-based Learning Curriculum in Microelectronics Engineering”, 14th IEEEInternational Conference on Parallel and Distributes Systems, 2008[3] K. Smith, S. Sheppard, D. Johnson, and R. Johnson, “Pedagogies of Engagement: Classroom-Based Practices,”Journal of Engineering Education, Vol. 94, No. 1, 2005, pp. 87-102.[4] B. A. Karanian, L. G. Chedid, M. Lande, G. Monaghan, “Work in Progress - Behavioral Aspects of StudentEngineering Design Experiences” in Proceedings of the 38th ASEE/IEEE Frontiers in Education Conference, NY,October 22 – 25, 2008.[5] L. Cooper and D. A. Kotys-Schwarts, “Designing the Design Experience – Identifying the Factors of StudentMotivation in Project-based Learning and Project Service-based Learning”, in Proceedings of the
, velocity and acceleration profiles; examples using Excel 2.2. S-curve velocity profile derivation of kinematics formulas for position, velocity and acceleration profiles; examples using Excel and MATLAB 3 Multi-axis motion 3.1. Slew motion Uncoordinated motion of individual axes of a machine. Solved examples of motion profiles
, C.S., Del Vecchio, C.A., Kosteleski, A.J., Wilson, S.A., “Development of Problem Sets forK-12 and Engineering on Pharmaceutical Particulate Systems,” Chemical Engineering Education, 44, 50-57, 20102 McIver, K. Whitaker, K. DeDelva, V. Farrell, S. Savelski, M. J. Slater C. S. “Introductory Level Problems IllustratingConcepts in Pharmaceutical Technology,” Advances in Engineering Education, 5 (1) 20113 Otero Gephardt, Z. Farrell, S. Savelski, M.J. Slater, C.S. Rodgers, M. Kostetskyy, P. McIver, K. Diallo, H.Zienowicz, K. Giacomelli, J. DeDelva V. “Integration of Particle Technology with Pharmaceutical IndustryApplications in the Chemical Engineering Undergraduate Curriculum and K-12 Education,” Proceedings of the 2011American Society for
experiment server while still maintaininga secure level of communication. With this interface, no add-ons or plug-ins will need to beinstalled on any computer, and anyone with a web browser and internet access will be able to usethe interface to control an experiment remotely.AcknowledgmentsThis work is partially supported by the National Science Foundation under Grant Numbers EEC-0935208, EEC-0935008, and DUE-0942778.Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the authors and do not necessarily reflect the views of the National Science Foundation.Bibliography[1] Ambrose, S. A., & Amon , C. H. (1997). Systematic design of a first-year mechanical engineering course at Carnegie Mellon
Mashhadi, and J. R. Brown, “Broadening Dissemination Genres to Share Hidden Insight via Design Cases in Engineering Education Research,” in International Handbook of Engineering Education Research, 1st ed., New York: Routledge, 2023, pp. 617–637. doi: 10.4324/9781003287483-34.[2] V. Svihla, S. Davis, and N. Kellam, “The TRIPLE Change Framework: Merging Theories of Intersectional Power, Learning, and Change to Enable Just, Equitable, Diverse, and Inclusive Engineering Education,” Stud. Eng. Educ., vol. 4, no. 2, pp. 38–63, 2023, doi: https://doi.org/10.21061/see.87.[3] V. Svihla, S. Davis, and N. Kellam, “Tenurism, Rankism, Engineeringism, Ableism, Racism, Sexism, oh my! Building awareness of power and privilege on
©American Society for Engineering Education, 2024 A layered mentoring approach for engineering excellence.Abstract:The Alternative Pathways to Excellence (APEX) Program at the University of St. Thomas,funded by NSF as an S-STEM Track 2 project, aims to solidify transfer pathways, and assistEngineering students by providing financial, academic, and practical support. The successfulintegration of transfer students into engineering programs presents a unique set of challenges andopportunities for higher education institutions. The APEX program provides a comprehensivesupport system, including structured and informal mentoring, guidance for both academics andextracurricular activities, and collaborative teamwork experiences. The program is
software (e.g., Autodesk and PTC) [1], [2], [3]. Generativedesign (GD) is a computational design technique that utilizes AI algorithms to generate uniqueoutcomes beyond human capabilities [4], [5]. GD methods in engineering apply generative AI toiteratively explore the design space and generate a (set of) solution(s) that satisfy human-definedobjectives and constraints [6], [7]. These approaches utilize a range of generative techniques, suchas genetic algorithms (GAs), variational autoencoders (VAEs), generative adversary networks(GAN), and large language models (LLMs) [8], [9], [10]. See Figure 1 for a few examples. GAscomputationally mimic natural selection by assigning each generated design a fitness function torepresent how well it reaches the
