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
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
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
): 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
-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
, the research team has also gathered quantitative data related to how the studentsengage with campus resources and personnel, as well as data on the character and composition ofthe students’ social support networks.Program descriptionThe SEED program was initiated in 2021 with support of the NSF S-STEM program which hasthe goal of recruiting and retaining financially-needy, academically-talented students to STEMcareers. The SEED program is open to students majoring in computer science or an engineeringdiscipline and the financial need requirement is satisfied by eligibility for the federal Pell grant.While not a requirement, students from backgrounds historically underrepresented in STEM areactively recruited to the program. Cohorts of
, longitudinalassessments of participants will be conducted over the next few years, as the program is ongoingand expected to continue until 2025, providing deeper insights into its effectiveness. The authorsalso plan to evaluate the impact of various program components on student outcomes and comparethe program's effectiveness with similar initiatives at other institutions, ensuring continuousimprovement and a broader understanding of its success.ReferencesAlaee, D. and Zwickl, B. (2021). A Case Study Approach To Understanding A Remote UndergraduateResearch Program., 480-485. https://doi.org/10.1119/perc.2021.pr.zohrabi_alaeeBosman, L., Soto, E., Ostanek, J., Garcia-Bravo, J., Lee, S., & Leon-Salas, W. (2023). Nsf ReuEntrepreneurially Minded Applied Energy
-367. Retrieved from https://magnascientiapub.com/journals/msarr/content/impact-robotics-clubs-k-12-students- interest-stem-careersBalgopal, M. M. (2020). STEM teacher agency: A case study of initiating and implementing curricular reform. Science Education, 762-785. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/sce.21578Ching, Y.-H., Yang, D., Wang, S., Baek, Y., Swanson, S., & Chittoori, B. (2019). Elementary school student development of STEM attitudes and perceived learning in a STEM integrated robotics curriculum. TechTrends, 63(1), 590-601. Retrieved from https://link.springer.com/article/10.1007/s11528-019-00388-0Mabli, J., Bleeker, M., Fox, M. K., Jean-Louis, B., &
-gemini- claude-meta-ai-which-is-the-best-ai-assistant-we-put-them-to-the-test [4] Vox, “Deepseek is bad for silicon valley. but it might be great for you.” 2025, accessed: Feb. 2, 2025. [Online]. Available: https://www.vox.com/technology/397330/deepseek-openai- chatgpt-gemini-nvidia-china [5] D. A. R. Team, “Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,” arXiv, 2025, https://arxiv.org/abs/2501.12948. [6] L. S. Maia and G. A.ˆ B. Lima, “A semantic-relations taxonomy for knowledge representa- tion,” Brazilian Journal of Information Science, no. 15, p. 23, 2021. [7] Z. Wang, S. Peng, J. Chen, X. Zhang, and H. Chen, “Icad-mi: Interdisciplinary concept association discovery from the
education and many low-income families who cannot afford for theirchildren to attend expensive or distant opportunities. To offer the local families an opportunityfor their children to attend camps and gather invaluable STEM-focused content and skills, it isimportant to obtain funding for camps to ensure students may attend. This paper will focus onthree camps: (1) Camp STEM, a camp open to any incoming 9-12th graders, (2) Gaining EarlyAwareness and Readiness for Undergraduate Programs (GEAR UP) in southern West Virginia(SWV) middle school STEM camp, and (3) a Health Science and Technology Academy (HSTA)Summer Institute for 10th graders. Camp STEM has successfully been run at WVU Tech sincethe mid-2000’s. In 2024, GEAR UP SWV middle school STEM
versatility and performance in Internet of Things (IoT)applications. The ESP32 offers significant computational power while maintaining an efficientenergy footprint. Its onboard Wi-Fi and Bluetooth modules provide seamless wirelesscommunication, making it an ideal choice for IoT applications. The ESP32’s Wi-Fi capabilitiesare a cornerstone of its functionality, supporting both 2.4 GHz frequency bands and a variety ofstandard protocols, including IEEE 802.11 b/g/n. This allows for high data transfer rates,efficient packet handling, and reliable connections, even in environments with significantinterference. Additionally, the ESP32 is equipped with advanced features such as Wi-Fi Directand mesh networking, enabling scalable deployments in complex IoT
