proposed future investigationswould benefit from the inclusion of perspectives on the prescribed structural changes fromfaculty and industry professionals and an exploration of factors such as the impact of students’race, gender, and incoming engineering knowledge on these trends. References[1] “Engineering by the numbers - American Society for Engineering Education.,” American Society of Engineering Education, https://ira.asee.org/wp-content/uploads/2017/ 07/2017 -Engineering-by-the-Numbers-3.pdf, 2017.[2] Shuman, L., Delaney, C., Wolfe, H., & Scalise, A., “Engineering attrition: Student characteristics and educational initiatives,” ASEE Annual Conference, 1999, pp. 4-229.[3
,” in 2021 ASEE Virtual Annual Conference Content Access, 2021.[11] R. G. Abd Ellah, “Water resources in Egypt and their challenges, Lake Nasser case study,” Egypt. J. Aquat. Res., vol. 46, no. 1, pp. 1–12, Mar. 2020, doi: 10.1016/j.ejar.2020.03.001.[12] P. E. Spector, Summated Rating Scale Construction: An Introduction. SAGE, 1992.[13] R. Johnson and L. Christensen, Educational research: Quantitative, qualitative, and mixed approaches, 5th ed. Washington D.C.: Sage Publisher, 2014.[14] J. F. Hair, W. Black, and B. Babin, Multivariate data analysis, 8th ed. Cengage Learning, 2019.[15] I. Osunbunmi, “A Mixed-Methods Study of College Experiences and Learning and Study Strategies of High-Achieving Engineering Students,” Grad
ASEE Annual Conference & Exposition, Tampa, Florida, Tampa, Florida, Jun. 2019. doi: 10.18260/1-2--32351.[11] K. Hadley and W. Oyetunji, “Extending the theoretical framework of numeracy to engineers,” J. Eng. Educ., vol. 111, no. 2, pp. 376–399, 2022, doi: 10.1002/jee.20453.[12] R. J. Mislevy, “Evidence and inference in educational assessment,” Psychometrika, vol. 59, no. 4, Art. no. 4, Dec. 1994, doi: 10.1007/BF02294388.[13] M. M. Riconscente, R. J. Mislevy, and S. Corrigan, “Evidence-centered design,” in Handbook of test development, 2nd ed, New York, NY, US: Routledge/Taylor & Francis Group, 2016, pp. 40–63.[14] Association of American Colleges and Universities, “Quantitative Literacy VALUE Rubric
ethnicity, without considering the intersectional nature of identities and how theyinteract to shape student experiences (Figard et al., 2023a). Although intersectionality research is growing in engineering education, studiesdiscussing disability as an aspect of intersectionality or identity are almost entirely nonexistent.While higher education research has slowly grown to address disabled students’ experiences oncampus and recognize disability as a social identity and aspect of campus diversity, it has stillfailed to address how ableism intersects with other aspects of oppression to impact disabledstudents’ experiences (Naples et al., 2019). Scholars have called for an expanded use ofintersectionality to study and work with disabled people
, doi: 10.1111/j.1744- 6570.1988.tb00632.x[3] D. Jackson, J. Fleming, and A. Rowe, “Enabling the Transfer of Skills and Knowledge across Classroom and Work Contexts,” Vocations and Learning, vol. 12, pp. 459-478, Mar. 2019, doi: 10.1007/s12186-019-09224-1[4] L.A. Perry, and J.S. London, “The Transfer of Learning Between School and Work: A New Stance in the Debate About Engineering Graduates’ Preparedness for Career Success abstract Paper,” in 2021 ASEE Virtual Annual Conference Content Access, Jul. 2021. [Online]. Available: https://peer.asee.org/37899[5] M. Taguma, E. Feron, and M.H. Lim, “A Literature Summary for Research on the Transfer of Learning,” in Future of Education and Skills 2030
Education Symposium, Cape Town, South Africa, 2019, pp. 10-12.[13] M. W. Mohiuddin, J. Tsenn, S. Balawi, C. R. Corleto, and J. Weaver-Rosen, "Vertical Integration of Teamwork Skills from Sophomore to Senior and Beyond!," in 2023 ASEE Annual Conference & Exposition, 2023.[14] E. Biech, The Pfeiffer book of successful team-building tools: Best of the annuals. John Wiley & Sons, 2007.[15] J. Brox. "The Results Are In: Poor Communication Number One Reason Teamwork Fails." https://www.refreshleadership.com/index.php/2015/09/results-poor- communication-number-reason-teamwork-fails/ (accessed February 10, 2023).[16] B. Groysberg and M. Slind. "The Silent Killer of Big Companies." Harvard Business
between 2018-2019. The remaining 5 participated inremote cohorts between 2020-2021 (see Appendix A for more details on participants’ cohorts). Table 1: Participants’ demographics and CSSI cohortFactor Value #. Factor Value #.Gender Women 10. First-generation college student Yes 4. Men 6. No 12.Race/Ethnicity* Asian 4. CSSI Location In-person 11. Black or African American 4
