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
phases, integrating industry/engineeringstandards at each design step, paying attention to health and safety of the public, maintainingethical standards, and proper documentation of the capstone design process must be criticalcomponents of any capstone design model. Missing or inadequacy of addressing those criticalcomponents may result in negative evaluation by ABET program evaluators (PEV s). Therefore,it is important for any engineering program to adopt a proper capstone model to satisfy ABETprogram assessment requirements.In view of these contexts, this paper discusses the capstone model used by the engineeringprogram at the Southern Arkansas University (SAU). The model has been developed to providean industry level design experience in
: Create an initial inventory of specific EdTech (e.g., AutoCAD), rather than broad EdTech categories (e.g., “CAD tools”). o Step 2. Define Educator Selection Criteria: Identify the factors relevant to educators when choosing which tool(s) to try among many potential options. o Step 3. Expand the EdTech Dataset: Gather or supplement data for each EdTech based on the selection criteria (established in Step 2 of Phase 1), ensuring all relevant attributes are captured. Augment the dataset to include comprehensive data for a set of tools (i.e., capturing all significant attributes for each EdTech) in at least one specific EdTech
a 28% improvement in persistence throughchallenging coursework.Lave and Wenger's (1991) Situated Learning Theory provides the third theoretical pillar, asemphasized in Brown et al.'s (2017) research showing how AI-supported authentic learningenvironments increased student engagement by 45% and improved transfer of theoreticalknowledge to practical applications by 38%. The integration of these theories creates a robustframework for understanding how AI tools can simultaneously reduce cognitive barriers, buildstudent confidence, and provide authentic learning experiences.Figure 1 illustrates the integration of these theoretical perspectives, demonstrating how theywork together to support comprehensive learning outcomes
. Craven, F. W., & Slatter, R. R. (1988). An overview of advanced manufacturing technology. Applied ergonomics, 19(1), 9-16. 3. Vichare, P., Nassehi, A., Flynn, J. M., & Newman, S. T. (2018). Through life machine tool capability modelling. Procedia Manufacturing, 16, 171-178. 4. Adeleke, A. K., Montero, D. J. P., Olu-lawal, K. A., & Olajiga, O. K. (2024). Statistical techniques in precision metrology, applications and best practices. Engineering Science & Technology Journal, 5(3), 888-900. 5. Hartikainen, S., Rintala, H., Pylväs, L., & Nokelainen, P. (2019). The concept of active learning and the measurement of learning outcomes: A review of research in engineering higher education
wildfires on the monitoring of said wildfires. 7Bibliography[1] H. An, J. Gan, and S. Cho, “Assessing Climate Change Impacts on Wildfire Risk in the United States,” Forests,vol. 6, no. 12, pp. 3197–3211, Sep. 2015, doi: 10.3390/f6093197.[2] A. Mohapatra and T. Trinh, “Early Wildfire Detection Technologies in Practice—A Review,” Sustainability, vol.14, no. 19, p. 12270, Sep. 2022, doi: 10.3390/su141912270.[3] J. D. Coop, S. A. Parks, S. R. McClernan, and L. M. Holsinger, “Influences of Prior Wildfires on VegetationResponse to Subsequent Fire in a Reburned Southwestern Landscape,” Ecological Applications, vol. 26, no. 2, pp.346–354, Mar. 2016, doi
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
. Table 2 summarizes the results from the forced convection experiments. Table 2. Forced convection results. Airspeed Airspeed Base Ambient Thermal Power Input, before fins after fins Temperature Temperature Resistance, Q [W] [m/s] [m/s] Tb [°C] T∞ [°C] R [K/W] 15.02 2 0.5 50.0 22.0 1.86 17.21 2.5 0.9 50.3 21.5 1.67 23.16 3 1.2 49.7 20.7 1.25 23.80 3.5
thedata. Anomaly scores are computed based on the number of splits required to isolate a data point. E(x)Mathematically, the anomaly score s(x) for a data point x is calculated as: s(x) = 2− c(n) , whereE(x) is the average path length for x, and c(n) is the average path length of a randomly selectedpoint in a sample of size n.Predictive Analytics: AI models like Long Short-Term Memory (LSTM) networks forecast futuresoil conditions. LSTMs are a type of recurrent neural network (RNN) that can learn long-termdependencies. The LSTM network uses gates to control the flow of information. The update rulefor the LSTM cell state Ct at time t is given by
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
