Paper ID #49763Mindset Matters: Exploring Grit and Attitudes in Engineering and CS Undergradsin an NSF S-STEM funded programDr. Tina Johnson Cartwright, Marshall University Dr. Tina Cartwright is a professor of science education at Marshall University. She collaborates with colleagues across both the Colleges of Science and Engineering and Computer Science to support student success in STEM.Julie Lynn Snyder-Yuly, Marshall University Julie Snyder-Yuly, Associate Professor Department of Communication Studies, Marshall University (Ph.D. University of Utah, 2017). Dr. Snyder-Yuly’s research engages qualitative and
-secondary computing programs, and creating resources based on findings that are accessible to post-secondary computing programs nationwide.S. Kiersten Ferguson S. Kiersten Ferguson is a faculty research associate at NCWIT and the College of Engineering & Applied Science at the University of Colorado Boulder. Her scholarly and teaching interests include strategic planning and implementation with a focus on systemic organizational change; recruitment and retention of faculty and students; mixed reality simulations; and pedagogical and curricular choices in higher education. Prior to joining NCWIT in 2023, Kiersten was a clinical associate professor in the Annette Caldwell Simmons School of Education and Human
Paper ID #49546Improving the use of online resources to enhance efficiency of the ProblemBased Learning in Engineering EducationRomain Kazadi Tshikolu, University of Detroit MercyDr. Alan S Hoback, University of Detroit Mercy Professor of Civil, Architectural & Environmental Engineering, University of Detroit Mercy ©American Society for Engineering Education, 2025Improving the use of online resources to enhance efficiency of theProblem/Project Based Learning in Engineering EducationRomain Kazadi Tshikolu, Loyola University of Congo, DRC, kazadiro@udmercy.eduAlan Hoback, Department of Civil, Architectural
experiences, engaging in critical questioning, and offering support. Outside of academic studies, Jameka serves as an ambassador for her department, reviewer for ASEE, and active volunteer for a Columbus STEM non-profit See Brilliance. Jameka has been recognized by her undergraduate institution for her commitment to achieving the vision of the Ronald E. McNair Scholars Program and most recently by her department for her scholarship as a graduate researcher. Jameka strives to be a well-rounded scholar and exhibit her dedication to people and scholarship.Dr. Monica Cox, The Ohio State University Monica F. Cox, Ph.D., is Professor in the Department of Engineering Education at The Ohio State University.Mrs. Monique S. Ross
records to confirm relevance; 22 records were excluded at this stage. Throughthis process, 47 records were identified as relevant to the present topic. See Figure 1 for thecomplete PRISMA flow diagram [23].The following data items were extracted from all relevant articles: country in which study wasconducted; country (or countries) of author(s); aim of paper (or study); funding source(s);relevance to STEM educational setting; whether the technology was tested with the population ofinterest; study method; start & end date of data collection; inclusion & exclusion criteria forsample population; total number of participants; technology type; how was the technology wasused; outcome(s) measured; result of the intervention(s).ResultsThis
1 1 Background: Demographics • Asian Americans make up ~5.6% of households in the U.S., the second smallest racial group after First Nation groups [1] • Yet, (non-/immigrant) Asian/Asian Americans (A/AAs) are usually considered non-minoritized groups in postsecondary science and engineering (S&E) education as A/AA takes up 6%, 10%, 12%, and 11% of degree receipts of associates’, bachelor’s, master’s, and doctoral respectively [2] 2Asian Americans make up approximately 5.6% of households in the U.S. according
and their career progression in STEM fields [1]-[2].In order to bridge these gaps, the U.S. National Science Foundation (NSF) Scholarships inScience, Technology, Engineering, and Mathematics Program (S-STEM) has fundedprograms aimed at supporting students through scholarships, mentorship, and careerdevelopment. The Graduate Engineering Education Scholarship (GEES) of the University ofPittsburgh is one of the success cases of the NSF S-STEM (Track 2) initiative. The GEESprogram, launched 2019 by the University of Pittsburgh’s Swanson School of Engineering(SSoE), is an attempt to address the financial issues that low-income students face. There aretwo primary objectives: (1) to increase access to Master of Science (MS) degrees
