/CBO9780511803932.[12] National Postdoctoral Association, “NPA Core Competencies.” [Online]. Available: https://www.nationalpostdoc.org/page/CoreCompetencies[13] B. S. C. Kwan, H. Chan, and C. Lam, “Evaluating prior scholarship in literature reviews of research articles: A comparative study of practices in two research paradigms,” Engl. Specif. Purp., vol. 31, no. 3, pp. 188–201, Jul. 2012, doi: 10.1016/j.esp.2012.02.003.[14] S. A. Crossley, D. R. Russell, K. Kyle, and U. R mer, “Applying Natural Language Processing
teaching interventions.References:[1] F. Mosteller, “Broadening the Scope of Statistics and Statistical Education,” Am. Stat., vol. 42, no. 2, pp. 93–99, May 1988, doi: 10.1080/00031305.1988.10475536.[2] A. Carberry, S. Krause, C. Ankeny, and C. Waters, “‘Unmuddying’ course content using muddiest point reflections,” in 2013 IEEE Frontiers in Education Conference (FIE), Oct. 2013, pp. 937–942. doi: 10.1109/FIE.2013.6684966.[3] L. P. Snead, “The Effect of Using the Muddiest Point Technique in a Large General Chemistry Class,” Master of Science, Drexel University, Philadelphia, Pennsylvania, United States, 2016. doi: 10.17918/etd-7381.[4] M. J. Prince and R. M. Felder, “Inductive Teaching and Learning Methods: Definitions
research [3]. Industry-universitypartnership is a requirement of this model, which calls for concerted participation of industryexperts in the training of students through identification of industry-relevant research problems,co-advising about how to approach their practical solutions, and training for other non-technicalskills that are crucial for success in industry. An assessment of student demand and their experience with P3’s non-traditional features,support of university administration, and the challenges felt by interested faculty advisers duringits implementation at Lehigh University were presented previously [3, 4]. This paper completesP3 program’s assessment by analyzing the feedback provided by industry scientists who haveserved
Methods to Inform Criteria for Broadening Participation in Institutions and Organizationsintroduction2022’s Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act [1]mandates efforts to “ensure collaboration and coordination across federal agencies, the privatesector, and with state and local governments to facilitate timely and effective reviews of allfederally funded projects.” The 4b requirement includes “measures of the institution’s ability toattract and retain a diverse and nontraditional student population in the fields of science,technology, engineering, and mathematics, which may include the ability to attract women,minorities, and individuals with disabilities.” To retain the workforce enabled by this act
quantitative approachesholds exciting possibilities to solve human-centered designs. Finally, interdisciplinarycollaboration between AI researchers, educators, and industry professionals can drive innovationand create new opportunities for integrating AI into industrial engineering education.References[1] Z. Lv, “Generative artificial intelligence in the metaverse era,” Jan. 01, 2023, KeAi Communications Co. doi: 10.1016/j.cogr.2023.06.001.[2] S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Business and Information Systems Engineering, vol. 66, no. 1, pp. 111–126, Feb. 2024, doi: 10.1007/s12599-023-00834-7.[3] A. Borji, “Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and
outcomes,” presented at the ASEE Annual Conference and Exposition, Conference Proceedings, 2012. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85029080569&partnerID=40&md5=e0961e206913bf6956d610a54a631bac[10*] I. J. Paredes, R. Li, S. Kwak, C. Woods, and D. R. Krusniak, “Creation of a Workshop Series on Inclusive Teaching and Design Practices for Engineering Undergraduate Teaching Assistants,” presented at the 2024 ASEE Annual Conference & Exposition, Jun. 2024. Accessed: Nov. 26, 2024. [Online]. Available: https://peer.asee.org/creation-of-a-workshop- series-on-inclusive-teaching-and-design-practices-for-engineering-undergraduate-teaching- assistants[11*] G. Zhang, “Support
understandable and separate parts. Each of these parts hasbeen designed with a specific purpose related to the system, and it looks at the system from aunique perspective. When put together, these three parts work hand in hand to describe thesystem completely. These parts of the same system also make it easy for students to divide theirthought processes into separate perspectives. These parts are (i) Project Concept Diagram/s, (ii)User Operational Flowcharts, and (iii) Functional Block Diagrams. Literature suggests that thesecharts/diagrams have a unique place in the System Engineering approach. However, in thispaper, a table is created with purpose, needed perspective, elements, format, and examples foreach part. Authors also point out connections
