conflicts, could not attend even if they wanted toThe cross-question network analysis (Figure 3) revealed that the most frequent co-occurrencebetween a motivation and barrier was Assignment Focused Study (A1) and Too many/too fewstudents (SP1), appearing together in 20 student responses.Figure 1: Area-proportional Venn diagrams for responses coded with codes from a single theme or multiple themes.Panel A: reported motivations for attending office hours; panel B: reported barriers. Panels are not to scale with eachother.Figure 2: Network visualization of code co-occurrences in student responses. Left Panel show relationships between motivationcodes; Right Panel represents relationships between barrier codes. Node size indicates frequency of
MERGEplatform in three geotechnical engineeringcourses with 35 students. To assess theplatform’s effectiveness morecomprehensively, we expanded our priorknowledge test from 11 to 18 questions ontwo topics, thermal conductivity and directshear testing. Students completed pre- andpost-tests before and after playing thegame, respectively. The results showedoverall score improvements, particularly inthe "Direct Shear" section (10 questions),where the average increased from 2.34 to Fig 1. (a) Bearing capacity calculator (b)7.06, indicating a significant learning Knowledge mapimpact. The "Thermal Conductivity"section (8 questions) also improved, with scores rising from 2.97 to 5.94, suggesting
Paper ID #48184Systematic Review of Faculty Adoption and Implementation of Artificial Intelligencein Engineering EducationDeborah Moyaki, University of Georgia Deborah Moyaki is a doctoral candidate in the Engineering Education and Transformative Practice program at the University of Georgia. She holds a bachelor’s degree in Educational Technology and is excited about the possibilities technology offers to the learning experience beyond the formal classroom setting. Her research focuses on enhancing the educational experience of engineering faculty and students by utilizing emerging technologies, including virtual reality
Paper ID #46083Providing engineering education researchers and stakeholders with easy accessto granular, disparate data sourcesJordan Esiason, SageFox Consulting Group Jordan Esiason has been working in STEM education research since 2018. He has been awarded an NSF CSGrad4US Fellowship and is currently pursuing a doctorate in computer science. Jordan’s current work includes developing data visualization tools for researchers, as well as tools for affect-responsive game-based learning environments. His interests broadly involve applying data mining and machine learning techniques such as natural language processing and
. 6Moustakas, C. (1994). Phenomenological research methods. SAGE Publications.Nørgaard, Birgitte & Ammentorp, Jette & Kyvik, Kirsten & Kofoed, Poul-Erik. (2012). Communication Skills Training Increases Self-Efficacy of Health Care Professionals. The Journal of continuing education in the health professions. 32. 90-7. 10.1002/chp.21131.Raelin, J. A., Bailey, M. B., Hamann, J. C., Whitman, D. L., Reisberg, R., & Pendleton, L. K. (2013, June). The effect of cooperative education and contextual support on the retention of undergraduate engineering students. In 2013 ASEE Annual Conference & Exposition (pp. 23-1190).Raelin, J.A., Bailey, M.B., Hamann, J., Pendleton, L.K., Reisberg, R. and Whitman, D.L. (2014
flexibility engine within the LMSand adjustments to the underlying framework to facilitate adaptability in the dynamic assignmentof materials, tasks, and evaluations, utilizing a more extensive cluster model encompassing abroader spectrum of student characteristics.References[1] S. Park, “Analysis of Time-on-Task, Behavior Experiences, and Performance in Two Online Courses With Different Authentic Learning Tasks,” The International Review of Research in Open and Distributed Learning, 2017, doi: 10.19173/irrodl.v18i2.2433.[2] Z. Zen, Reflianto, Syamsuar, and F. Ariani, “Academic Achievement: The Effect of Project-Based Online Learning Method and Student Engagement,” Heliyon, 2022, doi: 10.1016/j.heliyon.2022.e11509.[3] S. B