were selected based on financial need, academic ability, and letters ofrecommendation. Inclusion criteria for ongoing participation in this cohort include enrollment inan engineering major, a minimum GPA, attendance to cohort activities, and involvement in datacollection. Participants with continued enrollment since Fall of 2019 are included in the presentstudy. Data collection began during Fall of 2019 and at the time of this study, all participants hadcompleted their 3rd academic year and engaged in 6 semesters of data collection. To understandchanges in interest and subsequent enrollment decisions, the first three years of an engineeringprogram are the most appropriate to focus on as most enrollment changes happen during themiddle years [45
American Society of Engineering Education (ASEE), The Collaborative Network for Engineering and Computing Diversity (CoNECD), Frontiers in Education (FIE), as well as major psychological con- ferences.Catherine G. P. Berdanier, Pennsylvania State University Catherine G.P. Berdanier is an Assistant Professor in the Department of Mechanical Engineering at Penn- sylvania State University. She earned her B.S. in Chemistry from The University of South Dakota, her M.S. in Aeronautical and Astronautical Engineering and her PhD in Engineering Education from Purdue University. Her research expertise lies in characterizing graduate-level attrition, persistence, and career trajectories; engineering writing and communication; and
first author of nine peer- reviewed papers and a reviewer of three software engineering and natural language processing textbooks. Dr. Beasley has received the ASEE State of Engineering Education in 25 Years Award and USF Spirit of Innovation Award. He plays the guitar at his church and has spent five summers as a volunteer English teacher in Taiwan. Dr. Beasley joined the University of South Florida in August 2020 as an Assistant Professor of Instruction and is a USF STEER STEM Scholar. ©American Society for Engineering Education, 2023Strategies to Optimize Student Success in Pair Programming Teams1. IntroductionPair programming is a software development paradigm used both in industry and in
knowledge in the STEMdomain. Story-driven learning, by contrast, involves students sharing personal, potentiallyvulnerable, stories to improve their identity formation and entrepreneurial mindset, necessitatingthe identification of potentially unique pedagogical practices distinct from those used intraditional STEM settings. We started with a deductive (or top-down) approach, drawing on existing theories andresearch to anticipate specific pedagogical practices expected in these classrooms (e.g., Ellis etal., 2019; Herbel-Eisenmann et al., 2013; Smith et al., 2013). This helped us hypothesize whichpedagogical practices might emerge in story-driven learning classrooms, such as instructorslinking past and current topics (Kranzfelder et al
engineers and negotiate their multiple identities in the current culture of engineering. Dina has won several awards including the 2022-2023 Outstanding Research Publication Award by the American Educational Research Association (AERA) Division I, 2018 ASEE/IEEE Frontiers in Education Conference Best Diversity Paper Award, 2019 College of Engineering Outstanding Graduate Student Research Award and the Alliance for Graduate Education and the Professoriate (AGEP) Distinguished Scholar Award. Dina’s dissertation proposal was selected as part of the top 3 in the 2018 American Educational Research Association (AERA) Division D In-Progress Research Gala. Dina was a 2016 recipient of the National Science Foundation’s Graduate
engineering education research conferences and journals. Particularly, his work is published in the International Conference on Transformations in Engineering Education (ICTIEE), American Society for Engineering Education (ASEE), Computer Applications in Engineering Education (CAEE), International Journal of Engineering Education (IJEE), Journal of Engineering Education Transformations (JEET), and IEEE Transactions on Education. He is also serving as a reviewer for a number of conferences and journals focused on engineering education research. ©American Society for Engineering Education, 2024 Generative Artificial Intelligence in Undergraduate Engineering: A Systematic