enrolled in the course have mirrored State University’s undergraduate population. The studentpopulations comprise significant income and racial gaps, in which 49% of students are racial and ethnicminorities and 36% of students are first-generation, meaning they are the first in their families to attendcollege (State University, 2023). A third of students interviewed were first generation college students,but due to anonymity concerns we offer this as a general distinction rather than identifying particularstudents in the Table 2. Institutional Review Board approval was obtained for this study (University IRBProtocol H23-0706).Table 2. Demographics of interviewed participants Label Major(s
VI. REFERENCES [1] Rose, S. J., Allen, D., Noble, D., & Clarke, J. A. (2017). Quantitative analysis of vocalizations of captive Sumatran tigers (Panthera tigris sumatrae). Method Accuracy False False Processing Bioacoustics, 27(1), 13–26. https://doi.org/10.1080/09524622.2016.1272003 Positives Negatives Time [2] X. Kong, D. Liu, A. Kathait, et al., "Behavioral-psychological motivations encoded in
was taken once a steady state was reached. For batch tests, thepressure was increased once the data for the desired pressure value had been taken. Heat Source I T1 I TH n n s s u T2 u l l a a Speciment
microcontroller-based EplayBot reconfigurable robotic kit inthree separate stages: a small-scale foosball table with a programmable ‘kicking leg’, anautonomous car, and a humanoid robot. We prepared a lesson plan and are in the process ofcreating an interactive video tutorial for use with the platform. We hope educational robotic kitslike this would be able to successfully spark the notion of creativity, skillfulness, and motivationin future roboticists.AcknowledgmentsThis work was supported in part by the Launch Grant and the School of Engineering Dean’sOffice at Wentworth Institute of Technology. The authors thank Doreen Cialdea and Tory Lamfor logistical support, and Nepali Class Boston where part of this work was tested.References[1] M. E. Karim, S
Fall2024 semester. Several faculty awarded minimal extra credit for completing the survey, othersposted the link as an announcement or assignment with no extra credit, and three facultydistributed the survey to all students in the program. Three hundred forty-two responses werereceived. Although it is possible that some respondents may have completed the survey multipletimes, the authors thought that would be unlikely given the timing, with the survey beingadministered in the last two weeks of the semester. Survey responses were gathered and analyzedas described below.Descriptor analysisThe survey asked respondents to “Think of a professor(s) who has or had a positive influence inyour life or education. Provide (3) words or phrases that describe
to our project and have been an active participant in both the lecture portions of the course, but also in our design work. I feel as if my contributions have represented a good understanding of design principles and different project management skills."References[1] J. Stommel, “How to ungrade,” in S. Blum, Ed., Ungrading: Why Rating Students Undermines Learning (and What to Do Instead), West Virginia University Press, 2020, pp. 25–41.[2] C. Pulfrey, C. Buchs, and F. Butera, “Why grades engender performance-avoidance goals: The mediating role of autonomous motivation,” Journal of Educational Psychology, vol. 103, no. 3, pp. 683–700, 2011. [Online]. Available: https://doi.org/10.1037
AEOPcooperative agreement award (W911SR-15-2-0001).References[1] S. Weiss-Lopez, M. Frye, and O. Jones, “Overview of the megaGEMS AEOP Summer 2021 Research Apprenticeship Camp”, Proceedings of the 129th American Society of Engineering Education Annual Conference and Exposition, Minneapolis, Minnesota, June 26 - 29, 2022 https://peer.asee.org/overview-of-the-megagems-aeop-summer-2021- research-apprenticeship-camp[2] G. Sikazwe, S. Weiss-Lopez, D. Peters, and M. Frye, “How to Develop a Culture of Coding for the Future: A Case Study of the megaGEMS Coding Academy”, 2024 American Society of Engineering Education Annual Conference and Exposition Proceedings, Portland, Oregon, June 23 – 26, 2024. https://doi.org/10.18260
