Engineering (ONU 1997).Dr. John-David S Yoder, Ohio Northern University John-David Yoder received his degrees (B.S., M.S, and Ph.D.) in mechanical engineering from the Univer- sity of Notre Dame. He is Professor and Chair of the mechanical engineering at Ohio Northern University, Ada, OH. He has previously served as Proposal Engineer and Proposal Engineering Supervisor at Grob System, Inc. and Software Engineer at Shaum Manufacturing, Inc. He has held a number of leadership and advisory positions in various entrepreneurial ventures. He is currently a KEEN (Kern Entrepreneurial Ed- ucation Network) Fellow, and has served as a Faculty Fellow at the Jet Propulsion Laboratory, Pasadena, CA and an Invited Professor at INRIA
particular attention to the ways the produce knowledge and the role of mathematical modeling.Dr. Bethany S. Fralick, University of South Carolina, Aiken Dr. Fralick is an Assistant Professor of Engineering in the Department of Mathematical Sciences at the University of South Carolina Aiken in Aiken, SC. She received her Ph.D. in Mechanical Engineering from the University of South Carolina College of Engineering and Computing. Page 26.1344.1 c American Society for Engineering Education, 2015 Revisiting Graphical StaticsIntroductionUp until the 1950's, a
Paper ID #11554Student Development of a Five kW Solar Furnace for Solar Thermal Chem-istry ResearchDr. Gregory Scott Duncan, Valparaiso University G. Scott Duncan is an Associate Professor of Mechanical Engineering at Valparaiso University. He re- ceived a BSME (1990) from Purdue University and Ph.D (2006) in Mechanical Engineering from the University of Florida. His research has focused on the development of systems and components for the area of concentrated solar thermal chemistry.Dr. Shahin S. Nudehi, Valparaiso University Professor Nudehi received a Bachelor degree and a Master degree in Mechanical Engineering from
. His research is in nonlinear vibrations as it applies to structural health monitoring, and assistive technology. He is currently working on grants related to teaching in STEM fields and laboratory curricular development and is active in developing international research opportunities for undergraduates.Dr. Deborah S Munro, University of Portland Deborah is an Assistant Professor of Mechanical Engineering and teaches statics, strength of materials, finite element analysis, biomechanics, automated manufacturing, CAD, and capstone design. She spent multiple years in the orthopedic medical device industry prior to joining academia.Dr. Shazib Z Vijlee, University of Portland Dr. Shazib ”Shaz” Vijlee earned BS and MS
Paper ID #12881A Transdisciplinary Approach for Developing Effective Communication Skillsin a First Year STEM SeminarDr. Jeffrey J Evans, Purdue University, West Lafayette Jeffrey J. Evans received his BS from Purdue University and his MS and PhD in Computer Science from the Illinois Institute of Technology. His research interests are in artificial intelligence for music composition and performance and adaptive computing systems, focusing on the effects of subsystem interactions on application performance. He is a member of the ASEE, ACM and a Senior Member of the IEEE.Prof. Amy S. Van Epps, Purdue University, West
) 0 -0.02 -0.02 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 t (s) t (s) Single-Sided Amplitude Spectrum of y(t) Single-Sided Amplitude Spectrum of y(t
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until it reaches the specimen surface where some of the wavereflects back into the incident bar. The rest of the stress wave continues propagating through thetransmitter bar. In classical SHPB analysis, the specimen strain and stress can be estimated usingthe strain signals measured on the incident and transmitter bars based on the equations derived inreference [11]. The specimen strain is determined as a function of time by simplycalculating the following integral. 2C 0 t Ls 0 s (t ) R (t )dt (2)where (t) is the reflected incident bar strain history collected from the strain gauge
