solid model initially provided by the OEM (which wasmade purposefully deficient to illustrate the effect of poor modeling) and incorrectly predicted thefailed component(s). The next instructional phase was in experimental setup, nondestructivemeasurement techniques, data acquisition systems, and analysis of experimental data. This led tostudents running destructive experiments on real OEM assemblies in the lab, and discovering thattheir predictions did not match reality. We took advantage of the teaching opportunity to illustratethe effect of problem setup in meshing the solid models; students corrected and optimized theirmodel and were able to correctly predict the failed component. This exactly mirrors what happensin the OEM’s own labs.The
future?; and (2) Make asuggestion(s) for improving the course (a criticism alone is not helpful; tell your instructor howyou would fix any problem).ProcedureData from the course evaluations were collected once each semester had ended. Students in theFall 2013 course participated in the traditional version of the course while those in the Fall 2014and Spring 2015 courses participated in the flipped version. There were no differences in GPA,age, or gender between students in the traditional versus flipped courses. However, students inthe flipped sections had slightly more International students.Quantitative resultsAnalyses were run to test whether differences existed between a traditional versus flipped courseon student performance, course
Education, vol. 34, no. 1, pp. 29-45, 2009, Art. no. Electronic.[2] S. M. Reich and J. Reich, "Cultural Competence in Interdisciplinary Collaborations: A Method for Respecting Diversity in Research Partnerships," American Journal of Community Psychology, vol. 38, pp. 51-62, 2006, Art. no. Electronic.[3] A. Kakar, "Teaching analogies and metaphors to enhance communication in interdisciplinary and cross-functional groups," M. S. Electronic thesis, Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, 2008.[4] S. Kim, L. D. McNair, and M. C. Paretti, "Self-Organizing Units in an Interdisciplinary Course for Pervasive Computing Design," in American
thematic ‘semi-structured’ interviews (Flick, 2009). Resultswill be coded using open coding technique (Glaser & Strauss, 1999). Data collection should starton the second semester 2019. We expect to collect around 600 participants in the quantitativephase and around 30 in the qualitative phase.ReferencesAuthor (2019). Antro-Diseño. Santiago: Ediciones UC (in press)Burke, R.J., (2007). Women and minorities in STEM: a primer. In R.J. Burke and M.C. Maitis (Eds.), Women and Minorities in science, technology, engineering and mathematics: Upping the numbers (pp. 3-7). Bodmin, Cornwall: MPG Books Ltd.Cabrera, A. F. & La Nasa, S. (2000). On the path to college: Three critical tasks facing America's disadvantaged. Center for the
includes a prediction of an even greater shortage: To grow our nation’s science, technology, engineering, and mathematics (STEM) capacity and ensure that Americans nationwide can participate in a science and engineering (S& E) intensive economy, the United States must foster its Skilled Technical Workforce (STW) – individuals who use S & E skills in their jobs but do not have a bachelor’s degree. Rapid changes in the nature of work, education, technology, workforce demographics, and international competition have led to the National Science Board (NSB, Board) to conclude that our competiveness, security, and research enterprise require this critical, but often overlooked segment of our STEM-capable workforce. Adding to
data that could be of use wouldbe to test the various implementations with a different course instructor, and look for similarperformance differences. In conclusion, when some of principle problems associated withteaching and learning programming are analyzed, modified lecture with SI seems to offer somepositive initial results.References[1] D. Sleeman, “The challenges of teaching computer programming,” Communications of theACM, Vol. 29, No. 9, 1986.[2] S. Sentance and A. Csizmadia, “Computing in the curriculum: Challenges and strategies froma teacher’s perspective,” Educ. Inf. Technol., Vol. 22, pp.469-495, 2017.[3] M. Ben-Ari, “Constructivism in computer science education,” Proceedings of the twenty-ninth SIGCSE technical symposium on
] Rattan, A., Good, C., & Dweck, C. S. (2012). “It's ok — Not everyone can be good at math”: Instructors with an entity theorycomfort (and demotivate) students. Journal of Experimental Social Psychology, 48(3), 731–737. doi:10.1016/j.jesp.2011.12.012[2] “Minority Serving Institutions: Americas’ Underutilized Resource for Strengthening the STEM Workforce,” The NationalAcademies Press, Washington DC (2019). DOI: https://doi.org/10.17226/25257[3] Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race ethnicityand education, 8(1), 69-91.[4] Smith, J. M., & Lucena, J. C. (2016). Invisible innovators: how low-income, first-generation students use their funds ofknowledge to belong in
