students who took the survey were also satisfied with the program as indicatedby the 77% of survey respondents who agreed or strongly agreed that they would apply to be inthe ImageSTEAM program again. More than half of the students agreed or strongly agreed theywould recommend someone like them to attend the ImageSTEAM program (62%).AI workshops, in this paper, are viewed as problem-solving events using critical thinking toexplore ways and methods to improve learning using available tools. A comprehensive paperwill be made, when the third and final workshop is made in summer 2023. Lessons learned fromthe workshop experiences will be shared with the community.Acknowledgement: The authors thank the U. S. National Science Foundation for sponsoring
/S) transition, software-defined radio, through-the-wall radarimaging (TWRI)I.INTRODUCTION Materials common in construction are reinforced concrete or cement-based materials.Occasionally, these materials naturally develop cracks due to deterioration throughout their lifecycle. The detection of cracks, erosion, voids, and gaps in walls and structural supports is criticalin preventing structural failures. Microwave-based, non-invasive techniques such as Non-Destructive Testing (NDT) are preferred to detect structural anomalies since there is no impacton the integrity of the structure or material due to the penetration ability of microwaves intodielectric materials. Current NDT equipment have several limitations and drawbacks, such asbeing
these programsthrough learning how participants in K12 STEM outreach programs define mentoring. Thispaper focuses on one research question from our pilot study: How do university student mentor definitions of “mentoring” compare to those of faculty / staff program coordinators?Theoretical FrameworkTo categorize participants’ definitions of mentoring, the research team utilizes Pfund et al.’s [14]attributes of effective mentoring relationships, which are “supported by the literature andsuggested by theoretical models of academic persistence” [p. S238]. This framework was chosenbecause of the ample existing metrics and examples of measurable learning objectives provided,which can be mapped to experiences participants share in their
multidisciplinaryapproach, Proceedings of the 7th International Management Conference, "New Management forthe New Economy", November 7th-8th, 2013, Bucharest, Romania[7] F. C. Bothma , S. Lloyd & S. Khapova (2015). Chapter 2 Work Identity: Clarifying theConcept, pp. 23-51, Springer Science+Business Media Dordrecht, 2015 23 P. G .W. Jansen, G.Roodt (eds.), Conceptualising and Measuring Work Identity, DOI 10.1007/978-94-017-9242-4_2[8] R. L. Cruess, S. R. Cruess, J. D. Boudreau, L. Snell & Y. Steinert (2015). A schematicrepresentation of the professional identity formation and focialization of fedical students andresidents: A guide for medical educators. Academic Medicine, vol. 90(6), June 2015[9] K. Adams, S. Hean, P. Sturgis & J. M. Clark, (2006
., Brooke, C., Mickelson, S., and Freeman, S. (2009). Assessing student work to support curriculum development: An engineering case study. Journal of Learning Communities Research, 3(3), Dec 2008/Jan 2009, 47-62. 5. Richter, D.M. and Paretti, M.C. (2009). Identifying barriers to and outcomes of interdisciplinarity in the engineering classroom. European Journal of Engineering Education, 34(1), 29-45. 6. Seidel, V.P. and Fixson, S.K. (2013). Adopting design thinking in novice multidisciplinary teams: The application and limits of design methods and reflexive practices. Journal of Product Innovation and Management, 30(S1), 19-33. 7. Adams, R.S. and Felder, R.M. (2008). Reframing professional development: A systems approach to preparing
. Tversky, Eds. New York, NY: Cambridge University Press, 1982, pp. 201–208. their career when encountering with participants. coding and thematic coding. The quantitative 2 N. J. Roese, “Counterfactual Thinking,” Psychol. Bull., vol. 121, no. 1, pp. 133–148, 1997. 3 K. D. Markman, I. Gavanski, S. J. Sherman, and M. N. McMullen, “The Mental challenging situations, such
Technology (PCAST. ) “Transformation and opportunity: The future of the U.S. research enterprise”, Report to the President, 2012.[5] C. Wendler, B. Bridgeman, R. Markle, F. Cline, N. Bell, P. McAllister and J. Kent. Pathways Through Graduate School And Into Careers. Princeton, NJ: Educational Testing Service, 2012.[6] H. S. Barrows, Practice-Based Learning: Problem-Based Learning Applied To Medical Education. Springfield, IL: Southern Illinois University, 1994.[7] H. S. Barrows, How To Design A Problem-Based Curriculum For The Preclinical Years. New York, NY: Springer, 1985.[8] I. Choi, Y. C. Hong, H. Park, and Y. Lee, “Case-based learning for anesthesiology: Enhancing dynamic decision-making skills through
theperception of stress as part of engineering culture stress perception can also attract more studentsfrom marginalized groups.References1 Schneider, L. in A Paper Presented at St. Lawrence Section Conference. Toronto, Canada. Retrieved from: www. asee. morrisville. edu.2 Ross, S. E., Niebling, B. C. & Heckert, T. M. Sources of stress among college students. Social psychology 61, 841-846 (1999).3 Goldman, C. S. & Wong, E. H. Stress and the college student. Education 117, 604-611 (1997).4 Hudd, S. S. et al. Stress at college: Effects on health habits, health status and self-esteem. College Student Journal 34, 217-228 (2000).5 Macgeorge, E. L., Samter, W. & Gillihan, S. J. Academic Stress