Paper ID #49612Implementing a Flipped Learning Approach In Two Engineering CoursesDr. Lynn Dudash, University of Mount Union ©American Society for Engineering Education, 2025 WIP: Implementing a Flipped Learning Approach in Two Engineering CoursesIntroduction The flipped learning approach is an innovative teaching technique that has beenimplemented in many university level engineering courses over the past 15 years. Whileelements of the flipped teaching method have been used since the late 1990’s, two high schoolchemistry teachers, Jonathan Bergmann and Aaron Sams, are often credited
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
growth.In conclusion, introducing Lean Systems tools within the ENGR 1210 course proved to be avaluable approach for helping students analyze their challenges systematically. By continuing toimplement these problem-solving frameworks, alongside targeted support services, the universitycan better equip freshmen to succeed academically and personally.References[1] S. McKay, “Quality Improvement Approaches: Lean for Education,” Carnegie Foundation forthe Advancement of Teaching (Blog/Improvement in Action), 2017.[2] S. Ihsan and O. Khalifa, “Continuous Quality Improvement Strategies in EngineeringCurriculum,” Proceedings of 2nd International Conference on Professional Ethics and Educationin Engineering, Kuala Lumpur, Malaysia, 2011.[3] V. Narulaa, and
infrastructure,” Applied Energy, vol. 270, p. 115147, 2020.engaged educational institutions, industry partners, and labor [16] D. Gallachói, “The impact of NEVI program funding on job creation in the EV sector,” Sustainable Transportation Review, vol. 14, no. 1,unions in designing and implementing training initiatives are pp. 1–15, 2022.better positioned to meet the growing demand for EV [17] M. Brusaglino, F. S. Chen, and Y. Nakamura, “Skills and competenciestechnicians. However, many states continue to face for electric and hybrid vehicle maintenance,” Journal of
, formers,” IEEE Transactions on Geoscience and Re- pp. 1–4. mote Sensing, vol. 60, pp. 1–15, 2021. [5] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, [19] A. Jamali, S. K. Roy, D. Hong, B. Lu, and P. Ghamisi, “Learning internal representations by error propaga- “How to learn more? exploring kolmogorov–arnold tion, parallel distributed processing, explorations in the networks for hyperspectral image classification,” Re- microstructure of cognition, ed. de rumelhart and j. mote Sensing, vol. 16, no. 21, 2024, ISSN: 2072-4292. mcclelland. vol. 1. 1986,” Biometrika, vol. 71, no. 599- [Online
delay in Peres and URG-based reversible circuits reveals crucial trade-offs between energy efficiency and performance [7]. Peres-based circuits demonstrate lower power consumption and quicker (g) switching times making them well-suited for low-power applications [21]. TABLE 4: COMPARISON OF POWER, DELAY AND PDP POWER DELAY(s) PDP (W)Existing Work[1] 2.082e-05 3.015n 6.27e-24[SISO]Existing Work [1] 2.082e-05 12.34p 25.6e-17[SIPO]Existing Work [2] 2.259e-05 __ __[SISO
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
Sustainability at The University of Texas at San Antonio (UTSA) for supportingthis project through the KCEID Incentive Opportunity Award. We are also grateful to the EarthenConstruction Initiative (ECI) and the entire team for their support, particularly for providing thematerials used in this study. Any opinions, findings, conclusions, or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of UTSA. ReferencesAbraham, Y. S. (2020). Importance of Active Learning in an Undergraduate Course in Construction Scheduling. ASEE Virtual Conference.Arik, S., & Yilmaz, M. (2020). The Effect of Constructivist Learning Approach and Active Learning on
predict employee attrition,”support to employees regularly working overtime could Decis. Sci. Lett., vol. 13, no. 1, pp. 1–18, December 2024.effectively lower attrition risks. The key insights from LIME [2] E. A. Khan and S. M. H. Khan, “Factors affecting employee attritionand ICE highlight the importance of personalized retention and predictive modelling using IBM HR data,” J. Comput. Theor.strategies, allowing HR to address individual employee needs Nanosci., vol. 16, no. 8, pp. 3379–3383, January 2019.more effectively based on specific factors identified through [3] F. O. Usman, N. L. Ndubuisi, C. V. Ibeh, E. R. Daraojimba, C. A
learning: Knowledge-building andknowledge-telling in peer tutors’ explanations and questions. Review of Educational Research,77(4), 534–574.[2] Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal, 19(2), 237–248.[3] Chen, A., Wei, Y., Le, H., & Zhang, Y. (2024). Learning-by-teaching with ChatGPT: Theeffect of teachable ChatGPT agent on programming education. arXiv preprint arXiv:2412.15226.[4] Topping, K. J. (2005). Trends in peer learning. Educational Psychology, 25(6), 631–645.[5] Goodlad, S., & Hirst, B. (1989). Peer Tutoring: A guide to learning by teaching. London:Kogan Page.[6] Biswas, G., Leelawong, K., Schwartz, D., & Vye, N
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