mining and learning analytics in engineering education, broadening student participation in engineering, faculty preparedness in cognitive, affective, and psychomotor domains of learning, and faculty experiences in teaching online courses. He has published papers at several engineering education research conferences and journals. Particularly, his work is published in the International Conference on Transformations in Engineering Education (ICTIEE), American Society for Engineering Education (ASEE), Computer Applications in Engineering Education (CAEE), International Journal of Engineering Education (IJEE), Journal of Engineering Education Transformations (JEET), and IEEE Transactions on Education. He is also serving
-relatedfigures, ET programs had over 30,000 students and 10,000 graduates in 2021 (American Societyof Engineering Education [ASEE], 2022). Significant proportions of Bachelor of Science (BS)ET programs have articulation agreements that allow transfer of students with Associate ofApplied Science (AAS) and Associate of Science (AS) in ET (NAE, 2017). A recent survey ofET leaders for the 2019 ET Leadership Institute indicated that serving community collegetransfer students was among the top perceived opportunities for the future of ET programs (Foxet al., 2020). National data show that ET programs attract students from certain marginalized andminoritized groups, including neotraditional age (24 and older) and Black/African Americanstudents (NAE, 2017
& Exposition, Jun. 2017. Accessed: Jan. 06, 2022. [Online]. Available: http://peer.asee.org/designing-a-course-for-peer-educators-in-undergraduate-engineering- design-courses[2] Y. Cao, C. Smith, B. D. Lutz, and M. Koretsky, “Cultivating the next generation: Outcomes from a Learning Assistant program in engineering,” presented at the 2018 ASEE Annual Conference & Exposition, Jun. 2018. Accessed: Oct. 24, 2019. [Online]. Available: https://peer.asee.org/cultivating-the-next-generation-outcomes-from-a-learning-assistant- program-in-engineering[3] Blinded[4] V. Otero, S. Pollock, and N. Finkelstein, “A physics department’s role in preparing physics teachers: The Colorado learning assistant model,” Am. J. Phys., vol
Dean’s Awards for Outstanding New Faculty, Outstanding Teacher Award, and a Faculty Fellow. Dr. Matusovich has served the Educational Research and Methods (ERM) division of ASEE in many capacities over the past 10+ years including serving as Chair from 2017-2019. Dr. Matusovich is currently the Editor-in-Chief of the journal, Advances in Engineering Education and she serves on the ASEE committee for Scholarly Publications. ©American Society for Engineering Education, 2024 Stumbling Our Way Through Finding a Better Prompt: Using GPT-4 to Analyze Engineering Faculty Members’ Mental Models of AssessmentAbstractIn this full research paper, we discuss the benefits and challenges of using GPT-4 to
Instructional Strategies (RBIS) in Engineering Science Courses,” Journal of Engineering Education, vol. 102, no. 3, pp. 394–425, 2013, doi: 10.1002/jee.20020.[3] R. S. Moog and J. N. Spencer, Eds., Process-Oriented Guided Inquiry Learning (POGIL). Oxford University Press, USA, 2008.[4] S. R. Simonson, Ed., POGIL: An Introduction to Process Oriented Guided Inquiry Learning for Those Who Wish to Empower Learners. Stylus Publishing, LLC, 2019.[5] E. Mazur, “Peer instruction: Getting students to think in class,” AIP Conference Proceedings, vol. 399, no. 1, pp. 981–988, Mar. 1997, doi: 10.1063/1.53199.[6] E. Mazur, “Peer Instruction: A User’s Manual,” American Journal of Physics, vol. 67, no. 4, pp. 359–360, Apr. 1999, doi: 10.1119
. Vignoli, "Measuring design thinking mindset," presented at the Proceedings of the DESIGN 2018 15th International Design Conference, 2018.[8] S. Patel and K. Mehta, "Systems, design, and entrepreneurial thinking: Comparative frameworks," Systemic Practice and Action Research, vol. 30, no. 5, pp. 515–533, 2016, https://doi.org/10.1007/s11213-016-9404-5.[9] A. Jackson et al., "Learning by evaluating (LbE): Engaging students in evaluation as a pedagogical strategy to improve design thinking," presented at the 2023 ASEE Annual Conference & Exposition, Baltimore, MD, June 25–28, 2023, Poster presentation. [Online]. Available: https://peer.asee.org/42934.[10] M. Oliveira, E. Zancul, and A. L. Fleury
