, doi: 10.1002/tea.21341.[7] M. Estrada et al., “Improving Underrepresented Minority Student Persistence in STEM,” LSE, vol. 15, no. 3, p. es5, Sep. 2016, doi: 10.1187/cbe.16-01-0038.[8] S. H. Russell, M. P. Hancock, and J. McCullough, “THE PIPELINE: Benefits of Undergraduate Research Experiences,” Science, vol. 316, no. 5824, pp. 548–549, Apr. 2007, doi: 10.1126/science.1140384.[9] A. Godwin, G. Potvin, Z. Hazari, and R. Lock, “Identity, Critical Agency, and Engineering: An Affective Model for Predicting Engineering as a Career Choice: Identity, Critical Agency, and Engineering Careers,” J. Eng. Educ., vol. 105, no. 2, pp. 312–340, Apr. 2016, doi: 10.1002/jee.20118.[10] Verdin, Dina, “Enacting Agency: Understanding How First
lucky in that I didn’t feelany external comments about it.” Moreover, she shared that tasks were evenly split betweenmembers of a mixed-gender team. Similarly, A032 said she never “got bullied” or “got spokendown to” because of her gender. As a comment, A003 described the environment in hercompany as “everything was kept really neutral” because “we really don’t discriminate againstanyone”.However, gender “had more of a role” in a negative way in A016’s first two internships at thesame company. She faced gender stereotypes in multiple ways, leading her to “almost shy awayfrom wanting to pursue an engineering field”. The engineering team at her company (an oilrefinery) managed contract workers who performed manual labor at the refinery. The
Mathematics Program (S-STEM) program supports institutions of higher education to fundscholarships for academically talented low-income students and to study and implement aprogram of activities that support their recruitment, retention and graduation in STEM.Scholarships in STEM Network (S-STEM-Net) program supports both the creation of a resourceand evaluation center for the national S-STEM community and research hubs to study theconditions for the success of low-income undergraduate and graduate STEM students.Programs from the division of graduate education (DGE) [10]There are four DGE programs that are related to engineering education. The goal of the NSFResearch Traineeship program (NRT) is to encourage the development and implementation ofnew
content creationwas guided by the following state computer science standard and their associated core practices:6-8. DI.2, 6-8. DI., 6-8. DI.4, and 6-8.CD.4 [7]. With all the content made, the final step was to assemble the entire curriculum package.To do this, we followed the design-based research methodology [8]. In the end, what we had wasa middle school quantum infused curriculum that took a pre-existing unit on radioactive decayand added on coverage of quantum randomness. The major activities of our unit were: a labexploring carbon dating aimed at establishing an understanding of half-life via a penny tossexperiment and understanding quantum randomness by creating randomly colored artwork basedon a real time data output of 0’s and 1’s
Processing, 3rd Edition, Prentice Hall Signal Processing Series, 2009.[3] F. C. Berry, P. S. DiPiazza, and S. L. Sauer. "The future of electrical and computer engineering education," IEEETrans. on Education, vol. 46, no. 4, pp. 467-476, Nov. 2003.[4] S. Kuyath, "How Computer Animations Make Teaching Complex Topics More Effective And More Efficient, "in Proc. 2002 ASEE Annual Conference, Montreal, Canada, June, 2002.[5] K. K. Parhi, "Teaching Digital Signal Processing by Partial Flipping, Active Learning, and Visualization:Keeping Students Engaged With Blended Teaching," in IEEE Signal Processing Magazine, vol. 38, no. 3, pp. 20-29,May 2021.[6] “Unity Real-Time Development Platform | 3D, 2D, VR & AR Engine,” unity.com. https://unity.com
value ofthe recitation, and confidence in asking a professor or peer for help. This analysis will includelinear regression modeling similar to the analyses contained in the current paper to compare passrates and course grades. We also plan to further study the differences between FGCS andnon-FGCS who have participated in the PLSG model now that it has been fully implemented inthe course. This will include, but not be limited to, statistical analysis of interaction levels. ACKNOWLEDGEMENTThis material is also based upon work supported by the National Science Foundation under GrantNo. 2205001. REFERENCES[1] S. Weisen, T. Do, M. C. Peczuh, A. S. Hufnagle, and G. Maruyama
, identifyingcomparative strengths and areas for improvement in learning across the curriculum at present. Toenable this comparison, we establish a thresholding convention shown on the graphs in Figure 2.While Figure 1 presents the factor (category) means, Figure 2’s graphs instead plot the means forall 29 individual items. The data in each of Figure 2’s eight graphs are identical, yet each separatelyhighlights the items composing a different one from among the factors. All of Figure 2’s graphsalso contain an identical triangular shaded region. With its hypotenuse matching the slope of thelinear best-fit line for the set of 29 mean self-efficacy scores, this shaded region is set to encompassthe bottom 20% of items in terms of growth (relative to outgoing self
, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7[2] Matcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226 - 245. https://doi.org/10.1109/TLT.2019.2916802[3] Schwendimann, B. A., Rodríguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning
experiences with theirmentor. Examples of interview questions include, “Please tell me a little bit about yourexperience with your mentor.”, “How long has your mentor been mentoring you?”, “How didyou get connected with this mentor?”, and “Please tell me about an encounter with your mentorwhere you sought out their help or assistance.” These questions helped participants to describe atype of mentorship experience they want and those they actually have with their currentmentor(s). While answering these questions, participants used their drawings to reflect on theirexperience with mentors.We employed inductive qualitative data analysis to gain a preliminary understanding of the data[13]. Interview transcripts were primarily used to interpret
assignments.And attendance was high, with only a few students missing during each class period. In addition,all students successfully built a kalimba and an electronic piano.Students were surveyed prior to and after the mini-course to get feedback on their interests,background, and confidence. When asked to list the major(s) that they were considering, 44 ofthe 81 students (~54%) listed a STEM major, and 14 students listed engineering, specifically. Onboth the pre- and post-course surveys, students were asked to rank their confidence on differentactivities from breadboarding to succeeding in an engineering course using a scale from 1 (noconfidence) to 7 (full confidence). Pre/post responses for several of the questions asked are listedin Table 2. While
previous list. For each system list the following information: What type of system is it? (Mechanical, Electrical, Fluid/Thermal, Combinations?) If you were to model the system, what would the dynamic variable(s) be? What kind of physical quantities would you need to look up/solve for to model this system? • Talk as a group about everyone’s ideas. Share the story (as much or as little as you are comfortable with) surrounding the system you are interested in modeling. Start FBD’s of the systems you are leaning towards as a group to make sure you can model it.After completing the brainstorming activity, groups were instructed to use the generated ideas tochoose a project and then write a project proposal, which was due
pathways based upon established frameworks.Traditional curriculum plans, such as those from institutions designated by the NCAE-C incybersecurity defense education (CAE-CD) and cyber operations (CAE-CO) designation tracks,involve a manual process of identifying knowledge units for specific courses. Throughout themapping process, gaps are identified in curriculum plans based on the knowledge of the subjectmatter expert(s) (SME). Performing this task for one framework is challenging enough; considerthe increased complexity and risk of error when multiple frameworks are cross-referenced intothe plan. Improvement opportunities exist in the curriculum mapping and gap analysis process.This leads to the question of whether an LLM can speed up the
each category andexample(s) in the sections below. Table 1: Matrix of Origins, Identities and Trajectories. Origins Identities Trajectories Gender Identity Tinkerers & Builders Security & Social Mobility Race / Ethnic Identity Math-Science Mavens Enjoying Life Social Class Creative Engineers Good Job with General Area of Focus Citizenship Social Engineers Prestigious Job Connections to Engineers “Big Picture” Thinkers
). LGBTQ Inequality in Engineering Education. Journal of Engineering Education, 107(4), 583–610. https://doi.org/10.1002/jee.20239[2] Hughes, B. E. (2018). Coming out in STEM: Factors affecting retention of sexual minority STEM students. Science Advances, 4(3), eaao6373. https://doi.org/10.1126/sciadv.aao6373[3] Hughes, B. E. (2017). “Managing by Not Managing”: How Gay Engineering Students Manage Sexual Orientation Identity. Journal of College Student Development, 58(3), 385– 401. https://doi.org/10.1353/csd.2017.0029[4] Haverkamp, A., Butler, A., Pelzl, N. S., Bothwell, M. K., Montfort, D., & Driskill, Q. (2019). Exploring Transgender and Gender Nonconforming Engineering Undergraduate Experiences through
