Further Signal Integrity Experiences in Undergraduate Education 1AbstractSignal integrity has been identified as one of the key areas for scientific
Universidade de S˜ao Paulo. Professor of Physics at Mau´a Institute of Technology, since 1994 and President of Teacher’s Academy at the same Institution.Dr. Nair Stem, IMT - Graduated at Physics (Bachelor) at IFUSP, Master at Electrical Engineering and Doctor at Electrical Engineering at EPUSP. ©American Society for Engineering Education, 2025 Leveraging immersive environments in physics labs and flipped classrooms for engineering courses.This paper aims to explore the use of immersive (panoramic) video with hotspots as apre-class activity for an investigative physics laboratory on the topic of oblique launches,in conjunction with the flipped classroom methodology. The goal is to study
during the students transition to college.Dr. Megan Che ©American Society for Engineering Education, 2025 A Citation Analysis of the Theoretical Model for Secondary-Tertiary Transition in MathematicsKaren C. Enderle Dr. S. Megan Che, Ph.D.Dept of Teaching and Learning Dept of Teaching and LearningCollege of Education College of EducationClemson University Clemson University,Clemson, SC, USA Clemson, SC, USAIntroductionIn this conceptual essay, a citation analysis of the Theoretical
“representative of a dance” that was the larger project of change.Finally, speakers described elements of their team philosophy that helped them to buildcognitively complex, ‘real’ teams. They took time together to debrief difficulties and celebratesmall wins. It was crucial to bring a “generous spirit” to the work and be “comfy with mistakes.”Giving people the benefit of the doubt and showing willingness to learn from one another“lubricates a lot of conversations” and “ease[s] a lot of tensions.” Over time they developed anunderstanding of what decisions could be organic and what should be formalized, and learnedpatience with the human side of the change process. One described change projects as cross-country races, not track meets: We “don’t have to
project intended to assist two-year college faculty andadministrators to prepare proposals for the National Science Foundation Scholarships in Science,Technology, Engineering, and Mathematics (S-STEM) Program. S-STEM proposals are expectedto be built on a foundation of deep needs analyses specific to the targeted population of studentsin STEM disciplines. Based on needs assessment, programs can then focus on implementingappropriate interventions and supports that will be most effective in improving the retention andcompletion of their students. Guidelines for streamlining the acquisition and organization ofcritical elements of student needs analyses can be useful for two-year college faculty andadministrators to develop NSF S-STEM proposals and
discrimination encountered byuniversity-educated engineering professionals in their work communities. The study alsoexplores the linkages between age discrimination and equity climate inengineering/technology workplaces in the context of a Nordic welfare state, Finland.Masculine cultures and discrimination in engineering/technology workplacesRecent studies affirm that many engineering/technology workplaces are, to this day,characterized by culture(s) that favor men and masculinity [4–8]. As Cheryan and colleagues[1] describe: “In STEM fields, a masculine culture is a social and structural environment thatconfers a greater sense of belonging and ability to succeed to men than women”. Masculinecultures in technology workplaces have been described as
, Germany, and was awarded M.S. and Ph.D. ©American Society for Engineering Education, 2025 2025 ASEE Annual Conference Montreal, Quebec, Canada, June 22 - 25, 2025 Zhang, Z., Li., W., Shirvani, K., Chang., Y., Hung, Y., Y., Esche, S. K. Flipped Classroom and Collaborative Learning in Tool Design Education for Mechanical Engineering Technology Zhou Zhang, Wenhai Li, Khosro Shirvani, Yizhe Chang, Yue Hung, Sven K. EscheAbstractTraditional Tool Design courses often rely on passive lectures and individual assignments, whichcan limit engagement and creativity, particularly for Mechanical
“representative of a dance” that was the larger project of change.Finally, speakers described elements of their team philosophy that helped them to buildcognitively complex, ‘real’ teams. They took time together to debrief difficulties and celebratesmall wins. It was crucial to bring a “generous spirit” to the work and be “comfy with mistakes.”Giving people the benefit of the doubt and showing willingness to learn from one another“lubricates a lot of conversations” and “ease[s] a lot of tensions.” Over time they developed anunderstanding of what decisions could be organic and what should be formalized, and learnedpatience with the human side of the change process. One described change projects as cross-country races, not track meets: We “don’t have to