our community college NSF-REU projects is also described.The Internal Force ExampleA standard internal force problem of two blocks connected by a string over a pulley is shown inFigure 1. The tensions on either side of the pulley are not equal because the angular accelerationof the pulley is affected by the torque. The initial parameter values are: 10-kg incline-mass, 50-kg hanging-mass, pulley moment of inertia 20-kg-m-sq, pulley radius 0.3-m, coefficient of kineticfriction 0.2, and 35-degree slope angle. The system acceleration value was calculated as 1.47m/s/s. Changing the hanging-mass values or the incline-mass values would yield variousacceleration values using Newton’s law of motion. Adding random values would simulate labdata with
% NIH NASA-20% USDA R&D-25% DOD S&T-30% 2010 2011 2012 2013 2014 2015 2016*Includes EERE, OE, Fossil, Nuclear; excludes ARPA-E (regular appropriations began in FY 2011
Winning Large NSF Proposals D. Keith Roper Engineering Research Centers Program Leader Network for Computational Nanotechnology Program Leader Engineering Education and Centers Division, Engineering Directorate National Science Foundation ASEE Engineering Research Council Annual Conference Bethesda, MD Mon Mar 7 - Wed Mar 9, 2016Disclaimer: The comments in this presentation are of the author, and do not necessary reflect those ofthe National Science Foundation (NSF)Thanks to: D. Brzakovic, R. Gupta, C. Hemingway, P. Kharghonekar, S. Lim M. Molnar
2019 FYEE Conference : Penn State University , Pennsylvania Jul 28 Work in Progress: An Introduction to Computer Vision for First-Year Electrical and Computer Engineering Students Daniel T. Klawson, Nathaniel A. Ferlic, and Cheng Peng Department of Electrical and Computer Engineering, University of Maryland, College Park Abstract-- This work-in-progress paper will detail one of of machine learning, artificial intelligence, image processing,ENEE101’s newest modules, computer vision. ENEE101 is the and self-driving cars.introductory course to electrical and computer engineering (ECE)at the University of Maryland (UMD) [1] [2]. This
, write a problem statement, collect requiredinformation/data, calculate a numeric answer, and justify their solution. Informed by our pilot study, Grigg et al.’s [7] problem solving rubric, and our own experiences,we redesigned the ill-structured problem assignment used in spring 2017 and assigned it to 130 first-yearengineering students as a replacement for a 20-point exam question. The assignment required students toidentify and analyze a physical phenomenon using physics principles from the course. The module thatthis was assigned during focused on Newtown’s Laws, forces, circular motion, drag, terminal velocity,Newton’s Universal Law of Gravitation, and weightlessness. Students were given two weeks tocomplete the assignment and
in their first semester, showing around a 48% improvement in retention, or nearly 20 percentage points higher. Figure 1. Overall engineering retention rates, regardless if students took ENGR 1300 in their first semester. The years shown are the Fall cohorts of students. In Figure 1, we track first year, and second year retentionrates within the college of engineering. It should be notedthat ENGR 1300’s first cohort was Fall 2015 and was Figure 3. Second year engineering retention rates forrestricted to 72 students per section. Then, for Fall 2016, the Fall 2015 cohort considering whether students tookthe enrollment grew to 99 students per
addition of the ASA increasing thevoltages, and a distance of 7 to 9 cm from the tip of the needle spinnability of the fibers. Uniformity measurements on blankto the stationary collector plate. fiber mat (PCL/CHI = 100/0) revealed that fibers were mostly2.3 SEM Imaging and Material Characteristics deposited at the center of the collection plate and gradually Fiber morphologies and fiber diameters were analyzed by leveled off toward outer area (Figure 3).using a JOEL scanning electron microscopy. Circular punches 3.2 Mechanical Testingwere taken from the fiber mats and sputter coated with Au/Pd Representative engineering stress and engineering strainfor 30 s using
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women to engineering, with specific attention to theofferings at the University of Louisville J.B. Speed School of Engineering.IntroductionWomen and minorities have been underrepresented by alarming proportions ininstitutions of higher education in general and in science and engineering (S&E)programs in particular over the last quarter century. Although more female and minorityhigh school students have at least heard of engineering, relatively few of them have hadthe opportunity to become familiar with engineers and the work they perform. As notedin Figure 1, Bachelor's degrees awarded in S&E and non-S&E fields by sex for the yearsof 1966–2004, nationally, women earn substantially more bachelor's degrees in non-S&Efields than