“hypercompetitiveness” of engineering is a barrier for the development of Latiné students’ senseof belonging in their engineering major at a HSI [18]. This is compounded by systemiceducational inequities faced by the Latiné population as they progress through their K-12education [4]. Furthermore, students in Esquinca et al.’s study felt as though negativepedagogical practices (e.g., instructors making courses extremely difficult to weed out students[22]) also inhibit students’ sense of belonging [18]. Our analysis demonstrates that the corollaryto this statement appears valid as well: more inclusive and accessible teaching practices helpstudents’ sense of belonging grow. Finally, Latinas and non-binary Latiné students experiencethe “double bind” of not only
ability to succeed inengineering tasks, is a crucial predictor of whether students remain engaged in engineeringeducation or pursue engineering as a college major. This is especially critical in rural settings,where access to engineering education or career development opportunities may be limited. Toaddress this, the mixed methods study implemented a 3D printing experience centered on engagingstudents in hands-on making and tinkering activities. The quantitative component employed adesign one-group pre- and post-test design using a modified version of Mamaril et al.’s (2016)engineering self-efficacy survey to assess students’ self-efficacy levels before and after theirparticipation in the 3D printing activities. The qualitative inquiry focused
employers are looking for on graduates’ resumes," NationalAssociation of Colleges and Employers (NACE), Jan. 16, 2024. [Online].[3] J. L. Graves Jr., M. Kearney, G. Barabino, and S. Malcom, "Inequality in science and the casefor a new agenda," Proceedings of the National Academy of Sciences, vol. 119, no. 10, articlee2117831119, 2022.[4] V. Tiberius, "In defense of reflection," Philosophical Issues, vol. 23, pp. 223-243, 2013.[5] B. Jacoby, Service-Learning Essentials: Questions, Answers, and Lessons Learned. SanFrancisco, CA: John Wiley & Sons, 2015.[6] B. Brown, Dare to Lead: Brave Work, Tough Conversations, Whole Hearts. New York:Random House, 2018.[7] R. Jarvis, K. Dempsey, G. Gutierrez, D. Lewis, K. Rouleau, and B. J. Stone, Peer
and ensure that learners are equipped to realize their full potential.AcknowledgmentThis material is based upon work supported by the National Science Foundation under Grant No.2110769 under the Improving Undergraduate STEM Education (IUSE) program at Level 2. Anyopinions, findings, conclusions, or recommendations expressed in this material are those of theauthor(s) and do not necessarily reflect the views of the National Science Foundation.References[1] P. Rivera-Reyes and L. C. Perez, “Abstraction and problem solving in an undergraduate electrical engineering circuits course,” in 2016 IEEE Frontiers in Education Conference (FIE), 2016, pp. 1–7.[2] B. J. Zimmerman, and M. Campillo, "Motivating self-regulated problem solvers
are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References[1] S. Al-Sarawi, M. Anbar, R. Abdullah, and A. B. Al Hawari, “Internet of things market analysis forecasts, 2020-2030,” in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 2020, pp. 449–453.[2] H. Gurocak, X. Zhao, and K. Lesseig, “Preparing mechanical engineering students for industry 4.0: an internet of things course.” ASEE Annual Conference and Exposition, 2024.[3] ——, “Infusing internet of things into mechatronics to train mechanical engineering students for smart product design.” ASEE Annual Conference and Exposition, 2025.[4] M. KentBeck, A. Beedle, A. Bennekum, W
educationinitiatives target increasingly younger audiences, facilitating an early and smooth transition fromblocks to text becomes particularly important.While Scratch excels at nurturing computational thinking and creative skills, it was not designedto facilitate the transition to text-based programming. The relationship between block-based andtext-based environments remains an active area of research [9], [10], [11], [12], [13], with K¨ollinget al. identifying specific barriers in transitioning between the two [14].Key Transition ChallengesBuilding on K¨olling et al.’s framework for analyzing block-to-text transitions, we examine severalkey challenges specifically in the context of moving from Scratch to text-based programming.First, while Scratch provides
datavisualizations of summary statistics using Duquia et al.’s best practices. Standard texts forpublishing in the academic literature such as The Craft of Research [21] and Gastel and Day’sHow to Write and Publish a Scientific Paper [22] offer sound instruction on communicatingvisual evidence and designing effective tables and graphs, but are both meant for researcherswho have completed their data collection and analysis and need advice on clarifying andpolishing their visualizations for a research audience.Advanced theory and multiple types of instructional material are readily available to guide datastorytelling, i.e., the creation of sophisticated and aesthetically beautiful visualizations of large,complex datasets using powerful open source (e.g., R