those constructs. GCA uses an algorithmic approach to score teammates on sixconstructs, of which we used three: social impact, the degree to which an individual’scontributions are taken up by the team; responsiveness, the degree to which an individual picksup and develops the contributions of others; and participation, measured as the number ofutterances above or below the team average.Figure 5 - Scores for each member (S1-S4) of each team for each of the three GCA constructs.The results for team F22 are skewed by S4’s very small number of utterances.Figure 6 - LIWC team-level prosocial behavior scoreTable 1 - Descriptions and examples of interactional positioning codes, taken from [10].Positional move (code) Description
”(IUSE – 2211320 and 1934707).References[1] S. Streiner, D. Burkey, M. Young, R. Cimino, & J. Pascal, “Engineering Ethics Through High-Impact Collaborative/Competitive Scenarios (E-ETHICCS)." ASEE Annual Conference andExposition, Long Beach, CA, July 2021[2] P. Patel, “Engineers, Ethics, and the VW Scandal,” IEEE Spectrum, 25 Sept. 2015. [Online].Available: http://spectrum.ieee.org/cars-that-think/at-work/education/vw-scandal-shocking-but-not-surprising-ethicists-say. [Accessed Apr. 11, 2019].[3] M. Hart, “The Ethical Lessons of Deepwater,” ASME.org, March 2011. [Online]. Available:https://www.asme.org/engineering-topics/articles/engineering-ethics/the-ethical-lessons-of-deepwater. [Accessed Apr. 11, 2019].[4] R.P. Boisjoly, E.F. Curtis
definitivefindings from this multi-year panel-type longitudinal experiment will only be available once allmeasurements (M1-M5) for all three cohorts (blocks) are made, validated, and analyzed.6. References[1] R. T. Palmer, D. C. Maramba, and T. E. Dancy II, "A qualitative investigation of factors promoting the retention and persistence of students of color in STEM," Journal of Negro Education, vol. 80, no. 4, pp. 491-504, 2011.[2] G. L. Cohen, J. Garcia, V. Purdie-Vaughns, N. Apfel, and P. Brzustoski, "Recursive processes in self-affirmation: Intervening to close the minority achievement gap," science, vol. 324, no. 5925, pp. 400-403, 2009.[3] S. L. Clark, C. Dyar, N. Maung, and B. London, "Psychosocial pathways to STEM
., Tavener, S., Voss, K. Armentrout, S. Yaeger, P. and Marra, R., 1999, "Using Applied Engineering Problems in Calculus Classes to Promote Learning in Context and Teamwork," Proceedings - Frontiers in Education Conference, Vol. 2, 12d5-14.3. Barrow, D.L. and Fulling, S.A., 1998, "Using an Integrated Engineering Curriculum to Improve Freshman Calculus," Proceedings of the 1998 ASEE Conference, Seattle, WA.4. Hansen, E.W., 1998, "Integrated Mathematics and Physical Science (IMPS): A New Approach for First Year Students at Dartmouth College," Proceedings - Frontiers in Education Conference, Vol. 2, 579.5. Kumar, S. and Jalkio, J., 1998, "Teaching Mathematics from an Applications Perspective," Proceedings of the 1998 ASEE
California and B.S. in Electronics and Communication Engineering from India.Dr. Pramod Abichandani, New Jersey Institute of TechnologyMs. Heydi L. Dominguez, New Jersey Institute of Technology Heydi Dominguez is a fourth-year undergraduate student pursuing her Bachelorˆa C™s Degree in Me- chanical Engineering and minoring in Innovation and Entrepreneurship at the New Jersey Institute of Technology. Heydi is a first generation college student who isCraig IaboniKevin Alexander Nino ©American Society for Engineering Education, 2023 Using the ARCS Model of Motivation to design 9-12 CS CurriculumAbstractThis ongoing project provides an overview on the use of the Attention, Relevance,Confidence
seventies,” Hum. Relat., vol. 35, no. 12, pp. 1179–1204, 1982.[5] S. Assegaff and A. R. C. Hussin, “Review of Knowledge Management Systems As Socio-Technical System,” p. 6.[6] E. Molleman and M. Broekhuis, “Sociotechnical systems: towards an organizational learning approach,” J. Eng. Technol. Manag., vol. 18, no. 3–4, pp. 271–294, Sep. 2001, doi: 10.1016/S0923-4748(01)00038-8.[7] T. Reiman and P. Oedewald, “Assessment of complex sociotechnical systems – Theoretical issues concerning the use of organizational culture and organizational core task concepts,” Saf. Sci., vol. 45, no. 7, pp. 745–768, Aug. 2007, doi: 10.1016/j.ssci.2006.07.010.[8] S. Winter, N. Berente, J. Howison, and B. Butler, “Beyond the
established.So far, there has been support from organizational structures and changes in individual coursessupport existing learning outcomes.References[1] K. Haas, “Sankey Diagram Analysis: Undergraduate Program Updates 2017-2019,” 2020.[2] N. Desai and G. Stefanek, “A Literature Review of the Different Approaches That Have Been Implemented to Increase Retention in Engineering Programs Across the United States,” in ASEE Zone II Conference, 2017.[3] Georgia Institute of Technology, “Deliberate Innovation, Lifetime Education: Final Report of the Commission on Creating the Next in Education,” 2018.[4] K. D. Hall, D. G. Linzell, B. S. Minsker, J. F. Hajjar, and C. M. Saviz, “Civil Engineering Education Summit: Mapping