on pedagogicalmethods, or seeing methods used by other instructors. SEEFs shared common motivations ofjoining a career involving instruction motivated by experiences during student teaching roles,and a desire to improve teaching practices. In addition, the SEEF community referred to Barkleyet al.’s handbook [29] on collaborative learning techniques provided a wealth of teachingmethods, along with Godsell’s sourcebook [30] which provided perspective on differentmethods, their implementation and evaluation, among many other sources. However common toall the SEEFs was interpreting the application of these methods to fit their discipline. Forexample, in Computer Science the jigsaw method was used to explore the ethics of human-computer
external evaluator collects evaluation data on each cohort and each component of thetraineeship according to our logic model-based evaluation plan. At the time of paper submissionwe do not yet have results of the first year’s evaluation.AcknowledgmentThis material is based upon work supported by the National Science Foundation under Grant No.DGE-1828942.[1] E. Golde and G. Walker, Eds., Envisioning the Future of Doctoral Education: Preparing Stewards of the Discipline. San Francisco: Jossey-Bass, 2006.[2] C. G. P. Berdanier, A. Talley, S. E. Branch, B. Ahn, and M. F. Cox, "A strategic blueprint for the alignment of doctoral competencies with disciplinary expectations," International Journal of Engineering Education, vol. 32
lack of participants understanding other points of view and a lack ofconnectedness with other participants. In prior years, this lack of connection led to studentsstruggling with at least two components of the engineering design process: 1. students werereluctant to collaborate with their peers, as they were often “stuck” on using their own ideas and2. students had difficulty defining the purpose for their designs, or in other words, difficultyexplaining the problem(s) they were trying to solve [13]. These findings were a springboard forconsidering how to effectively integrate empathy and engineering as the thread which weaves theprogram together. Program designers intentionally wove empathy connections with people’sreal-life stories into the
Technology at Farmingdale YEONG S. RYU graduated from Columbia University with a Ph.D. and Master of Philosophy in Mechan- ical Engineering in 1994. He has served as an associate professor of Mechanical Engineering Technology at Farmingdale State College (SUNY) since 2006. In addition, he has conducted various research projects at Xerox Corporation (1994-1995), Hyundai Motor Corporation (1995-1997), and New Jersey Institute of Technology (2001-2003). He has been teaching and conducting research in a broad range of areas of system identification and control of nonlinear mechatronic systems and vibrations in structures requir- ing precision pointing to eliminate the detrimental effects of such diverse disturbance sources
Educational Research Association and American Evaluation Association, in addition to ASEE. Dr. Brawner is also an Exten- sion Services Consultant for the National Center for Women in Information Technology (NCWIT) and, in that role, advises computer science and engineering departments on diversifying their undergraduate student population. She remains an active researcher, including studying academic policies, gender and ethnicity issues, transfers, and matriculation models with MIDFIELD as well as student veterans in engi- neering. Her evaluation work includes evaluating teamwork models, broadening participation initiatives, and S-STEM and LSAMP programs.Mr. Behzad Beigpourian, Purdue University at West Lafayette
they relied on for their sense of recognition as scientists. In theirstudy, all the women saw themselves as science people; that is, they identified as scientists. Theirinternalized recognition, coupled with the recognition by meaningful others, further reinforcedwomen’s identities as scientists. As such, external recognition played a critical role in validatingtheir competence as knowledgeable science people. All of the women in Carlone and Johnson’s[21] study were professionals or working towards a terminal degree, thus maintaining a steadfastinterest in their career pursuits. Hazari et al.’s [22] study provided evidence of the importance ofexplicitly integrating interest in the identity framework as it helped students establish an
disruptmarginalization, more seriously? We offer these as discussion openings for the session and forthe larger community.References:Grant, J., Masta, S., Dickerson, D., Pawley, A. L., & Ohland, M. W. (2022, July). “I Don’t LikeThinking About this Stuff”: Black and Brown Student Experiences in Engineering Education. In2022 ASEE Annual Conference & Exposition.