. The professor that wishes to treat such areas will be most successful in a laboratoryenvironment that includes computer monitor projection capabilities, as well as individual workstations. Inexamining the expanded utilization possibilities of engineering computer laboratories, though, other issues mustbe addressed before a strategy of implementation can be advocated. To explore the role of informationtechnology in the college classroom, we must more clearly define its missions in both the educationenvironment and the engineering workplace. We can then identify key areas that information technology andservices (IT&S), and specifically engineering computer laboratories, can assist in achieving these goals,supported by examples from the
I .—-. Session 3230 Use of Hypermedia Modules on CD-ROMs to Teach Communication Skills in Engineering Laboratories — Sandra Gronhovd, S. L Mehta North Dakota State UniversityA b s t r a c t The ability to communicate is essential for engineering students, and coursework is frequently offered toprovide these skills: However
, non-ideal process in an engine or the extra work while the ratio of the actual efficiency of an engine to associated with each non-ideal proo%s in an air the ideal efficiency of an engine with the same heat conditioner, but the existing text books do not source and sink describes the 2nd Law eftkiency of a provide good examples of the applications of these cycle. techniques. Thus, a paper was presented and published for the 1995 conference to demonstrate the Simiiarly, two methods can also be defined for use of these techniques for fuel burning Carnot and evaluating the overall performance of a cycle. The Rankine cycles. This paper presents similar fwst
and prevention.Simultaneously, business and industry are increasingly seeking graduates withappropriate background and training in this emerging and lucrative field of biomedicalengineering and technology. The United States Labor Department supports this area ofconcentration by forecasting a job growth of 31.4 percent through 2010, double the ratefor all other jobs combined. The aging U. S. population as well as the increase demandfor improved medical devices and systems, are contributing to this increase in demand.Women will be motivated so that the stagnant or even decreasing 20 percent level ofenrollment in engineering and technology fields nationwide may be lifted byunderstanding that the careers in this area are exciting, rewarding
universities.Whereas the nation has developed an excellent system of graduate education for basic researchduring the 1960’s, 70’s, 80’s, and 90’s, it has not placed an equal emphasis on professionallyoriented graduate education to continue the professional development of our nation’s engineeringgraduates who enter engineering practice in industry. As a consequence, America’s primaryengineering resource for creative technological development and innovation in industry has notbeen fully developed to its potential during the last three decades.If we are to develop professionally oriented curriculum that is more aligned to the needs of theU.S. engineering workforce in industry, in order to ensure the nation’s competitiveness forworld-class technology development
, including spreadsheets. The weights corresponding to each need go ondifferent rows, and the Learning Objectives run along different columns. Relation matrix elementsare identified as: R(column number, row number) = R(j,i)Likewise, the computed array, S can be expressed as S(column number) = SjThe index “i” varies from 1 to m, where m = the number of learning objectives and the index “j”varies from 1 to n, where n = number of needs. Learning Objectives LO1 LO3 LO3 Needs Weights N1 W1 R(1,1) R (1,2) R (1,3) N2 W2 R
Machine and compare the results with unwelded specimens.ProcedureTwo 6061 aluminum alloy plates (6x4x ¼ in) were welded together using the FSW process. Theweld was performed using tool rotational speed of 1200 rpm, the transverse speed of 4.5 mm/s,and plunging force of 5000 N. The welded plate was cut perpendicular to the welded line toproduce four rectangular strips. The strips were machined using CNC mill to make identicalspecimens for the tensile tests. The five steps of the welded specimens’ preparation and thegeometric characteristics of the test specimen are shown in Figure 3. Figure 3- Procedure Steps for Tensile TestThe recorded operation parameters of the FSW machine during the Al-Al welding processes