Protection”, Manufacturing PA Innovation Program Expo, Harrisburg, PA, April 4, 2024. Presented poster and attended conference. 4. Michael, R.J., Piovesan, D., Gee, D., “Undergraduate Engineering Design Projects that Involve Inter- Departmental Collaboration,” Proc. ASEE-NCS 2020 Conference, West Virginia University, Morgantown, WV, Mar. 27 – 28, 2020. 5. Michael, R.J. and Piovesan, D., “Use of Engineering Software Programs for Self-Directed Learning,” Acad. Process Educators 2018 Conference, Gannon University, Erie, PA, June 2018. 6. Pollino, M., Sabzehzar, S., Michael, R., “Mechanical Behavior of Base Isolated Steel Storage Racks Designed for Sliding-Rocking Response,” Eleventh U.S
decided that cold air wouldbe a better option to be noticeable in all conditions and clothing types. To achieve this, a vortextube is used to convert the compressed air into cold air. For the air to be noticeable from at least 6feet away, the ideal velocity out of the nozzle needs to be 1.5-2 m/s. Because of this, a 100 SCFMEXAIR vortex tube was used for this design. The breech simulation already uses an air compressorfor the other operations, so a branch hose will be taken off the compressor to provide air for thevortex tube.With the original visual recoil system already finished, the team decided that for not only ease ofuse but also ease of manufacturing, the ring would be modified to fit the vortex tube and compo-nents needed
credits for students of color andPell-eligible students as they are more likely to transfer credits or change majors. AV E R A G E E A R N E D C R E D I T S BY C O U R S E L E V E L Student of Color Not Student of Color Pell Eligible Not Pell Eligible Transfer Non-Transfer 55.0 49.5 50.0 44.2 44.4 45.0 41.9 40.7 39.3 39.1 38.0 37.2 38.4
we are constructing the dimensions of the column. Using less material will decrease the overall cost of constructing the column(s) while maintaining a strength that conforms to the necessary specifications. While other materials, especially the A36 Steel, are comparable in price considering the material that would be needed, high-strength concrete maintains a good balance between GWP, cost, strength, and amount of material necessary to withstand the applied load.Responses, such as above, demonstrates that the student team gained an understanding of thetrade-offs in material selection by considering multiple factors (GWP, cost, and materialproperties). They use data-based reasoning to justify their choice
the risk of flooding and improving water quality. native plants, rain gardens, trees and green roofs. Acquisition Students will know… Students will be skilled at… how BMP solutions can satisfy the design Quantifying BMP solutions and their cost. requirements. Stage 2 - Evidence Evaluative Criteria Assessment Evidence Completed Site plan PERFORMANCE TASK(S
techniques.References1. USC Center for Excellence in Teaching: CET Classroom teaching observation checklist2. Robin K. Morgan: Exploring the Pedagogical Effectiveness of Clickers; InSight -A Journal of Scholarly Teaching (2008)3. Yljing Stehle: Integrity Independent Lab into Project: a Modification Made to the Materials Science Lab Curriculum; American Society for Engineering Education, 2024 Conference4. K. Smith, S. Sheppard, D. Johnson, R. Johnson: Pedagogies of Engagement: Classroom-Based Practices, Journal of Engineering Education, 1/20055. https://www.physicsforums.com/threads/ : Students Engaged In Active Learning Think They Learn Less, 12/25/20246. Ashley Mowreader: Why Students Recommend College Professors to Peers
Production, vol 18, pp. 275-284, 2010 7. https://www.iso.org/sectors/environment/climate-change 8. Rodriguez J. “An elective course in Green Chemical Engineering and Sustainability.” 2024 ASEE North Central Section Conference, 2024 9. The open source Life Cycle and Sustainability Assessment software https://www.openlca.org/ 10. https://www.epa.gov/ghgemissions/carbon-footprint-calculator 11. Bielefeldt A., T. and S. Wilkinson. “Introducing and stimulating sustainable engineering in first-year civil engineering students.” ASEE Annual Conference and Exposition. pp. 14.800.1-14.800.15, 2009 12. Kagawa, F. “Dissonance in students’ perceptions of sustainable development and sustainability.” International