and AnalysisThe data used for this study came from PDS reports for 2020 and covered the academic yearfrom summer 2019 through spring 2020. This time frame includes the early phase of theCOVID-19 pandemic lockdown that began in spring 2020. Thus, many students’ reflections werenot yet impacted by that event. We particularly focused on students’ reports in either technicalactivities or research activities and their career goals for analysis, which helped us answer theresearch questions. For technical work experience, we chose four open-ended questions from thesurvey to analyze in this study (see Appendix A). We also selected the four questions fromstudents who reported on their research experience in the survey (see Appendix B). For
performance equationcan be described using Eq.1.ZXA = t A . ZT + iA ZI + eA . ZEA → (Eq.1)Where ZXA is the performance of ratee, ZT is the true ability, ZI are the environmental factors.And ZEA is random error, whereas, t A ,iA , and eA are the weights of the different components.References[1] R. J. Wherry and C. J. Bartlett, “THE CONTROL OF BIAS IN RATINGS: A THEORYOF RATING,” Personnel Psychology, vol. 35, no. 3, pp. 521–551, Sep. 1982, doi:10.1111/j.1744-6570.1982.tb02208.x.[2] Y. Chuang, H. Chiang, and A. Lin, “Helping behaviors convert negative affect into jobsatisfaction and creative performance: The moderating role of work competence,” PR, vol. 48,no. 6, pp. 1530–1547, Sep. 2019, doi: 10.1108/PR-01-2018-0038.[3] M. Kilduff, A
.[2] T. R. Hinkin, "A Brief Tutorial on the Development of Measures for Use in Survey Questionnaires," Organizational Research Methods, vol. 1, no. 1, pp. 104-121, 1998, doi: https://doi.org/10.1177/109442819800100106.[3] T. R. Hinkin, "Scale Development Measures.," in Research in Organizations, R. A. Swanson and E. F. H. III Eds. San Francisco, California: Berrett-Koehler Publishers, Inc, 2005, ch. 10.[4] A. Costello and J. Osborne, "Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis," Practical Assessment, Research, and Evaluation, vol. 10, Article 7, 2019, doi: https://doi.org/10.7275/jyj1-4868.[5] K. Popper, The Logic of Scientific
Paper ID #41992Putting Affect in Context: Meta-Affect, Beliefs, and Engineering IdentityAlyndra Mary Plagge, Trinity University Alyndra Plagge is an undergraduate Psychology student at Trinity University. She is majoring in Psychology and minoring in Education and set to graduate in May 2025. After graduation she plans to pursue her master’s degree.Dr. Emma Treadway, Trinity University Emma Treadway received the B.S. degree in Engineering Science from Trinity University in 2011, and her M.S.E. and Ph.D. degrees in Mechanical Engineering from the University of Michigan, Ann Arbor in 2017 and 2019, respectively. She
. Sadiq, and T. Husain, “Risk-based process safety assessment and control measures design for offshore process facilities,” J Hazard Mater, vol. 94, pp. 1–36, 2002.[20] R. Srinivasan, B. Srinivasan, M. U. Iqbal, A. Nemet, and Z. Kravanja, “Recent developments towards enhancing process safety: Inherent safety and cognitive engineering,” Comput Chem Eng, vol. 128, pp. 364–383, 2019, doi: 10.1016/j.compchemeng.2019.05.034.[21] D. D. Burkey, D. Anastasio, C. Bodnar, and M. Cooper, “Collaborative Research: Experiential Process Safety Training for Chemical Engineers. STEM for All Video Showcase,” ASEE Annual Conference and Exposition, Conference Proceedings, Jun. 07, 2020.[22] D. C. Hendershot and W. Smades, “Safety culture
, S., Henderson, C., & Prince, M. J. (2013). Estimates of use of research-based instructional strategies in core electrical or computer engineering courses. IEEE Transactions on Education, 56(4), 393-399.[8] Taraban, R. (2011). Information Fluency Growth Through Engineering Curricula: Analysis of Students' Text‐Processing Skills and Beliefs. Journal of Engineering Education, 100(2), 397-416.[9] Cheville, RA., 2019, “Pipeline, Pathway, or Ecosystem – Do Our Metaphors Matter?” Distinguished Lecture, ASEE Annual Conference, Tampa, 2019.[10] Borrego, M. J., Prince, M. J., Nellis, C. E., Shekhar, P., Waters, C., & Finelli, C. J. (2014, June). Student perceptions of instructional change in engineering courses: A pilot
are reflected in numerous publications and presentations at prestigious IEEE; ASEE conferences, Wiley’s & Springer Journals. His research primarily revolves around understanding Cognitive Engagement Analysis, Assessing Methods in Engineering Education, and Facial Expressions (emotions) in the Learning process. He is a member of various technical committees, serving as a reviewer for esteemed journals and international conferences including ASEE, Springer (JAIHC) , JCEN, and IEEE Transaction on Education. His commitment to advancing education, paired with his extensive academic and professional experiences, positions him as a promising researcher in engineering education.Dr. Angela Minichiello, Utah State