1.26disabled students are less likely to be comfortable with 2 Pearson Weatherton, Y., R. D. Mayes, and C. Villanueva-Perez (2017) Barriers to Persistence of Engineering Students with Disablities: A Review of Literature, ASEE, Paper ID#19583
theoptimization applications was designed to be consistent with the activation of the neuralnetworks reported in MRI studies on engineering students, physics professors and hapticlearners. The effectiveness of the optimization approach would confirm the assertion put forth inan ASEE previous presentation that engineering physics is a universal donor degree. It wouldalso provide a means by which to implement the recommendation presented in another previousASEE paper in which the engineering students’ conclusion was “the learning of physics beingirrelevant in their third semester after completing introductory physics”. The contrast betweenthe van Hiele learning model and Bloom’s taxonomy model on educational learning objectives inthe learning of physics is
Paper ID #36187Remote Professional Development Opportunities for K-12 Teachers during aPandemicDr. Howard S. Kimmel, New Jersey Institute of Technology HOWARD KIMMEL is Professor-Emeritus of Chemical Engineering and Retired Executive Director of the Center for Pre-College Programs at New Jersey Institute of Technology. In 2019 Dr. Kimmel was a recipient of the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring, one of 15 awardees nationwide. In addition, Dr. Kimmel has received numerous awards in recognition of his service, including: ASEE 1985 Vincent Bendix Minorities in Engineering
conference papers and book chapters.Mr. Ryan Hare, Rowan University Ryan Hare received his B.S. in Electrical and Computer Engineering from Rowan University in 2019. He is currently pursuing his Ph.D. in Electrical and Computer Engineering at Rowan University. His current research focus is applying artificial intelligence methods to create enhanced educational systems and improve student learning. Further interests include serious games, intelligent tutoring systems, adaptive or intelligent educational systems, and leveraging student data to enhance learning. American c Society for Engineering Education, 2022 Evaluation of an AI-assisted Adaptive Educational
surveying, geodesy, integration of multi-sensor remote sensing, navigation and geospatial mapping technologies, and hydro-acoustic signal processing for hydrographic mapping. His research interest covers a wide range of geophysical modeling, space weather, and real-time Geospatial Infrastructure Information Management Systems (GIIMS). He has authored and co-authored more than 40 refereed journal articles on these areas of interest and more than 30 conference presentations on real-world applications. He is a member of the American Society of Engineering Education (ASEE), the New Jersey Society for Professional Surveyors (NJSPLS) and of the Hydrographic Society of America (THSOA).Dr. Huiran Jin, New Jersey Institute of
Paper ID #36184Motivating Middle Schoolers to Be EngineersDr. Howard S. Kimmel, New Jersey Institute of Technology HOWARD KIMMEL is Professor-Emeritus of Chemical Engineering and Retired Executive Director of the Center for Pre-College Programs at New Jersey Institute of Technology. In 2019 Dr. Kimmel was a recipient of the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring, one of 15 awardees nationwide. In addition, Dr. Kimmel has received numerous awards in recognition of his service, including: ASEE 1985 Vincent Bendix Minorities in Engineering Award, and ASEE CEN- TENNIAL
. Cognition and Instruction, 30(2), 170-206.[2] Weinberg, P. J. (2020, June). A Pathway Towards STEM Integration: Embodiment, Mathematization, and Mechanistic Reasoning. In 2020 ASEE Virtual Annual Conference Content Access.[3] Russ, R. S., Scherr, R. E., Hammer, D., & Mikeska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Education, 92(3), 499-525.[4] Royal Society. 2017. Machine learning: The power and promise of computers that learn by example. Technical Report.[5] Fiebrink, R. (2019). Machine learning education for artists, musicians, and other creative practitioners. ACM Transactions on Computing Education (TOCE
Caporale, and J. David Furlow, (2019), “First-Year Seminars as a venue for Course-basedUndergraduate Research Experiences: a preliminary report”, First year seminars as a venue for course-based undergraduate research, University of California, Davis. Davis, CA. 95616 Volume 45 (2) August2019.Zhaoshuo Jiang, Juan M. Caicedo, and Robert Petrulis, (1018), “NSF REU SITE: Collaborative Research:Integrated Academia-Industry Research Experience for Undergraduates in Smart Structure Technology”,ASEE Annual Conference Proceedings, 2018, Salt Lake City, Utah.
Magnetism with Transmission Lines', 87th Annual Pacific Northwest Section ASEE additionally estimates value based additionally estimates value based on One of the most important lessons that can be taught in an engineering on equation derived by student. equation derived by student. Measurement of Created Resistor - 3 cm Conference, 2019.electromagnetics class is that basic electrical components are not just
. Academics, vol. 29, no. 2, pp. 116–142, May 2018, doi: 10.1177/1932202X18758260.[17] N. Beider, “The zeal of the convert revisited,” J. Scientific Stud. Religion, vol. 60, no. 1, pp. 5–26, Mar. 2021, doi: 10.1111/jssr.12698.[18] R. Kegan, In over our heads: The mental demands of modern life, 4th ed. Cambridge, Mass.: Harvard Univ. Press, 1997.[19] M. Bunn, A. Bennett, and P. J. Burke, “In the anytime: Flexible time structures, student experience and temporal equity in higher education,” Time & Soc., vol. 28, no. 4, pp. 1409– 1428, Nov. 2019, doi: 10.1177/0961463X18787649.[20] A. Bennett and P. J. Burke, “Re/conceptualising time and temporality: An exploration of time in higher education,” Discourse: Stud. Cultural Politics