is supported by the US National Science Foundation through grant number 2126978.The opinions are those of the authors and do not necessarily represent the National ScienceFoundation. We acknowledge Dr. Jacqueline Handley's contribution to data collection andpreliminary analysis. We also thank Dr. Aileen Huang-Saad and Dr. Joi Mondisa for theiradvisory roles in this project. 4REFERENCES[1] National Research Council, Discipline-based education research: understanding and improving learning in undergraduate science and engineering. Washington (D.C.): The National academies press, 2012.[2] P. Shekhar, H. S. Aileen, and J. Libarkin, “Understanding
Sci, vol. 3, no. 1, pp. 129–137, Jan. 2016, doi: 10.1177/2372732215623553.[6] S. Thompson, “Perspectives on English language learner programs: A case study,” Dissertation, Lindenwood University, 2019.[7] J. Reeves, “‘Like Everybody Else’: Equalizing Educational Opportunity for English Language Learners,” TESOL Quarterly, vol. 38, no. 1, p. 43, Apr. 2004, doi: 10.2307/3588258.[8] D. Chakraverty, “A Cultural Impostor? Native American Experiences of Impostor Phenomenon in STEM,” CBE Life Sci Educ, vol. 21, no. 1, Mar. 2022, doi: 10.1187/cbe.21-08-0204.AppendixTable 1- Communication Workshop Prompts. The graduate student/post-doc mentors delivered these scenarios verbally to the PROPEL interns,who were asked to
theuniversity staff supporting makerspaces.ReferencesAndrews and Boklage, under review.Creswell, J.W., & Creswell, J.W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed). SAGE Publications.Forest, C. R., Moore, R. A., Jariwala, A. S., Fasse, B. B., Linsey, J., Newstetter, W., & Quintero, C. (2014). The Invention Studio: A University Maker Space and Culture. Advances in Engineering Education, 4(2), n2.Martin, L. (2015). The promise of the maker movement for education. Journal of Pre-College Engineering Education Research (J-PEER), 5(1), 4.Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook. 3rd.Ogle, J. H., Bolding, C. W
major selected by the institution. Hence these smalladvantages are accumulating to something that is really important: their choice of major and theireventual career path.Bibliography 1. N. V. Mendoza Diaz, S. Y. Yoon, D. A. Trytten and R. Meier, "Development and Validation of the Engineering Computational Thinking Diagnostic for Undergraduate Students," in IEEE Access, vol. 11, pp. 133099-133114, 2023, doi: 10.1109/ACCESS.2023.3335931. 2. Noemi V. Mendoza Diaz, Trinidad Sotomayor, Effective teaching in computational thinking: A bias-free alternative to the exclusive use of students’ evaluations of teaching (SETs), Heliyon. Volume 9, Issue 8, 2023, e18997, ISSN 2405-8440, doi.org/10.1016/j.heliyon.2023.e18997
form (Table 2) in the EOP Activity Worksheet todocument the core LO they chose, the reason they selected that LO, and which ABET studentoutcome(s) would be achieved with this LO, as denoted by the orange circle icon and associatednumbers at the end of each LO (see Appendix 2).Step 4: EOP prompted teams to utilize the physical copies of the EOP Framework:Comprehensive Guide to Teaching Core Learning Outcomes [12] for inspiration and ideas forclassroom activities and resources that they could bring into their selected engineering course tointroduce sustainability to students or to suggest their own ideas of activities/assignments.Example activities provided by EOP included a reading and discussion question, a video thatintroduces a
sexual identity—intersect with STEM-related areas of inquiry. Using a variety of interdisciplinary perspectives, WGS 250 investigates how STEM fields both shape and are shaped by ideas and assumptions about gender and identity. Topics include feminist critiques of science, intersections of gender with technology design/use, gender and the built environment, and links between gender and “doing” STEM. Learning Outcomes: ● Demonstrate an understanding of core critical concepts in the field(s) of feminist STEM studies, particularly critiques of objectivity, neutrality, and evidence. ● Identify and articulate the mutually constitutive intersections of social categories