draw out guide values and assumptions in theanalysis portion of this project [11]. We asked guides to describe details of the experience,including what was solidified for them. Interviews were conducted via Skype video conference,and were audio recorded, transcribed, and coded.The first author of this study is a member of the raft guide community and thus benefited fromeasy access to a pool of participants for recruitment. Multiple coders to ensure analysis was notbiased. The first participant was a 30-year-old male who is a high school social studies teacher inthe off-season. He has been guiding for 9 years and has taught numerous guide schools in whichhe trained others to become guides. The second participant was a female in her early 20’s
searching. Educational Psychologist, 39, 43–55.Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefsabout knowledge and knowing and their relation to learning. Review of EducationalResearch, 67(1), 88–140.King, P. M. & Kitchener, K. S. (1994). Developing Reflective Judgment: Understanding andPromoting Intellectual Growth and Critical Thinking in Adolescents and Adults. San Francisco:Jossey Bass.King, P.M., & Kitchener, K. S. (2001). “The Reflective Judgment Model: Twenty Years ofResearch on Epistemic Cognition,” in B.K. Hofer and P.R. Pintrich, eds., PersonalEpistemology: The Psychology of Beliefs about Knowledge and Knowing, Mahwah, NJ:Lawrence Erlbaum Associates.King, P. M. & Kitchener, K. S
city in Massachusetts,USA. The 199 participating students worked in pairs and trios. An overview of the curriculum ispresented in Table 1, below. In practice the curriculum lasted 14 days, as teachers provided extratime for learners who needed remediation or extra challenge.We generated data from pre- and post-surveys (N = 120 paired); pre-, post- and follow-upinterviews (14, 17, and two, respectively); students’ design artifacts; and classroom observationsof eight student pairs (including 20 hours of video and 10 hours of screen-capture), all in order toexplore student engagement in practices of computation, engineering, and science. Table 1 Overview of smart-greenhouse curriculum sequence Day(s) Topic
the impact of creating the videos is inprogress and will be reported at the 2019 ASEE Annual Conference.5. Conclusion This project is studying the role of prosocial affordance beliefs about the ECE professionon motivation to persist in the profession. It also seeks to understand whether a simpleclassroom intervention that forces the student to think about the prosocial value of thecourse material can improve their beliefs about the profession, and in turn, their persistenceintensions. 46. References Bardi, A., & Schwartz, S. H. (2003). “Values and behavior: Strength and structure of relations,” Personality and Social Psychology Bulletin
with technology innovations, since computingcapabilities are driving advances in data management and cyber-physical system capabilities. 6 Acknowledgments The authors wish to acknowledge support from Office of Naval Research for grant “HigherEducation Pathways for Maritime Mechatronics Technicians (MechTech)”, Agency ProposalNumber N00014-15-1-2422.ReferencesArciszewski, H. F. R., de Greef, T. E., & van Delft, J. H. (2009). Adaptive Automation in a Naval Combat Management System. IEEE Transactions on Systems, Man & Cybernetics: Part A, 39(6), 1188-1199. doi: 10.1109/TSMCA.2009.2026428Arregi, B., Granados, S., Hascoet, J. Y., Hamilton, K., Alonso, M., & Ares, E
interaction, we hope to identify recommendations wecan make to other parents on how to foster engineering interest in their children, as wellas contribute ideas for activities for K-5 classrooms to reach a wider range of children.AcknowledgementThis material is based upon work supported by the National Science Foundation underGrant No (HRD-1136253). Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect theviews of the National Science Foundation. We would also like to acknowledge thecontributions of the GRADIENT research team members Scott VanCleave, MaggieSandford and Zdanna Tranby for data collection.References 1. Ceci, S., J., & Williams, W. M. (2010
the Simulink model the pulses in thesignal are counted and converted to an angular velocity. Since direction is not important in this setof experiments only one photo interrupter was used. However, a second photo interrupter couldbe added if direction is needed in the future.Motor DriverSince the voltage and current required for the motor are too high to be directly sourced by theRaspberry Pi, an additional power supply and H-bridge were used to drive the motor. A variableDC power supply that has 1.5V increments from 3V to 12V was selected. However, 4 AAbatteries could also be used. The SoftPWM library from the WiringPi libraries was used inanother Simulink S-function driver to generate the pulse-width modulated (PWM) signal to drivethe H