how hybrid and online formats can be adapted to more diversecontexts and maximize their effectiveness in different student populations.AcknowledgmentsThe authors gratefully acknowledge the leadership and financial support of the School ofEngineering at the Universidad Andres Bello, Chile. We also thank the Educational Research andAcademic Development Unit (UNIDA) for its mentorship and guidance in developing researchskills for higher education faculty.References[1] G. Zhang, T. Anderson, M. Ohland, & B. Thorndyke, "Identifying factors influencing engineering student graduation: a longitudinal and cross‐institutional study," Journal of Engineering Education, vol. 93, no. 4, p. 313-320, 2004. https://doi.org/10.1002/j.2168
why not, using examples from your interactions. Response: Open-text field.Section 5: Perceptions of Entrepreneurial Behaviors 14. Rate the following statements: Scale: Strongly Agree, Somewhat Agree, Neither Agree nor Disagree, Somewhat Disagree, Strongly Disagree. o When working in a group, I ensure everyone participates. o I can change plans quickly and effectively when needed. o I enjoy solving problems and thinking of new ideas. o I investigate both sides of an argument. o I like having a backup plan in case my original plan doesn’t work.Appendix B: Full Table of Skills Gained Skill N Themes Fixed Independent Work on Tasks
Lafayette. She received her B. Eng from Federal University Oye-Ekiti in Electrical and Electronics Engineering. She has 13 years of industry experience as a Reliability Engineer (Electrical) in the manufacturing Industry. Her research interests involve advancing ethics, empathy, and policies in engineering education, specifically related to women in engineering, minoritized and underrepresented groups, and strategies to enhance their interest in engineering. ©American Society for Engineering Education, 2025 Research in the Formation of Engineers: Prompting Socially Engaged Divergent Thinking in Engineering Design by Leveraging Generative AIAbstractWith appropriate scaffolding and prompt
wear non-medical masks,which is planned for submission to the ASEE conference. An external evaluator evaluated theREU activities at both sites, presented in detail next.3 Evaluations and resultsIn this section, the REU site external evaluation results are discussed. The results were notifiedto the PIs of the collaborative REU site, maintaining the anonymity of the survey responses.The survey provided a qualitative analysis of the 10-week REU program, focusing on four keyareas: (A) participant characteristics, including technical skills, prior research experience,attitudes and beliefs about research, and confidence levels; (B) research mentors’ evaluations ofthe participants; (C) the program’s impact on the participants; and (D) participant
the exam marks come from the Apply andAnalyze levels of the taxonomy, which is not unexpected as (a) testing lower levels of thetaxonomy such as Remember and Understand on an exam is not considered good assessmentpractice as it encourages rote learning; and (b) Evaluate and Create levels are typically assessedthrough practical project activities during semester. Examining the course ILOs reveals that twoof the three ILOs map to the analog section of the final examination – one is at the ‘Apply’ leveland the other at the ‘Analyze’ level, which implies the examination is broadly in alignment.A plot of all students’ scores on the Analysis scale versus the Application scale is shown inFigure 3. 100.0
the importance of age-appropriate circuit education tools,offering insights for educators designing engaging and developmentally appropriate STEMcurriculums for young learners.INTRODUCTIONEarly exposure to circuit education is crucial for developing the knowledge and skills they needfor future studies in engineering. However, traditional circuit education has largely relied ontheoretical instruction, often failing to accommodate the cognitive and motor skill levels of earlyelementary students. Conventional circuit components are typically small, requiring fine motorprecision, and their abstract nature can make it difficult for young learners to form a tangibleunderstanding of electrical principles. Research suggests that young children
Practices,” Sustainability (Switzerland), vol. 15, no. 22, Nov. 2023, doi: 10.3390/su152215870.[20] M. van den Berg, H. Voordijk, and A. Adriaanse, “Information processing for end-of-life coordination: a multiple-case study,” Construction Innovation, vol. 20, no. 4, pp. 647– 671, Aug. 2020, doi: 10.1108/CI-06-2019-0054.[21] D. William Dobson, A. Sourani, B. Sertyesilisik, and A. Tunstall, “Sustainable Construction: Analysis of Its Costs and Benefits,” American Journal of Civil Engineering and Architecture, vol. 1, no. 2, pp. 32–38, Apr. 2013, doi: 10.12691/ajcea-1-2-2.[22] C. Calle Müller, L. Lagos, and M. Elzomor, “Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response