-source materials so we may construct the activity ourselves.References[1] J. K. Perron, C. DeLeone, S. Sharif, T. Carter, J. M. Grossman, G. Passante, and J. Sack, “Quantum Undergraduate Education and Scientific Training,” 2021. [Online]. Available: https://arxiv.org/abs/2109.13850[2] A. Asfaw, A. Blais, K. R. Brown, J. Candelaria, C. Cantwell, L. D. Carr, J. Combes, D. M. Debroy, J. M. Donohue, S. E. Economou, E. Edwards, M. F. J. Fox, S. M. Girvin, A. Ho, H. M. Hurst, Z. Jacob, B. R. Johnson, E. Johnston-Halperin, R. Joynt, E. Kapit, J. Klein-Seetharaman, M. Laforest, H. J. Lewandowski, T. W. Lynn, C. R. H. McRae, C. Merzbacher, S. Michalakis, P. Narang, W. D. Oliver, J. Palsberg, D. P. Pappas, M. G. Raymer, D. J. Reilly, M
strategies toboost preparation and participation, thereby enhancing learning outcomes across engineeringsubjects.Due to the small sample size, it is challenging to make conclusive recommendations based on theobservations. The results of this study, limited to the data from 2022-2023, should not begeneralized to broader conclusions. Further data collection and analysis over several more courseofferings are necessary to draw informative conclusions. Future studies should encompassdifferent courses with larger sample sizes. Engineering faculty can create a more engaging andeffective learning environment in their courses by incorporating the strategy used in this study.References[1] Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N
. “The renaissance foundry: A powerful learning and thinking system to develop the 21st century engineer,” Critical Conversations in Higher Education, 1(2), 2015, 176-202. 6. V. Matthew, S. Lipkin-Moore, P. E. Arce, A. Arce-Trigatti, N. Lavoine, L. Lucia, E. Selvi, M. Eggermont, M. Tiryakioglu, J. Hall, R. Edelen, and J. Plumblee. “A Roadmap for the Design and Implementation of Communities of Practice for Faculty Development,” Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 2022, https://peer.asee.org/40564 7. K. Pabody, C. Wilson, A. Arce-Trigatti, P. E. Arce, S. H. Buer, A. Haynes, R. Chitiyo, J. R. Sanders, and T. Smith. “The Renaissance Foundry Model and culturally
Engineering Education,” 2022. Retrieved from https://engineeringforoneplanet.org/wp- content/uploads/2022_EOP_Framework_110922.pdf 4. P. E. Arce, J. R. Sanders, A. Arce-Trigatti, L. Loggins, J. Biernacki, M. Geist, J. Pascal, and K. Wiant. “The renaissance foundry: A powerful learning and thinking system to develop the 21st century engineer,” Critical Conversations in Higher Education, 1(2), 2015, 176-202. 5. V. Matthew, S. Lipkin-Moore, P. E. Arce, A. Arce-Trigatti, N. Lavoine, L. Lucia, E. Selvi, M. Eggermont, M. Tiryakioglu, J. Hall, R. Edelen, and J. Plumblee. “A Roadmap for the Design and Implementation of Communities of Practice for Faculty Development,” Paper presented at 2022 ASEE Annual Conference
+( ) 𝐸2 𝑁A confidence interval (𝑧) of 95% is chosen, yielding Z-values of 1.96, with a margin of error (𝐸)of 4%-8%, which corresponds to the 95% confidence interval [13]. The standard deviation (𝜎) isestimated to be 1, given the small population size. After n surveys have been completed, thesample standard deviation (s) will be calculated and a confidence interval for σ will bedetermined to ensure that the correct number of samples are take. N is the total population size ofall the students exposed to the flipped classroom with alternate instructors. Three statisticalmethods of means, materiality, statistical relations, and Cronbach’s Alpha, illustrated in Equation(2), will be used to analyze and understand the results of the survey
evaluations ofteaching, course surveys, or simply teaching evaluations) have been used for assessing teachers’effectiveness in one form or another since the 1920’s. In many cases, though, modernassessment has relied far too heavily on student opinions as though it were a comprehensiveassessment of teaching effectiveness and student learning [2], when in fact, there are numerousapproaches to evaluate teaching more holistically. Other common strategies for teachingevaluation include peer observation (by fellow faculty members), external review (often byexperienced teaching and learning professionals), and self-evaluation. In each case, modernapproaches center on evidence-based evaluation practices [3], and several examples arediscussed herein.The
Proteins and DNA are Poorly Correlated”, Mol Biol Evol. 2023 Apr 4;40 (4).with the student population. There are about 5 physics majors, [2] L. Teekas, S. Sharma, and N. Vijay, “Terminal regions of a protein are a50 engineering majors, and 500 ET majors in which 20 of them hotspot for low complexity regions and selection”, Open Biol. 2024are considering transition to engineering. None of the Jun;14(6):230439.participants in this report is majoring in physics. In fact, all the [3] B. Leung, unpublished data, Year of 2025 Great Neck