Science, vol. 37, pp. 331-356, 2007.[4] W. Faulkner, "Doing gender in engineering workplace cultures. II. Gender in/authenticity and the in/visibility paradox," Engineering Studies, vol. 1, pp. 169-189, 2009.[5] M. Tremblay, T. Wils, and C. Proulx, "Determinants of career path preferences among Canadian engineers," Journal of Engineering and Technology Management, vol. 19, pp. 1-23, 2002.[6] R. W. Lent, S. D. Brown, and G. Hackett, "Contextual supports and barriers to career choice: A social cognitive analysis," Journal of Counseling Psychology, vol. 47, p. 36, 2000.[7] R. W. Lent, H.-B. Sheu, C. S. Gloster, and G. Wilkins, "Longitudinal test of the social cognitive model of choice in engineering
. She holds a Ph.D. in Mechanical Engineering from the University of Minnesota. ©American Society for Engineering Education, 2023 Engineering a Transfer Friendly Experience with Alternative Pathways to ExcellenceAbstract:The Alternative Pathways to Excellence (APEX) program is an NSF funded S-STEM Track 2project that seeks to strengthen efforts to recruit and retain STEM transfer students by integratingfinancial, academic, and practical supports.The APEX program provides student support services, formal and informal mentoring, curricularand co-curricular supports, and cohort building activities all formulated to create accessiblepathways into engineering careers for a population
Cincinnati. Along with his current role as the Manager of Diversity, Inclusion, and Community Engagement for the Co ©American Society for Engineering Education, 2023 Greater Equity, Access, and Readiness for Success in Engineering and Technology (GEARSET) - An Alternate Pathway to Engineering and ETIntroductionThe Greater Equity, Access, and Readiness for Engineering and Technology (GEARSET)Program, an NSF funded S-STEM program was developed to address several institutional needsat the university. The original target population for the GEARSET program was identified as asubset of the students who applied to the College of Engineering (COE) at the University ofToledo (UToledo) and do not meet all the admissions
Science Foundation Grant #1710735. Special thanksto all the faculty who participated in our T1 Summit.References[1] Marker A, Pyke P, Ritter S, et al. Applying the CACAO change model to promote systemic transformation in STEM. Transform Institutions Undergrad STEM Educ 21st Century 2015; 176–188.[2] Peterson V, James C, Dillon HE, et al. Spreading Evidence-Based Instructional Practices: Modeling Change Using Peer Observation. In: The 22nd Annual Conference on Research in Undergraduate Mathematics Education. Oklahoma City, OK: Mathematical Association of America, 2019.[3] Lane AK, Skvoretz J, Ziker JP, et al. Investigating how faculty social networks and peer influence relate to knowledge and use of evidence
education, K-12 STEM teacher professional development, and preservice teacher preparation in STEM.Dr. Joanna K. Garner, Old Dominion University Dr. Garner is Executive Director of The Center for Educational Partnerships at Old Dominion University, VA. American c Society for Engineering Education, 2021 Near-Peer Mentoring and Early Exposure to Computer Science – Quantitative and Qualitative Results - SummaryIntroductionThe CS/M Scholars Program at Western Washington University (WWU) is funded by an NSFTrack 2 S-STEM grant (Award Number 1742110). The grant funds scholarships for low-incomehigh-achieving students majoring in computer science or math and
remotely on their year-long projects at the end of August. The mid-termpresentations were held on January, 2021 and it appeared that all students were making very goodprogress. Advisors meet with students weekly over ZOOM. While everyone is looking forward toreturning to our traditional format, we have all adjusted and I really believe that this year’s programis running as well as it would under traditional circumstances.References[1] ”Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report” National Academies Press: OpenBook, https://www.nap.edu/read/24886/[2] Berman, F., Rutenbar, R., Hailpern, B., Christensen, H., Davidson, S., Estrin, D., aˆ Szalay, A. S. Realizing the potential of data science
Applications” innext phase of the project.AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.1935646. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.References[1] G. Giffi, P. Wellener, B. Dollar, H. Ashton Manolian, L. Monck, and C. Moutray, “Deloitte and The Manufacturing Institute skills gap and future of work study,” 2018.[2] S. A. Ambrose, M. Lovett, M. W. Bridges, M. DiPietro, and M. K. Norman, How learning works : seven research-based principles for smart teaching. San Francisco: US: Jossey- Bass, 2010.[3] S. A. Ambrose and L