motivating them to choose a career path in thearea of UAV technologies.AcknowledgementThe project is funded by the NSF’s EEC Program. We would also like to thank Lockheed MartinCorporation and Northrop Grumman Corporation, and NASA Armstrong Flight Research Centerfor hosting the participants and giving them a tour their research labs and facilities. We wouldalso like to thank Northrop Grumman Corporation and Lockheed Martin Corporation for theircontinued support of the UAV Lab at Cal Poly Pomona.References[1] S. Bhandari, Z. Aliyazicioglu, F. Tang, and A. Raheja, “Research Experience for Undergraduates in UAV Technologies,” Proceedings of American Society of Engineering Education Annual Conference, Salt Lake City, UT, 25-28 June 2018
-making andconflict management practices thereby enhancing productivity. In addition, support systems forinclusivity and accountability such as the responsibility matrix, team building ice breakers oractivities, and action items trackers facilitated trust management and relationship building [24].Furthermore, team management artefacts such as project schedule(s), task list(s), meeting notes,procurement and budget tracker(s) supported students’ efficient time management practices.While the project schedule facilitated planning of design project activities, the task listsfacilitated work transparency; meeting notes enabled progress tracking of tasks, and theprocurement tracker allowed for cost transparency of design project purchases. The
culturewith a focus on better supporting traditionally underrepresented students. Subsequent researchwill explore how student participation in these types of engagement activities correlate to thedevelopment of an inclusive makerspace and engineering education culture.Acknowledgement – This material is based upon work supported by the National ScienceFoundation S-STEM program under Grant No. 1834139. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the authors and do notnecessarily reflect the views of the National Science Foundation.References[1] M. Galaleldin, F. Bouchard, H. Anis and C. Lague, "The impact of makerspaces on engineering education," in Proceedings of the Canadian Engineering
. Bilec, A. Dukes, A. Nave, A. Landis, and K. Parrish, “Developing and Sustaining Inclusive Engineering Learning Communities and Classrooms.” In 2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022.[3] D. T. Rover, M. Mina, A. R. Herron-Martinez, S. L. Rodriguez, M. L. Espino, and B. D. Le, “Improving the Student Experience to Broaden Participation in Electrical, Computer and Software Engineering,” in 2020 IEEE Frontiers in Education Conference (FIE), 2020, pp. 1–7.[4] L. Long and J. A. Mejia, “Conversations about Diversity: Institutional Barriers for Underrepresented Engineering Students,” J. Eng., vol. 105, no. 2, 2016.[5] M. E. Matters, C. B. Zoltowski, A. O. Brightman, and P. M. Buzzanell
• Academic calendar timing challenges • Resources 17Call to ActionWhat’s Next?• Data informed decisions• Faculty development• Improve MP process• Course coordination• Improve classroom space 018DiscussionReferences:[1] G. C. Wolniak, M. J. Mayhew, and M. E. Engberg, “Learning's Weak Link to Persistence,” The Journal of Higher Education, vol. 83, pp. 795-823. 2012.[2] M. W. Ohland, A.G. Yuhasz, and B.L. Sill, “Identifying and removing a calculus prerequisite as a bottleneck in Clemson's General Engineering Curriculum.” Journal of Engineering Education, vol.93, no.3, pp.253-257. 2004.[3] J. Handelsman, S. Elgin, M. Estrada, S. Hays, T. Johnson, S. Miller
. Expansion to other campuses and disciplines, using a self-sustaining model such as theone employed in Supplemental Instruction may ensure that the value WATTS provides is able toendure.AcknowledgementThe authors are grateful to the National Science Foundation for their generous funding of thiseffort at PSB, IUPUI, and UTRGV. The authors are also grateful for the lasting contributions ofMr. Jon Meckley, who was not only a key contributor to this research effort but also a kind,witty, and caring human being. He will be greatly missed.References[1] S. Wu, S. Zha, and S. Mattson, “Integrating team-based learning modules to improve civil engineering students’ technical writing skills,” Journal of Civil Engineering Education 146, no. 3, 2020.[2
MSIPP DE-NA0003980.The authors are thankful to the support of the DOE/NNSA program manager and the colleaguesat participating universities and national labs. Special thanks to Dr. Stephen Egarievwe atMorgan State University for his constant support and collaboration.References 1. J. Kennedy, P. Abichandani and A. Fontecchio, “An initial comparison of the learning propensities of 10 through 12 students for data analytics education,” IEE Frontiers in Ed- ucation Conference, Oklahoma City, OK, pp. 916-918, 2013. 2. Hirsch, D. D. (2013). The glass house effect: Big Data, the new oil, and the power of analogy. Me. L. Rev., 66, 373. 3. Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020). Big data analytics