’ comprehension of NLP, preparing them forfuture developments in the subject and developing the practical skills necessary for their jobs.Keywords: Natural Language Processing (NLP), Undergraduate Education, Interactive Tools, PythonLibraries, Interdisciplinary Case Studies.1 IntroductionThe rapid advancement of digital technology, especially in artificial i ntelligence ( AI), i s s ignificantly re-shaping the landscape of higher education. Traditional lecture-centered teaching is increasingly being sup-plemented by dynamic, technology-enhanced approaches. In today’s education, AI-powered platforms andvirtual learning environments have become essential, leading to a new emphasis on adaptable, personalizedlearning experiences that cater to diverse
analytical methods including natural languageprocessing (NLP) could enhance analysis accuracy and contribute to enhancing the overalldiverse and inclusive learning environment. Beyond these considerations, extending the analysisto include academic writing materials from additional years could provide a more comprehensiveview of how language practices evolve over time. This could offer deeper insights into theeffectiveness of initiatives focused on fostering inclusive language use. ReferencesAeby, P., Fong, R., Isaac, S., & Tormey, R. (2019). The impact of gender on engineering students’ group work experiences. International Journal of Engineering Education, 35(3), 756–765.Alfred, M. V., Ray
entire MLprocess, fostering computational thinking and problem-solving [18]. Kajiwara et al. employed agamified ML role-playing game, simplifying concepts for high school students [15]. Ethicalconsiderations were integrated through projects like VotestratesML, which explored AI's societalimpacts in democratic contexts [20], and Kong et al.’s collaborative projects addressing fairnessand bias in AI systems [16].3.5 Results for RQ4: Which of the AI4K12 Five Big Ideas frameworks are being included?The AI4K12 Five Big Ideas rubric assessed studies on Perception, Representation & Reasoning,Learning, Natural Interaction, and Societal Impact, scoring from 0 (not addressed) to 4 (thoroughintegration). Results highlighted strengths in Learning
method, even if the answer was incorrect, which indicates a strongemphasis on students’ ability to grasp and apply concepts:“If you show me the process that youhave done, and you do the right process and doing the problem. I will give you 90% of the creditirregardless of if you get the right answer or not.” Additionally, ID1’s grading system wasflexible, allowing for student redemption. According to ID1, poor performance on an initial testcould be offset by improvement on subsequent assessments. This flexibility might encouragecontinuous learning, as students were not penalized heavily for early mistakes and instead aregiven the opportunity to demonstrate growth over the course of the semester: “I make the courseso that hey, you flunk the first
disabled students.To broaden participation and increase diversity in engineering and computing majors in 4-yearuniversities and colleges, bridge and success programs (also called intervention programs in someliterature) such as summer bridge, engineering scholar, and bootcamp have been used to supportstudents’ college transition and retention [1-8]. Some were initially created with federal fundingsupport from U.S. National Science Foundation (NSF) Scholarships in Science, Engineering,Technology, and Mathematics Program (S-STEM) and Louis Stokes Alliances for MinorityParticipation Program (LSAMP) [9] and institutionalized later. Both S-STEM Scholars programand LSAMP Scholars program not only provide financial support to student participants but
supportive option for its students.References [1] B. Bygstad, E. Øvrelid, S. Ludvigsen, and M. Dæhlen, "From dual digitalization to digital learning space: Exploring the digital transformation of higher education," Computers & Education, vol. 182, p. 104463, 2022. [2] R. P. Goldenson, L. L. Avery, R. R. Gill, and S. M. Durfee, "The virtual homeroom: Utility and benefits of small group online learning in the COVID-19 era," Current Problems in Diagnostic Radiology, vol. 51, no. 2, pp. 152–154, 2022. [3] V. G. Padaguri and S. A. Pasha, "Synchronous online learning versus asynchronous online learning: A comparative analysis of learning effectiveness," in Proc. AUBH E-Learning Conf., 2021. [4] K. Baba, N
, Abdul Hamid et al. (2018) explored engagement prediction by manpower, including Healthcare, Construction, Entertainment, Computer Conference (EDUCON), Mar. 2022, doi: https://doi.org/10.1109/educon52537.2022.9766690. using AI-assisted facial expression detection. Their model used the Bag of • Ovidiu Andrei Schipor, S. G. Pentiuc, and M. D. Schipor
): Algorithm Details – do the authors name the machine learning method(s) used? Do they cite a quality paper for these method(s)? Do they discuss algorithmic settings? Example 1:“Linear discriminant analysis” has no algorithmic settings and means a specific function Example 2: “discriminant analysis” is unclear (i.e. there are many discriminant variants such as linear and quadratic) Example 3: Artificial neural networks have many settings (number of nodes, number of layers, types of nodes, training methods, architecture variant). All of these must be specified for repeatability Data Details – do the authors describe the source of the data or the collection means? Do they cite a source? Do they describe all data variables? Performance Result