, Design, and Technology program at the Pennsylvania State University.Dr. Gi Woong Choi, University of Cincinnati Gi Woong Choi, Ph.D. is an Assistant Professor of Instructional Design and Technology. Dr. Choi received his Ph.D. in Learning, Design, and Technology from Penn State University and has a background in human-computer interaction and user experience. His current research interests include AI in Education, informal STEM learning, problem-solving, makerspaces, and educational affordances of technologies.Ju Hui Kang, University of Cincinnati Ju Hui Kang is a PhD student in Instructional Design and Technology at the University of Cincinnati. She has a previous background in human-computer interaction and
Transformer (ViT) models forsatellite imagery-based wildfire identification. In comparison toconventional Convolutional Neural Networks (CNNs), ViT modelsare trained to detect early indications of wildfires with betteraccuracy and generalization using publicly accessible NASAsatellite image datasets. The project involves setting up theenvironment, preparing and preprocessing a wildfire dataset, andtraining a ViT model using PyTorch Library. The trained model isevaluated on test data to assess its accuracy and reliability.Additionally, attention maps are visualized to interpret the model’s Fig1: Wildfire.decision-making process. Results demonstrate the potential ofViT models in capturing complex patterns in satellite images
EEM cohort emphasizedapplied engineering (e.g., energy systems, machinery, etc.). Thisdisciplinary contrast allowed a comparative analysis of learningoutcomes in technical versus interdisciplinary contexts. Fig 1. Magnitude in Improvement in Both Course IV. FINDINGS This section presents a detailed overview of the study’s B. Skills Assessmentsquantitative and qualitative results, highlighting how the We used discipline-specific tasks to evaluate appliedintervention differentially affected technical proficiency, proficiency. DB students were asked to craft normalized SQLperceived skill development, and overall
Topics.Appendix B Challenges in Implementing Curriculum Change and other Barriers to Calculus ReformA major current barrier to broader enrollment in this experimental course lies in the prerequisite structureof engineering electives. Students in Robotics take courses in Circuits, Dynamics, and Machine Learning,just to name a few, which are taught in other departments and have calculus prerequisites explicitlydefined in terms of traditional Calculus I, II, and ODEs. Because Calculus for the ModernEngineer is not yet recognized as equivalent, students must go on a waitlist for these courses, oftenresulting in exclusion. Additionally, approximately 30% of our students are double majors in CS, CSE,ECE, or ME—departments that
STEM fields. Social Psychology of Education, 15(4), 427-448. 6. Burke, R. J., & Mattis, M. C. (2007). Women and Minorities in Science, Technology, Engineering, and Mathematics: Upping the Numbers. Edward Elgar Publishing. 7. Margolis, J., & Fisher, A. (2002). Unlocking the Clubhouse: Women in Computing. MIT Press. 8. Leaper, C. 2015. Do I Belong?: Gender, Peer Groups, and STEM Achievement. Sociology Education. International Journal of Gender, Science, and Technology. 9. Cech, E., Rubineau, B., Silbey, S., & Seron, C. (2011). Professional role confidence and gendered persistence in engineering. American Sociological Review, 76(5), 641-666.10. Brawner, C. E., Camacho, M. M., Lord, S. M., Long, R. A., & Ohland, M. W
2024].[2] M. K. Swenty and B. J. Swenty, "Professional Licensure: The Core of the Civil Engineering Body of Knowledge," in Proceedings of the 2017 ASEE Annual Conference, Columbus, Ohio, 2017.[3] M. K. Swenty and B. J. Swenty, "A Study of EAC-ABET Civil Engineering Accreditation Curriculum Requirements and Exemption Provisions of State Licensure Laws and Rules," in ASEE National Conference and Exposition, Baltimore, MD, 2023.[4] B. J. Swenty and M. K. Swenty, "Licensure Requirements for Teaching Civil Engineering Design Courses in the United States," in Proceedings of the 2020 ASEE Annual Conference, Virtual Conference, 2020.[5] M. K. Swenty and B. J. Swenty, "Is Engineering Education the Weak Link in Licensure’s