tuition and other funding purposes, studentsenrolled in the 3+2 Program are treated as undergraduate students, and thus they are eligible forfunding as they work on multiyear projects with undergraduate students.Note that the project described here does not entirely fit the VIP Program definition from the VIPConsortium [15]. Namely, students may take research credits which are graded S/U. If they takethese credits as Independent Study, then they are graded A-F. Also, Senior Seminar is graded S/Uwhile Senior Design Project is graded A-F. So, there is a mix of grading types that was agreedupon by the program faculty.Moreover, stipends to fund participating students are secured through grants. About $2000 perstudent team for a senior design project
up disproportionate space 6 (0.42%) *Not coded as either inclusive or 967 (68.39%) marginalizing Inclusive Moves(1) Encouraging sharing. One way students increased the participation of other students was by encouraging sharing, which we define as proactively putting out an open-ended call for others’ input. To be coded as an encouraging sharing move, a student’s utterance had to go beyond simply asking for affirmation or refutation of an idea they themselves had stated. For example, when working on a problem about a firefighting hose, S made an encouraging sharing move when they put out a call for a peer’s idea: S: Yeah. What do you think Abe? What should we do?(2) Acknowledging
work supported by the National Science Foundation under AwardNumbers 2114241 and 2114242. Any opinions, findings, and conclusions, or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.ReferencesBartlett, R. (2013). Playing with meaning: using cartoons to disseminate research findings. Qualitative Research, 13(2), 214-227.Berhane, B., Secules, S., & Onuma, F. (2020). Learning While Black: Identity Formation and Experience for Five Black Men Who Transferred Into Engineering Undergraduate Programs. Journal of Women and Minorities in Science and Engineering, 26(2), 93–124. https://doi.org/10.1615/JWomenMinorScienEng
strategies for creating equitable access to the discipline. Byexamining how Western Tech Scholars and their peers become cybersecurity professionals, thispaper provides information about “what works” in influencing a diverse body of students tostudy cybersecurity in institutions that are minority serving.3 MethodologyThis qualitative case study considers the Western Tech S-STEM program as the bounded system[15] under investigation. This section describes the data sources used in this study as well as thedata analysis strategies used. IRB was obtained before gathering data.3.1 Data CollectionData sources for this study include the following: a) Annual interviews with Western TechScholars, occurring between May and October from 2019 to 2021, b
identities are encouraged and how strongly they are expressed. Separating bygender, the results show the significant difference between men, women, and nonbinaryengineering students and how they consider their gender identity. The average Model for MultipleDimensions of Identity based on school type can help understand students' priorities when decidingto attend a small school.References[1] A. D. Patrick and M. Borrego, “A Review of the Literature Relevant to Engineering Identity,” in ASEE Annual Conference and Exposition, Conference Proceedings, 2016, doi: 10.18260/p.26428.[2] K. L. Meyers, M. W. Ohland, A. L. Pawley, S. E. Silliman, and K. A. Smith, “Factors Relating to Engineering Identity,” Glob. J. Eng. Educ., vol. 14
Science Teaching, 44(8), 1187-1218.Chang, M. J., Sharkness, J., Hurtado, S., & Newman, C. B. (2014). What matters in college for retaining aspiring scientists and engineers from underrepresented racial groups. Journal of Research in Science Teaching, 51(5), 555-580.Collins, D., Bayer, A. E., & Hirschfeld, D. A. (1996). Engineering Education for Women: A Chilly Climate? Women in Engineering ProActive Network.Crenshaw, K. (1990). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stan. L. Rev., 43, 1241.Cross, K. J., Clancy, K. B., Mendenhall, R., Imoukhuede, P., & Amos, J. R. (2017, June). The double bind of race and gender: A look into the experiences of
bachelor’s degrees earned by women in the U.S. has remained between 18.1% and20.5% from 2000 to 2015, with women receiving 20.1% of degrees in 2015 [1]. By contrast,women’s representation in the engineering workforce has been steadily increasing since the1990’s, from 8.6% in 1993 to 14.5% in 2015 [1]. However, according to statistics from 2010,within five years of graduation, 36 percent of women who obtained engineering bachelor’sdegrees either left or never entered the field and within fifteen years after graduation, 60 percentof women who earned engineering bachelor’s degrees had left the field [2]. Despite the recentincreases, these numbers indicate that women are still underrepresented in the workforce and thatretention of women engineers in