Paper ID #45761A Gender-based Comparative Analysis of Motivations and Challenges in ConstructionEducationDr. Saeed Rokooei, Mississippi State University Saeed Rokooei is an associate professor in the Department of Building Construction Science at Mississippi State University. Dr. Rokooei’s primary research interests include community resilience, engineering education, simulation and serious games, project management methodologies, data analytics, creativity and innovation, and emerging technologies.Mr. George D Ford P.E., Mississippi State University Dr. George Ford P.E. is the Director of Mississippi Stateˆa C™s Building
Student with ADHD and a Reading Disability,” in Promoting Safe and Effective Transitions to College for Youth with Mental Health Conditions, A. Martel, J. Derenne, and P. K. Leebens, Eds. Cham: Springer International Publishing, 2018, pp. 95–102.[3] M. A. Zapata and F. C. Worrell, “Disability Acceptance and Affirmation Among U.S. Adults With Learning Disabilities and ADHD,” J. Learn. Disabil., vol. 57, no. 2, pp. 79–90, Mar. 2024.[4] S. Maul and R. Figard, “Diminishing the data divide: Interrogating the state of disability data collection and reporting,” presented at the American Society for Engineering Education 2024, Portland, OR, 2024.[5] Learning Disabilities Association of America, “ADHD – Affects focus, attention and
degree plan choices: A qualitative study with engineering and communication students," submitted to the International Communication Association's Annual Conference, 2025.6. E. L. Deci and R. M. Ryan, "Self-determination theory," in Handbook of Theories of Social Psychology, vol. 1, pp. 416-436, 2012.7. M. S. Eickholt, "The effect of superiors' mentoring on subordinates' organizational identification and workplace outcomes," Master’s Thesis, West Virginia University, 2018.8. K. Kricorian, M. Seu, D. Lopez, and others, "Factors influencing participation of underrepresented students in STEM fields: Matched mentors and mindsets," International Journal of STEM Education, vol. 7, no. 16, 2020.9. S. L. Kuchynka, A. E
Health, Volume 28, 2023, 100395, ISSN 2352-6483, doi: 10.1016/j.smhl.2023.100395 for successful implementation of AI in educational systems. P = (K × U)/2 (1) C = 5 - (D + R + S + L)/4 (2) educational initiatives aimed at increasing AI literacy could be effective in [6] Z. Xiong, C. Wang, Y. Li, Y. Luo and Y. Cao, "Swin-Pose: Swin Transformer Based Human Pose Estimation," 2022 IEEE where Where improving student perceptions. 5th
fields [26].Ultimately, the STEM workforce should reflect the population it serves. However, research bythe National Science Foundation finds “Hispanic, Black, and American Indian or Alaska Nativepersons collectively account for 37% of the U.S. population ages 18–34 years in 2021, and 26%of S&E bachelor’s, 24% of S&E master’s, and 16% of S&E doctoral degrees earned by U.S.citizens and permanent residents in 2020” [27]. In addition, women earned 51% of S&Ebachelor’s, 51% of S&E master’s, and 47% of S&E doctoral degrees in the U.S. in 2020, butdespite women’s high levels of representation in S&E (which includes the life sciences andsocial sciences), women of color earned only 14.9% of all S&E bachelor’s degrees [27
funding. I need to show that I am being active in seeking funding, I have to be actively applying for grant proposals and it's in my field it's mostly NSF. I need to have publications in peer reviewed journals [and] presentations. You know, all that stuff and then in service. All the etcetera like that that drawer at your home, where you just throw everything else. Everything else goes there. The junk drawer, that[‘s] service everything else. – Mila, Latina, Puerto Rican• Motives – a psychological feature that arouses a person to action to meet a specific goal. In terms of EM, this can be framed as achievement orientation, impact, and value creation.• Achievement orientation - A focus on setting and reaching goals, often
methodology. Table 2. Review of technologies being used in STEM education for SLWD.Author(s) Country Technologies Purpose Target Group Education Designedand Year Level Solution/MethodologyIatraki et al., Greece Virtual Investigate the design issues Intellectual Primary Employed a focus group(2021) [21] Reality/Augmented in the development of digital disability (ID) methodology to explore the Reality (VR/AR) learning environments for
The University of Texas at Arlington, Arlington, TX Copyright © 2025, American Society for Engineering Education 10 AcknowledgmentWe would like to acknowledge the Klesse College of Engineering and Integrated Design (KCEID)and the Office of Sustainability at The University of Texas at San Antonio (UTSA) for supportingthis project through the KCEID Incentive Opportunity Award. Any opinions, findings, conclusions,or recommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of UTSA. ReferencesAbioye, S. O., Oyedele, L