wereendangered through their participation. Complementary to this line of analysis, we have beenconducting workshops that introduce this framework as a tool for participants seeking to makechange [6, 7].AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.EEC 1914578, 1915484, 1913128, 1519339. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation. We would also like to acknowledge thecontributions of colleagues Kelly Cross, Donna Riley, and Alice Pawley, and graduate studentsJasmine Desiderio and Susan Sajadi.References1. Kezar, A., What is the best way to
decision treeand separate the sample into mutually exclusive nodes which share common attributes.Attributes of nodes with a high proportion of students overpersisting will be used to identifycurrently enrolled students at risk of overpersisting. The variables used to make thesedeterminations can be categorical or continuous. In addition to traditional demographic variables,the variables currently under consideration are listed in Table 1.Table 1. Predictor variables computed for CHAID analysis Variable(s) Description first.term.hours Number of credit hours attempted in student’s first term first.major & last.major Majors enrolled in during the student’s first and last terms grad.major
“disseminationparadigm” in contrast to the “propagation paradigm” [6]. The dissemination paradigm is the ideathat “if we build it, they will come,” meaning that developers acting in this paradigm believe thatdesigning evidence-supported pedagogical innovations and presenting the results at conferencesand in journal articles will result in adoption. However, the data suggest that this is not the case[7], [8]. On the other hand, the propagation paradigm involves developers working with potentialadopters throughout the development process to create innovations that meet the needs of a widerange of engineering educators, thus providing motivation and opportunity for sustainedadoption. Using Froyd et al.’s [6] paradigms as two ends of a spectrum, we can capture a
achieve thesame research objective(s). In the fourth year, all the REU participants worked in group projectsetting. In addition, each group of REU participants was required to complete a group projectreport discussing the social impacts of their research projects. In both individual project andgroup project settings, REU participants were provided ample opportunities to share theirresearch progress through formal and informal presentations in order to enhance REUparticipants’ understanding and broaden their perspective of energy systems challenges. Inaddition, each REU participant was required to submit an individual final research project report,in order to highlight their findings through an individual poster presentation and give anindividual
other educators and researchers. The data collected through the DEFT system will then beused to develop a pedagogical framework for engineering design.References[1] Ball, J. and Ormerod, T. C. Structured opportunistic processing design: a critical discussion. International Journal of Human-Computer Studies, 43(1):131—151, 1995.[2] Guindon, R. Designing the Design Process: Exploiting Opportunistic Thoughts. Human- Computer Interaction, 5(2):305—344, June 1990.[3] Fricke, G. Successful Individual Approaches in Engineering Design. Research in Engineering Design, 8(3):151—165, 1996.[4] Atman, C. J., Adams, R. S., Cardella, M., Turns, J., Mosborg, S., and Saleem, J. Engineering Design Processes: A Comparison of Students and Expert
. Edwards, R. P. Ramachandran and U. Thayasivam, ``Robust Speaker Verification With a Two Classifier Format and Feature Enhancement’’, submitted to IEEE International Symposium on Circuits and Systems, Baltimore, Maryland, May 28—31, 2017.6. Y. Mehta, R. Dusseau and R. P. Ramachandran, ``Conducting State-of the-art Research in an Institution with a Strong Undergraduate Education Focus”, ASEE Annual Conference and Exhibition, Atlanta, Georgia, June 23--26, 2013. (with)7. S. Davis, M. Frankle, R. P. Ramachandran, K. D. Dahm, and R. Polikar, “A Freshman Level Module in Biometric Systems”, IEEE Int. Symp. on Circuits and Systems, Beijing, China, May 19–23, 2013.8. R. P. Ramachandran, R. Polikar, K. D. Dahm
Industry Initiatives for Science and Math Education [IISME]). Retrieved from http://www.igniteducation.org/about/impact/ on October 27, 2017.[6] J. Dubner, S. Silverstein, N. Carey, J. Frechtling, T. Busch-Johnsen, J. Han, G. Ordway, N. Hutchison, J. Lanza, J. Winter, J. Miller, P. Ohme, J. Rayford, K. Weisbaum, K. Storm, and E. Zounar, “Evaluating Science Research Experience For Teachers Programs and Their Effects on Student Interest and Academic Performance: A Preliminary Report of an Ongoing Collaborative Study by Eight Programs.”, MRS Proceedings, 684, GG3.6 doi:10.1557/PROC-684-GG3.6, 2001.[7] A. M. Farrell, “What Teachers Can Learn From Industry Internships.” Educational Leadership, pp. 38-39