manufacturing systems; control of large-scale complex systems; robotics/mechatronics; and adaptive and robust control of nonlinear dynamic systems.Prof. Satish Bukkapatnam, Texas A&M University Satish T. S. Bukkapatnam received his Ph.D. and M.S. degrees in industrial and manufacturing engineer- ing from the Pennsylvania State University. He currently serves as Rockwell International Professor with the Department of Industrial and Systems Engineering department at Texas A&M University, College Station, TX, USA. He is also the Director of Texas A&M Engineering Experimentation Station (TEES) Institute for Manufacturing Systems. His research in smart manufacturing addresses the harnessing of high-resolution
students discussed whichfoot type to use for the foot adaptation component of the survival suit design. The first instanceof EBR stated by Sean was also coded functionality because he explicitly referred to hisknowledge that human feet would work in the snowy conditions. The second instance of EBRwas coded technology, since Samuel justified his counterargument by referring to an existingtechnology, shoes. He used his prior knowledge about existing technologies to point out a flaw inhis teammate’s argument that human feet would be the best option for the survival suit.Example related to colors and camouflageIn addition to the choice of the survival suit covering material, students also had to choose whichcolor(s) to make the exterior of their suit
forTeaching and Learning Ordinary Differential Equations: A Systemic Literature Review andBibliometric Analysis,” Mathematics, vol. 9, no. 7, p. 745, Mar. 2021, doi:https://doi.org/10.3390/math9070745.[5] S. Arslan, “Do students really understand what an ordinary differential equationis?,” International Journal of Mathematical Education in Science and Technology, vol. 41, no. 7,pp. 873–888, Oct. 2010, doi: https://doi.org/10.1080/0020739x.2010.486448.[6] C. L. Rasmussen and K. D. King, “Locating starting points in differential equations: arealistic mathematics education approach,” International Journal of Mathematical Education inScience and Technology, vol. 31, no. 2, pp. 161–172, Mar. 2000, doi:https://doi.org/10.1080/002073900287219.[7] C. L
to student success in engineering education,” EuropeanJournal of Engineering Education, vol. 42, no. 4, pp. 368–381, 2017.[5] M. Scheidt, A. Godwin, E. Berger, J. Chen, B. P. Self, J. M. Widmann, and A. Q. Gates,“Engineering students’ noncognitive and affective factors: Group differences from clusteranalysis,” Journal of Engineering Education, vol. 110, no. 2, pp. 343–370, 2021.[6] S.-M. R. Ting and R. Man, “Predicting academic success of first-year engineeringstudents from standardized test scores and psychosocial variables,” International Journal ofEngineering Education, vol. 17, no. 1, pp. 75–80, 2001.[7] B. F. French, J. C. Immekus, and W. C. Oakes, “An examination of indicators ofengineering students’ success and persistence
beneficial involvement. Threshold theories of studentinvolvement predict diminishing or negative returns at higher levels of involvement. These studieshave measured level of involvement as either number of activities or number of hours involved inactivities [58], [63], [64]. These studies fit nonlinear functions of involvement with respect ofacademic outcomes, finding that at high levels of involvement the benefits leveled off or evendeclined slightly. Vetter et al.’s [17] findings about the significance of quality of involvement overquantity of involvement echo these findings, concluding that “co-curricular programs andactivities are of greatest benefit when they encourage students to engage more deeply… only oneor two meaningful co-curricular
learn to see individualstructures or features, and to ask what function that structure or feature accomplishes and whythat is important to the organism. When students are practiced in this, they “learn to see theworld through new eyes” – the world around them is no longer part of the background of theirlives, but rather is now filled with potential solutions to challenging design problems [16].Curriculum BID specific ActivitiesSeveral standard lessons and activities were used for teaching engineering, brainstorming forideas, and as empathy building exercises for problem description. For example, we useSCAMPER, a semi-structured approach to ideation and improving ideas. The categories are, (S)Substitute, (C) Combine, (A) Adapt, (M) Modify
, M. Henderson, E. Creely, A. A. Carvalho, M. Cernochova, D. Dash, T. Davisand P. Mishra, "Creativity and risk-taking in teaching and learning settings: Insights from sixinternational narratives," International Journal of Educational Research Open, vol. 2, no. 2, pp.1-11, 2021.[6] N.R. Kuncel, S. Hezlett, and D. Ones, "Academic performance, career potential, creativity,and job performance: Can one construct predict them all?," J. Educ. Psychol., vol. 102, no. 3, pp.599-616, Aug. 2010.[7] P. C. Wankat, R. M. Felder, K. A. Smith and F. S. Oreovicz, "The scholarship of teachingand learning in engineering," in Disciplinary Styles in the Scholarship of Teaching andLearning: Exploring Common Ground, vol. 1, Indiana University Press, 2002, pp. 217