2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. The Effectiveness of Periodic Workshops as part of an NSF S-STEM Support Ecosystem Mohamad Musavi Karissa Tilbury Department of Electrical and Computer Engineering Department of Chemical and Biomedical Engineering University of Maine University of Maine Orono, ME Orono, ME musavi@maine.edu
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
2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgeport, CT, USA.Investigating the Thermal and Mechanical Properties of Hempcrete Through Practical Experiments Glenda R.S. Giordani Rachmadian Wulandana Felipe S. Oliveira Mechanical Engineering Program Instituto de Física Gleb Wataghin Mechanical Engineering Program SUNY New Paltz UNICAMP SUNY New Paltz New Paltz, NY, USA Campinas, SP, Brazil
-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 #49549Visualizing and Identifying Patterns of Student Flow Through UndergraduateEngineering ProgramsDr. Bonnie S. Boardman, The University of Texas at Arlington Bonnie Boardman is the Undergraduate Program Director and a Professor of Instruction in the Industrial and Manufacturing Systems Engineering Department at The University of Texas at Arlington. Her primary research interests are in the engineering education and resource planning disciplines. ©American Society for Engineering Education, 2025 1
Paper ID #49518Engaging Undergraduate Students in Solving Real Roadway Problems at theCampus of the Islamic University of MadinahDr. Aiman S Kuzmar, Islamic University of Madinah, Saudi Arabia Dr. Aiman Kuzmar, P. E. has a 1994 Ph. D. from Duke University. He has a 1987 Master’s from Rice University. His 1984 BS degree is from King Fahd University of Petroleum and Minerals in Dhahran, Saudi Arabia. All of his degrees are in Civil Engineering. Dr. Kuzmar is a licensed Professional Engineer (P. E.) with the State of North Carolina since 1999. He is also a licensed Registered Engineer with the Jordanian Association of
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
Paper ID #45650Relating Kinetic Energy Changes to Power Generation in a Mechanical EngineeringWind Turbine LabDr. Chuck H. Margraves, University of Tennessee at Chattanooga Dr. Chuck Margraves is a UC Foundation Associate Professor and Graduate Coordinator of Mechanical Engineering at the University of Tennessee at Chattanooga. His current research focus is on STEM Education, particularly in the area of energy sustainability, at the collegiate and high school levels.Prof. KIDAMBI SREENIVAS, University of Tennessee at ChattanoogaTrevor S. Elliott, University of Tennessee at ChattanoogaLance Isaac Rose, University of Tennessee at
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
system’s accuracy and overall reliability,mathematically as: according to [6]. Single-modality interventions using VNS treatment alone do not meet all patient requirements because complex seizure patterns show diverse changes in intensity S(t) = σ(Wc · CN N (x(t)) + Wl · LST M (y(t))) (1) together with frequency [7] and [8]. Where: Recent advancements have focused on integrating machine • S(t) represents the seizure detection output at time t
, certificates in Organizational Leadership and Technical Project Management, and a Bachelor of Science in Business Administration from Strayer University.Dr. Andrew B. Williams, The Citadel Andrew B. Williams, Ph.D. is the Dean of Engineering and the Louis S. LeTellier Chair at The Citadel School of Engineering. Dr. Williams is an alumni of the National Academy of Engineering Frontiers in Engineering Symposium and the National GEM Consortium Ph.D. in Engineering Program. He received both his Ph.D. in Electrical Engineering with an emphasis in AI and his BSEE from the University of Kansas.Dr. Kevin Skenes, The Citadel Kevin Skenes is an associate professor at The Citadel. His research interests include non-destructive
Manag. Stud. Entrepreneurship J. (MSEJ), vol. 4, no. 6, pp. 9481–9493,mitigate these challenges through effective sampling strategies 2023.and rigorous data validation. [10] S. Chatterjee and K. Banerjee, “Impact of social media in women entrepreneurship–unlocking potentials for business success,” J. Mines, Met. Fuels, vol. 71, no. 5, 2023. IV. CONCLUSION [11] B. D. Metcalfe, B. L. Bastian, and H. Al-Dajani, Eds., Women
Knowledge This work was funded through the School of Engineering and Critical Thinking Skills in Code Blue Managementand Computing at Fairfield University and the Sapre Aude Among Undergraduate Nursing Students in Malaysia,”Fund. We would also like to thank the Egan School of Nursing Sage Open, vol. 11, no. 2, p. 21582440211007123, Apr.for their collaboration on this project. 2021, doi: 10.1177/21582440211007123. [12] M. Azizi, G. Ramezani, E. Karimi, A. A. Hayat, S. A. REFERENCES Faghihi, and M. H. Keshavarzi, “A comparison of the[1