fluid mechanics students for their participation,feedback, and support of this experimental project.References1 Britton, B. K., and Tesser, A., “Effects of Time-Management Practices on College Grades,” Journal ofEducational Psychology, Vol. 83, No. 3, 1991, pp. 405-410.2 Gregory, J. M., W. J. Carter, and P. S. Gregory, The Student's Handbook for Academic Survival in College,McGraw-Hill, 1997.3 Gregory, J. M, Xie, X., and Mengel, S. A., “Active and Passive Learning Connections to Sleep Management,” 33rdASEE/IEEE Frontiers in Education Conference, Boulder, CO, Nov. 2003.4 Gregory, J. M, Xie, X., and Mengel, S. A., “Sleep Management: A Frontier for Improved AcademicPerformance,” Proceedings of the 2003 ASEE Gulf-Southwest Annual Conference, The
Psychological Association, 2012.[2] Koedinger , K. R., E. Brunskill, R. S. Baker, E. A. McLaughlin, and J. Stamper, “Newpotentials for data-driven intelligent tutoring system development and optimization,” AIMagazine, vol. 34, no. 3, pp. 27–41, 2013.[3] Butz, C. J., S. Hua, and R. B. Maguire, “A web-based intelligent tutoring system forcomputer programming,” in Proceedings of International Conference on Web Intelligence, pp.159–165, IEEE, 2004.[4] Hsiao, I.-H., P. Brusilovsky, and S. Sosnovsky, “Web-based parameterized questions forobject-oriented programming,” in Proceedings of World Conference on ELearning, E-Learn, pp.17–21, 2008.[5] Brusilovsky, P. and S. Sosnovsky, “Individualized exercises for self-assessment ofprogramming knowledge: An
, especiallyfor untenured, tenure-track faculty who have expectations for being able to share passions for notonly research but also teaching. The TLC is supporting our professors of practice as theytransition from industry to academia and teaching. The support by the department chair reducesperceived risk of trying new teaching pedagogies. Finally, we are building a diverse communityof faculty dedicated to teaching in a department that has not has a strong teaching community inthe past.Ambrose, S. A., M. Bridges, M. DiPietro, M. C. Lovett and M. K. Norman (2010). How learning works : seven research-based principles for smart teaching. San Francisco, CA :, Jossey- Bass.Cox, M. D. (2004). "Introduction to faculty learning communities." New
] A. K. Ambusaidi, and S. M. Al-Bulushi, “A longitudinal study to identify prospective science teachers’ beliefs about science teaching using the draw-a-science-teacher-test checklist,” International Journal of Environmental & Science Education, vol. 7, no. 2, pp. 291-311, April 2012.[6] K. D. Finson, “Investigating preservice elementary teachers’ self-efficacy relative to self- image as a science teacher’” Journal of Elementary Science Education, vol. 13, no. 1, pp. 31-41, October 2001.[7] R. Hammack, & T. Ivey, “Elementary teachers’ perceptions of engineering and engineering design,” Journal of Research in STEM Education, vol. 3, no. ½, pp. 48-68, 2017[8] C. Cunningham, C. Lachapele, and A
collaboration.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.#1525345. 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. This work is done in collaboration with the University of Kansas, Indiana University,Queen’s University at Kingston, University of British Columbia, University of California, Davis,University of Colorado Boulder, and the University of Texas at San Antonio.References [1] C. Baillie and G. Fitzgerald, “Motivation and attrition in engineering students,” European Journal of Engineering Education, vol. 25, no. 2, pp. 145–155, 2000. [2] B. N. Geisinger and D
) Bioinformatics in the post-sequence era. Nat Genet 33 Suppl:305-10. 4. Our cultural commonwealth: The Report of the ACLS Commission on Cyberinfrastructure for the Humanities and Social Sciences, July 18, 2006 5. Buetow, K (2005) Cyberinfrastructure: empowering a “third way” in biomedical research. Science 308(5723): 821-824. 6. Greene, K. and S., Donovan. (2005) Ramping Up to the Biology Workbench: A Multi-Stage Approach to Bioinformatics Education. Bioscene 31(1): 3-11. 7. Rainey, D., Faulkner, S., Craddock, L., Cammer, S., Tretola, B., Sobral, B.W., and O., Crasta. 2007. A project-centric approach to cyberinfrastructure education. TeraGrid 2007. 8. He, Y., R. R. Vines, A. R. Wattam, G
University of Georgia has resulted in over 100 publications and 3 patents. Page 13.1379.1© American Society for Engineering Education, 2008 Variation in computing the Length Factor in the Universal Soil Loss Equation Ernest W. Tollner Abstract The universal soil loss equation, A = R*K*L*S*C*P, estimates average annual soil loss A based on rainfall (R), soil factor (K), length factor (L), slope (S), effective cover factor C, and a practice factor P. In teaching the use of the relationship, students can find values of R on