decolonisation of knowledge. Africa Insight, 44(1), 23–37.Kemmis, S., & McTaggart, R. (2005). Participatory action research: Communicative action and the public sphere. In N. K. Denzin & Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (pp. 559-603). SAGE Publications.Kirkpatrick, K., & Faragó, B. (Eds.). (2015). Animals in Irish literature and culture. PalgraveMacmillan UK. https://doi.org/10.1057/9781137434807Kramer, A. (2022). Thinking like an engineer: Interrogating the epistemic hierarchy of a professional engineering community of practice. The Ohio State University.Leavy, P. (2020). Method meets art: Arts-based research practice. Guilford publications.Leydens, J. A., & Lucena, J. C. (2017
Paper ID #45468From essential to ridiculous: Exploring instructor perceptions of empathy-focusedinstructionJennifer Howcroft, University of Waterloo Jennifer Howcroft is a Continuing Lecturer in the Department of Systems Design Engineering at the University of Waterloo. Her pedagogical research focuses on engineering design, holistic engineering education, stakeholder interactions, and empathy in engineering education.Dr. Kate Mercer, University of Waterloo Dr. Kate Mercer graduated with a Master of Information from the University of Toronto, and a PhD in Pharmacy from the University of Waterloo, focusing on
Publication and Reviews, vol. 4, no. 12, p. 1556-1562, 2023.[24] R.R. Panigrahi, A.K. Shrivastava, K. M. Qureshi, B. G. Mewada, S. Y. Alghamdi, N. Almakayeel, A. S. Almuflish, and M. R. N. Qureshi, “AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country,” Sustainability, vol.15, no.18, 13743, 2023. Available: https://doi.org/10.3390/su151813743[25] M. Chiriatti, M. Ganapini, E. Panai, M. Ubiali, and G. Riva, “The Case for Human-AI Interaction as System 0 Thinking,” Nature Human Behavior, vol. 8, p. 1829-1830, 2024. Available: https://doi.org/10.1038/s41562-024-01995-5[26] G. Y. Wiliams and S. Lim, “Psychology of AI: How AI Impacts the Way People
sequences problems," Computer Applications in Engineering Education, vol. 28, (2), pp. 304–313, 2020.[8] B. Golman and A. Yermukhambetova, "An Excel VBA‐based educational module for simulation and energy optimization of spray drying process," Computer Applications in Engineering Education, vol. 27, (5), pp. 1103–1112, 2019.[9] I. Nachtigalova et al, "A spreadsheet‐based tool for education of chemical process simulation and control fundamentals," Computer Applications in Engineering Education, vol. 28, (4), pp. 923–937, 2020.[10] K. W. Wong and J. P. Barford, "Teaching Excel VBA as a problem solving tool for chemical engineering core courses," Education for Chemical Engineers, vol. 5, (4), pp. e72– e77, 2010.
–status quo forces tend to dominate in workplaces.Those who A. have not personally experienced discrimination and B. are more likely to be inpositions of power, i.e. majority men, tend to be more satisfied with the status quo (e.g.,[32]). On the other hand, experienced discrimination seems to impact men’s views of equityclimate more than women’s. Although Bairoh and Putila [31] did not study EQC, theirfindings suggest that men who had experienced (gender-based) discrimination were ratherpessimistic about equity in their work communities. Analyzing such results further couldyield interesting insights on how to improve both gender and age equality inengineering/technology workplaces, both in Finland and elsewhere.Certain previous studies conducted
Design Instructors”.The authors would like to acknowledge the significant efforts of Marian Boktor in conductingthe search for existing sustainability tools.BibliographyAlhawamdeh, B., & Muchson, M., & Al-Bodour, A. M. R. (2024, March), Sustainability Components Assessment of Engineering Design Capstone Projects Paper presented at 2024 ASEE North Central Section Conference, Kalamazoo, Michigan. 10.18260/1-2—45637Ansys Granta EduPak, https://www.ansys.com/products/materials/granta-edupack, accessed 1/7/2025Brown, S,, Bornasal F,, Brooks S,, Martin JP. 2014. J. Prof. Issues Eng. Educ. Pract. 141 (2): C4014005.Byggeth S, Broman G, Robert KH. 2007. A method for sustainable product development based on a modular system of guiding questions
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