theinterview protocol are listed below in Table 1.Table 1. Example questions from second interview protocol Example Questions Status of Job What types of jobs (i.e., positions and industry) have you applied for? Search Job Search In general, how do you search and apply for jobs? Tell me about your process to find available positions that match your interests. Interviews Have you interviewed for any full-time positions? Tell me a bit about how your interview(s) have gone so far. Have you felt prepared during your interviews? Why or why not? Challenges Have you experienced any challenges during the job search process? If so, what are they
), Adelaide, Australia, July 15-20, 2017, in INSIGHT, vol. 20, issue 3, pp. 9-17, Sep. 2017.[5] T.F.A C. Sigahi and L.I. Sznelwar, “Exploring applications of complexity theory in engineering education research: A systematic literature review,” Journal of Engineering Education, vol. 111, no. 1, pp. 232-260, 2022.[6] A. Kwamie, S. Causevic, G. Thomson, A. Sie, R. Sauerborn, K. Rasanathan, and O.P. Ottersen, “Prepared for the polycrisis? The need for complexity science and systems thinking to address global and national evidence gaps,” BMJ Global Health, vol. 9, no. 9, 2024.[7] J. Rosenhead, L.A. Franco, K. Grint, and B. Friedland, “Complexity theory and leadership practice: A review, a critique, and some recommendations,” The
1Work-Based Experiences (WBEs) (Corwin et al., 2015; Krim et al., 2019; Linn et al., 2015;NAS, 2017). A pivotal tool in implementing the student-centered component(s), into the variousstudent opportunities is the Reflexivity worksheet. In operationalizing the “Seed to Flower”framework, this Reflexivity worksheet is a tool enabling coordinators to help move STEM teammembers from awareness to action. The grant’s innovative approach positions the network as amodel for transforming the higher education landscape to support STEM student success.About the “Seed to Flower” framework - The S2F framework was piloted in the professionaldevelopment modules provided to HSI agents participating in the grant and facilitated by thesubject matter experts in
Mathematics Teacher artin High School STEM Academy, Arlington ISD M Abstract s part of UT Arlington’s Research Experience for Teachers (RET) in Engineering and ComputerAScience program, K-12 STEM teachers participated in research with the UTA faculty and graduate students with the goal to translate this research experience into classroom activities that will broaden the student’s awareness of participation in computing and engineering pathways. High school teachers C. Lugo from Fort Worth ISD and M. Treadway from Arlington ISD researched with Dr. K. Hyun, Civil Engineering, UT Arlington and graduate students, A. Imran, and M
Networks Curve Fitting”, 2024. (Last accessedproduct, besides data science statistical computation as an AI March 2025) https://lucidar.me/en/neural-networks/curve-fitting-product? The Students’ responses: data collection automation nonlinear-regression/is AI (8 out of 8)[4] J. Wittenauer, “Neural Newo rks”, 2016. (Last accessed Feb 2025) (Last [10] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. accessed Feb 2025) https://www.johnwittenauer.net/machine-learning- “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”, exercises-in-python-part-6/ 2016 https://arxiv.org/abs/1503.03585[5
-source GNU Radio software, and SDR hardware such as ADALM Pluto, along with affordable benchtop equipment, enhances learning and understanding of key concepts. REFERENCES [1] “Modern Digital and Analog Communication Systems”, B.P. Lathi and Zhi Dong, Oxford University Press, Fifth Edition, 2019. [2] Pierre, J., & Hossain, M. S., & Hosur, S
Development History and Implementation Outlook of the United States, 2023 ICEBE. [3] J. Putnam, J. Littell, “Crashworthiness of a Lift plus Cruise eVTOL Vehicle Design within Dynamic Loading Environments”, 76th Vertical Flight Society's Annual Forum & Technology Display, October 6, 2020. [4] M. Ding, A. Xie, S. Zhu, W. Song, J. Cai, X. Yan, "Crashworthiness
Stability for Fiber-Optic Mach-Zehnder Interferometer Filter”, 3rd International Conference on Microwave and Millimeter Wave Technology Proceedings, 0-7803-7486-/X/02, pp. 1087-1089, 2002.3. Vizoso Beatriz, Vazquez Carmen and Civera Rafael, “Amplified Fiber-Optic Recirculating Delay Lines” IEEE Journal of Lightwave Technology, Volume 12, No. 2, pp 294-304, February 1994.4. M. Ferdjallah and H. Bouchareb, “A New Synthesis Procedure for Designing Digital Filters Based on Optical Fiber Structures”, ASEE North Central Section Conference, Morgantown WV, 2023, March 24-25, 2023.5. D. Fye, " Practical limitations on optical amplifier performance," IEEE J. Lightwave Tech. 2,403-406 (1984).6. S. S. Wagner