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
b since using “Add Trendline” cannot Table 1: Record the time for specific heights of the water during an experiment Time (s) Height (cm) 12 11 10 9 8 7 6 5 4 3
based technologies, biological transport and moreover crucial for understanding the behavior of water in confined nanopores. V. References [1] S. Yesudasan, “Extended MARTINI water model for Fig. 4: This graph represents the calculation of water using the heat transfer studies,” Molecular Physics, vol. 118, SPC/E model with different diameters. no. 13, p. e1692151, Jul. 2020, doi
’ 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
became a possibility formaintaining modern comfort while causing minimal environmental harm. People continuelooking for ways to maintain modern comforts as new ideas are investigated, includingintegrating renewable energies with vehicles for a more sustainable form of transportation.Literature ReviewAutomobile evolution began as horse drawn carriages retrofitted with steam, gasoline, andelectric propulsion. Over time, technological advancements saw the creation of modern-dayautomobiles with gasoline and diesel rising as the main energy source. However, the burning offossil fuels began having noticeable negative environmental impacts by the 1970’s, sparking adebate for change to alternative forms of energy. People hoped to create a purely
-series statistical overview of the ARIMA approach and recurrent neural net-works (RNNs), specifically long-short-term memory (LSTM) TABLE Imodels are as follows. P ROJECTED G ROWTH R ATE S CENARIOS (OVERALL USA)A. Building Our Model: ARIMA and LSTM Growth Rate Type Value To create the ARIMA model, we first specify an (p, d, q) Average Growth Rate 0.0345 (3.45%)configuration and fit it to the enrollment data.The tuple (5,1,0) Maximum Growth Rate (Optimistic) 0.1146 (11.46%)represents
, 2019. [2] A. Osta and K. D. Dahm, “Work in progress: Integrating entrepreneurial mind-set within undergraduate engineering course projects,” in 2019 ASEE Annual Conference & Exposition, 2019. [3] E. Davishahl, T. A. Vannelli, M. J. Babcock, and D. Hanley, “The seecrs scholar academy at whatcom community college: Three cohorts of s-stem scholarships later,” in 2021 ASEE Virtual Annual Conference Content Access, 2021. [4] M. E. Van Den Bogaard, D. Reeping, C. Finelli, and J. Millunchick, “Student experiences with the online learning environment during covid,” in 2022 ASEE Annual Conference & Exposition, 2022. [5] M. Mosleh, P. Chandran, A. P. Maclin, J. Harkless, C. J. Robinson, H. Salmani, S. T. Smith, G. Washington
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
. Journal of Manufacturing and Materials Processing, 9(1), 16.[2].Mokhtar, W. A., & Nasir, S. B. (2024, March). Effects of injection molding processparameters on the mechanical properties of ABS and PP polymer. In 2024 ASEE North CentralSection Conference.[3].Ahmed, T., Sharma, P., Karmaker, C. L., & Nasir, S. (2022). Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm. Materials Today: Proceedings, 50,565-569.[4]. Nasir, S. B., & Mokhtar, W. (2024). Effects of Injection Molding Process Parameters on theMechanical Properties of ABS and PP Polymer.[5]. Tranter, J. B., Refalo, P., & Rochman, A. (2017). Towards sustainable injection molding ofABS plastic products. Journal of Manufacturing Processes
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
Baseline and Study Group. Summary and ConclusionsShort class interventions do not consume a lot of class time but their impact on student learningoutcome in the Materials and Manufacturing Selection in Design course were measured and showeda statistically significant improvement with more than 95% confidence. Students’ engagement with ahands-on experience helped students understand hard concepts of cold working, annealing,temperature, and time and their impact on the physical material behavior. References1. Balawi, S., and Pharr, M. (2024, March), Experiential Learning Utilizing Class and Lab Demos in a Material Science and Manufacturing Course Paper
imagesaffect the performance of SR models, making it difficult to but also the extraction of valuable data, as discussed by Islamextract accurate information. Data augmentation is a key strategy et al. [3], indicating how much SR performance underwaterto address these issues, involving deliberate adjustments to a can be impacted by these distortions.dataset to improve its diversity. Such adjustments include imagerotation, flipping, s caling, b rightness, c ontrast, a nd saturation. Data augmentation (DA) has been proven as a successfulData augmentation plays a significant r ole, e specially i n deep solution to these challenges. In other words, data augmentationlearning applications with sparse training data
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