tools and concepts. Theexpectation was that the students would be more honest in their assessment of their learning thanin the final reflections where there may have been a tendency to tell the instructor what theywant to hear.The student Self Evaluations and interviews were coded using Saldana’s [33] structural codingapproach, a first cycle coding method with a focus on particular topics relevant to the researchquestions. In this case, Kendall et al.’s [3] definition of engineering leadership provides aframework to evaluate the evidence of the development of engineering leadership competenciesin the course: Engineering Leaders (a) employ the full range of engineering skills and knowledge in the design of socio-technical innovations
(CEED) at Virginia Techfor providing us with the opportunity to host a workshop for incoming students during theirsummer bridge program. This material is based upon work supported by the National ScienceFoundation under Grant No. 1943811. 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.References[1] American Society for Engineering Education, “Profiles of Engineering and Engineering Technology, 2022,” American Society for Engineering Education, Washington, D.C., 2023. Accessed: Mar. 27, 2024. [Online]. Available: https://ira.asee.org/wp-content/uploads/2024/03/Engineering-and-Engineering-Technology
with research. 4. A dedicated staff member whose job is to be a student liaison and plan/coordinate and facilitate REU events is a critical addition to the leadership team.Acknowledgements: This material is based upon work supported by the National Science Foundationunder Grant 2149667. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.References[1] National Academies of Sciences Engineering and Medicine, Advanced Technologies for Gas Turbines.Washington, DC: The National Academies Press, 2020.[2] National Academies of Sciences Engineering and Medicine, Commercial Aircraft Propulsion andEnergy Systems
challenges.Examples of these challenges include integrating FPGAs with sensors to achieve intelligenthome energy management or optimizing air conditioners for precise temperature control. Theoverarching goal was to explore a range of science and math concepts, such as binary arithmetic,Boolean logic, combinational circuits, finite state machines, and memory read-and-writeprocesses. Additionally, students had the opportunity to engage with guest speakers who areexperts and role models in the field of computer hardware engineering.Measures and data sourcesBefore and after the seminar, students completed Romine et al.'s [15] Student Interest inTechnology and Science (SITS) survey. The SITS instrument assesses individual interest inscience and technology
partial when teachers eitherdidn’t get to the end of the challenge or did not implement major elements of the challenge (e.g.,not having students do presentations at the end of the challenge).Figure 1. STEM-ID Implementation by Grade Level Challenge and Teacher 6th Grade 7th Grade 8th Grade Systems Cell and System Visualizat Desig Syste Visualizat Desig Phone Investigati DesigTeachers Data s ion n Data ms ion n Design on n123
. Hayne, “Design of an Instructional Processor,” Supplement to: C. Roth and L. John, Digital Systems Design Using VHDL, Third Edition, Boston, MA: Cengage Learning, 2018. [Online]. Available: http://academic.cengage.com/resource_uploads/downloads/1305635140_559956.pdf.[3] RISC-V International. [Online]. Available: https://riscv.org/.[4] S. Harris, D. Chaver, L. Pinuel, O. Kindgren, and R. Owen, “RVfpga: Computer Architecture Course and MOOC using a RISC-V SoC Targeted to an FPGA and Simulation,” Proceedings ASEE Annual Conference and Exposition, Baltimore, MD, June 2023.[5] Grenoble Institute of Technology, “LeaRnV: RISC-V based SoC Platform for Research Development and Education.” 2020. [Online]. Available: https