. Simul. Mater. Sci. Eng., vol. 13, no. 2, p. R53, 2005, doi: 10.1088/0965-0393/13/2/R01.[7] K. Thornton, S. Nola, R. Edwin Garcia, M. Asta, and G. B. Olson, “Computational materials science and engineering education: A survey of trends and needs,” JOM, vol. 61, no. 10, p. 12, Oct. 2009, doi: 10.1007/s11837-009-0142-3.[8] National Science and Technology Council, “Materials Genome Initiative Strategic Plan,” 2021. [Online]. Available: https://www.mgi.gov/sites/default/files/documents/MGI-2021- Strategic-Plan.pdf[9] K. Ohno, K. Esfarjani, and Y. Kawazoe, Computational Materials Science: From Ab Initio to Monte Carlo Methods, 1999th edition. Berlin ; New York: Springer, 2000.[10] A. D. Rollett and P. Manohar, “The Monte Carlo
for course,program, and assessment support. References1. S. Reber and E. Smith, “College Enrollment Disparities: Understanding the Role of Academic Preparation”, January 2023 [Online], Brookings Center on Children and Families, Available: https://www.brookings.edu/wp-content/uploads/2023/02/20230123_CCF_CollegeEnroll ment_FINAL2.pdf, [Accessed Dec. 12, 2024] )2. M. Kurlander, S. Reed and A. Hunt, “Improving College Readiness: A Research Summary and Implications for Practice”, Policy Analysis for California Education, August 2019, [Online]. Available: https://edpolicyinca.org/publications/improving-college-readiness-research-summary-and -implications-practice, [Accessed Dec. 12
. 112, pp. 719-740, 2023, doi: 10.1002/jee.20538.[13] M. Polmear, “‘It gives me a bit of anxiety’: Civil and architectural engineering students’ emotions related to their future responsibility as engineers,” American Society for Engineering Education (ASEE) Annual Conference. Paper ID #37665. 15 pp. 2023.[14] K.J. Jensen and K.J. Cross, “Engineering stress culture: Relationships among mental health, engineering identity, and sense of inclusion,” Journal of Engineering Education, vol. 110, pp. 371-392, 2021, doi: 10.1002/jee.20391.[15] K. Fiedler and S. Beier, “Affect and cognitive processes in educational contexts,” Chapter 3 in R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education
’s with Alan Turing and the “Turing Test”,officially coined as “Artificial Intelligence” by John McCarthy in 1956 (19). AI is a generalterm for technology that “enables computers and machines to stimulate human learning,comprehension, problem solving, decision making, creativity and autonomy” (19). Under theumbrella of AI are multiple AI-based tools (AI-T) that can assist in the engineering designprocess.The first of these is Machine Learning (ML). According to Radhika Jajkumar, “ML refers to theprocess of training a set of algorithms on large amounts of data to recognize patterns, whichhelps make predictions and decisions” (20). There are three categories that ML is divided into:supervised learning, unsupervised learning, and reinforced
knowledge of use in engineering education.Finally, the presence or absence of explicit "designing for X" framing reveals its potential value forfocusing RtD work. While Shroyer's explicit use clarified her focus, its absence in Coppola's worksuggests opportunities for more intentional framing in future RtD efforts in engineering education.Even this limited sample reveals how the approach can generate structured insights about educationaldesign work while accommodating different scales of innovation. As we continue to examineadditional examples of RtD in engineering education, these initial insights provide a foundation forunderstanding the approach's potential contributions.ReferencesCoppola, S. M., & Turns, J. A. (2023, June). Developing a
, especially in the capstone which typically has all students from a major or multiple major(s) participating in it; • Having enough projects to keep group sizes optimal without having multiple groups working on the same project lest they collaborate and effectively work as a larger group; • Having an open-ended design problem with enough regular, expert mentorship meetings to effectively guide the students to viable solutions and design deliverables without “hand-holding” the students toward a specific set of solutions; and • Defining an appropriately complete and robust yet approachable target for design deliverables with a structure that is flexible enough to accommodate a range of projects while