grant funding or industry partnerships.Dr. Kinnis Gosha, Morehouse College Dr. Kinnis Gosha (Go-Shay) is an Assistant Professor in the Department of Computer Science and Director of the Culturally Relevant Computer Lab at Morehouse College. Dr. Goshaˆa C™s research interests include conversational agents, social media data analytMrs. Talia Capozzoli Kessler, Georgia Institute of Technology Talia Kessler, MSPP is a research associate at The Center for Education Integrating Science, Mathematics, and Computing (CEISMC) at Georgia Tech. As a research associate, she works on research and evaluation projects centering on K-12 STEM education. She has a Master’s degree in Public Policy from the Georgia Tech and is currently
diverse earth science learners. Journal of Geoscience Education, 65(4), 407–415.2. Miller, A. J., Brennan, K. P., Mignani, C., Wieder, J., David, R. O., and Borduas-Dedekind, N. Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard. Atmospheric Measurement Techniques., 14, 3131−3151, 2021.3. Mahant, S., Yadav, S., Gilbert, C., Kjærgaard, E. R., Jensen, M. M., Kessler, T., Bilde, M., & Petters, M. D. (2023). An open-hardware community ice nucleation cold stage for research and teaching. HardwareX, 16.4. Hiranuma, N., Augustin-Bauditz, S., Bingemer, H., Budke, C., Curtius, J., Danielczok, A., Diehl, K
Depoliticization and Meritocracy Hinder Engineers’ Ability to Think About Social Injustices,” in Engineering Education for Social Justice: Critical Explorations and Opportunities, J. Lucena, Ed., Dordrecht: Springer Netherlands, 2013, pp. 67–84. doi: 10.1007/978-94-007-6350-0_4.[8] A. Jaiswal, G. Nanda, and M. Sapkota, “Building a Fairer Future: Integrating Social Justice in the Engineering Curriculum,” in 2024 IEEE Frontiers in Education Conference (FIE), Washington, DC, USA: IEEE, Oct. 2024.[9] S. L. Bem, “Gender schema theory: A cognitive account of sex typing,” Psychol. Rev., vol. 88, no. 4, pp. 354–364, 1981, doi: 10.1037/0033-295X.88.4.354.[10] S. J. Ceci and W. M. Williams, “Sex Differences in Math-Intensive Fields,” Curr. Dir
State University (Ph.D.).Ellen Wang Althaus, University of Illinois at Urbana - Champaign Ellen Wang Althaus, PhD (she/her) is a collaborative and innovative leader forging new initiatives and building alliances to foster diversity, equity, and inclusion (DEI) in science, technology, engineering, and mathematics (STEM) disciplines. In her current role as Assistant Dean for Strategic Diversity, Equity, and Inclusion Initiatives in the Grainger College of Engineering at the University of Illinois Urbana-Champaign she • Leads the strategy enhancing the Grainger College of Engineering (GCOE)’s commitment to diversity, equity, inclusion, and access. • Develops robust structures to support faculty and staff appropriately
-2933, 2018.[2] F. Jamil, "On the electricity shortage, price and electricity theft nexus," Energy Policy, pp. 267-272, 2013.[3] I. N. Kessides, "Chaos in power: Pakistan's electricity crisis.," Energy Policy, vol. 55, pp. 271-285, 2013.[4] A. Tanveer, "Non-technical loss analysis and prevention using smart meters," Renewable and Sustainable Energy Reviews, pp. 573-589, 2017.[5] T. Bihl and A. and Zobaa, "Data-mining methods for electricity theft detection.," in Big Data Analytics in Future Power Systems, CRC Press, 2018, pp. 107-124.[6] T. Abdelhamid, "Six Sigma in lean construction systems: opportunities and challenges," Proceedings of the 11th Annual Conference for Lean Construction, pp. 22-24, 2003.[7] T. J. Bihl and S
influenced by. Like individual socioeconomics,these characteristics reflect hierarchical social and economic ranking amongst people. Importantly,they reflect Keynes (1936) argument that socioeconomics are group mentalities that organizepeople’s positions amongst society. Keynes (1936) illustrated that individuals with similar incomeslive together (household) or near one another (neighborhood/school) and likely have a similaroccupation. Given these features, we consider the following relational socioeconomic factors:1. Family/household income, occupation, and education are representations of the total, combinatory income(s), prestige, or educational status of the household. Household socioeconomic status has also been inferred based on what
potential, respectively,from ATHENA. The current paper describes the implementation of the DACE process for the Summer2024 project, some findings, and the lesson plans developed by Zagozda to share more broadly to theASEE Community. MethodsAs described in Thomason et al.2, the DACE process provides an approach that middle/high schoolteachers can follow and translate to their classrooms. As a brief summary, DACE consists of thefollowing steps: 1. Calibration of the computer model(s) for the application of interest. 2. Design experiments to organize a set of computer model input parameter settings. 3. Execution of the computer model(s) to generate performance metric outputs. 4