professor at the Department of Computer Science at Central Connecticut State University. He earned his PhD from the Center for Advanced Computer Studies of the University of Louisiana in 1999. Results of his doctoral research have been applied to network planning and industrial simulation. Dr. Kurkovsky served and continues to serve as a PI on a number of NSF-sponsored projects, including four S-STEM grants, three IUSE grants, and an REU Site grant. He also received funding from NIH, NSA, and ACM. He has an established record of over 100 peer-reviewed publications in the areas of software engineering, mobile computing, and computer science education.Nathan Sommer, Xavier University Nathan Sommer has taught software
," Pew Research Center, Washington, D.C., 2016.[2] K.-y. Wong and C. K. Yip, "Education, economic growth, and brain drain," Journal of Economic Dynamics and Control, vol. 23, no. 5-6, pp. 699-726, 1999.[3] L. Cancado, J. Reisel, and C. Walker, "Impacts of a Summer Bridge Program in Engineering on Student Retention and Graduation," Journal of STEM Education, vol. 19, no. 2, 2018.[4] D. Wood, A. Gura, J. Brockman, G. Gilot, S. Boukdad, and M. Krug, "The Community-Engaged Educational Ecosystem Model: Learning from the Bowman Creek Experience," presented at the Engaged Scholarship Consortium, Minneapolis, MN, 2018.[5] D. Wood, A. Gura, J. Brockman, A. Rayna Carolan-Silva, S. Boukdad, and J. C
ScienceFoundation grant #2141674. Their support is greatly appreciated. W. Zhu acknowledges thefunding from the University of Houston Advanced Manufacturing Institute, University ofHouston Division of Research, the support from National Science Foundation grant #1855147,#1950036, #2141674, US Department of Education grant P116S230007, and US Department ofAgriculture grant #13111855, #13424031, and National Academy of Science grant #200011064to the University of Houston.ReferencesBuehler, E., Comrie, N., Hofmann, M., McDonald, S., & Hurst, A. (2016). Investigating the implications of 3D printing in special education. ACM Transactions on Accessible Computing, 8(3), 1-28.Chai, C., & Koh, J. (2017). Changing teachers’ TPACK and design
as well by having multiple teams work on each phase of matter.References[1] J. O’Connell, “Challenges to Learning and Teaching Thermodynamics,” ChemicalEngineering Education, Vol. 53 No. 1 (2019): Winter 2019.[2] S. C. Brown, "The Caloric Theory", Men of Physics: Benjamin Thompson – Count Rumford,Elsevier, pp. 16–24, 1967. doi:10.1016/b978-0-08-012179-6.50008-3, ISBN 9780080121796[3] “Abstract Thinking: What It Is, Why We Need It, and When to Rein It In,” HealthLine, n.d.[Online]. Available: https://www.healthline.com/health/abstract-thinking[4] P. Freire, Pedagogy of the oppressed. New York: The Continuum Publishing Company, 1970.[5] D. Riley, “Pedagogies of liberation in an engineering thermodynamics class.” Proceedings ofthe American
among underrepresented groups in STEM. Byaddressing these challenges and amplifying the factors that motivate and sustain women inengineering, we can not only enhance their experiences but also contribute to a more diverse andinnovative engineering workforce.References[1] F. A. Hrabowski, "Empowering underrepresented students in STEM: The role of mentoringand community," 2019.[2] M. Gasman and T. H. Nguyen, "HBCUs are at the forefront of STEM education for AfricanAmericans," 2016.[3] S. Leath and T. M. Chavous, "Influences of race and gender on African American women'sSTEM experiences," 2017.[4] K. Cross, T. McDonald, and D. Rowe, "Mentorship and identity development for women inSTEM fields at HBCUs," 2019.[5] UNCF, "The Impact of HBCUs on
of constant curiosity, makingconnections, and creating value among both educators and students. By embracing scalableapproaches and innovative solutions, institutions can amplify the impact of EML, transformingthe culture of engineering education and preparing students to address the complex challenges oftomorrow.7. AcknowledgmentsWe thank the Kern Family Foundation and the KEEN Program for their continuous support.8. References[1] Boice, R., (2000). Advice for New Faculty Members. Allyn & Bacon, Needham Heights,MA[4] Brent, R., & Felder, R.M. (2003). A Model for Engineering Faculty Development. Intl.Journal of Engr. Education, 19(2), 234–240.[2] Dillon, H., James, C., Prestholdt, T., Peterson, V., Salomone, S., & Anctil, E
. (2018, December). Effects of GPS spoofing on unmanned aerial vehicles. In \textit{2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC)} (pp. 155-160). IEEE. [4] Warner, J. S., \& Johnston, R. G. (2003). GPS spoofing countermeasures. \textit{Homeland Security Journal}, \textit{25}(2), 19-27. [5] Tippenhauer, N. O., Pöpper, C., Rasmussen, K. B., \& Capkun, S. (2011, October). On the requirements for successful GPS spoofing attacks. In \textit{Proceedings of the 18th ACM conference on Computer and communications security} (pp. 75-86). [6] Zhang, T., \& Zhu, Q. (2017). Strategic defense against deceptive civilian GPS spoofing of unmanned aerial vehicles. In