computational modeling activities areintegral to each educational learning module. When students formulate computational models,they develop understanding by engaging in the theory and observations of a situation. Studentscomplete each educational learning module in about three hours outside of class after they havebeen introduced to the individual topic in lecture(s) and completed a series of homeworkproblems. As students complete an activity, they are encouraged to refer to its correspondinggrading rubric, which conveys expectations of quality across different levels of expertise. Ourpedagogical model can be used to design learning modules for difficult concepts in other STEMsubjects.Keywords: cognitive apprenticeship, pedagogical model, engineering
26.470.5rate of rotation. Figure 3 depicts this situation.21 X G S 𝜔𝑡 r θ O
Figure 2. Pictures of plastic venturi sections for (a) Air flow and (b) Water flow.time to figure things out, although open-ended play with the hardware was encouraged. TheTA(s) and instructor were in the room to interact with the students.Venturi nozzle experimentThe first experiment implemented was based on flow through a venturi. The learning objectivestargeted for this experiment were • How fluid flow rates are measured. • How Conservation of Mass defines the relation between velocity and cross-sectional area in a conduit. • How the Bernoulli Equation defines the relation between pressure and velocity in a flowing fluid. • How experimental and ideal conditions differ.Students performed experiments with both
. Iterate Iteratea The difficulty of meeting the requirements will necessitate iteration.b The difficulty of manufacturing will necessitate iterative prototyping.c My team has minimal prototyping experience.3 For a high avg, use a virtual prototype; else, use physical models. Physical Virtuala Virtual prototype(s) will require less time than a physical one(s).b Virtual modeling will validate: physics, interfaces and/or requirements.c A CAD model is needed for analysis (FEA, CFD, etc.) or manufacture.d Time & budget allow pursuit of both virtual and physical prototypes
Processing Workshop, 2004 and the 3rd IEEE Signal Processing Education Workshop. 2004 IEEE 11th, pages 58–62, Aug 2004. doi: 10.1109/DSPWS.2004.1437911. [2] Xuemin Chen, Gangbing Song, and Yongpeng Zhang. Virtual and remote laboratory development: A review. In Proceedings of Earth and Space 2010: Engineering, Science, Construction and Operations in Challenging Environments, pages 3843–3852, Honolulu, HI, 2010. [3] Lyle D. Feisel and Albert J. Rosa. The role of the laboratory in undergraduate engineering education. Journal of Engineering Education, 94(1):121–130, 2005. [4] S. Dormido Bencomo. Control learning: Present and future. In Annual Reviews in Control, pages 115–136, 2004. [5] Nancy Roberts. Teaching dynamic feedback
Paper ID #11558Integrating MS Excel in Engineering Technology CurriculumMr. Dustin Scott Birch, Weber State University Dustin S. Birch possesses a Master of Science in Mechanical Engineering from the University of Utah, a Bachelor of Science in Mechanical Engineering from the University of Utah, and an Associate of Science in Design and Drafting Engineering Technology from Ricks College. Birch is an Assistant Professor and Program Coordinator in the Mechanical Engineering Technology Department at Weber State University. He also serves as the Chairman of the Board of the Utah Partnership for Education. He is a member of the
tangential acceleration values is r = 0.705. 0.4 0.2 Tangential acceleration (m/s^2) 0 0 10 20 30 40 50 -0.2 -0.4 -0.6 Accelerometer based -0.8 GPS based -1 Time (s)Figure 4: Tangential acceleration as determined from
interpersonal skills they attributed to the multidisciplinaryproject with their roles as employees. This case study suggests that undergraduate researchacross disciplines can supplement the undergraduate education and help mechanical engineeringstudents obtain skills useful in addressing contemporary issues like those identified in the NAEgrand challenges1. Further research can help reinforce these initial findings and expand theengineering education community’s understanding of the outcomes associated withmultidisciplinary undergraduate research teams.References1. National Academy of Engineering. Published at http://www.engineeringchallenges.org/, Accessed on 12/18/2014.2. Kirkpatrick, A., Danielson, S., Warrington, R., Smith, R., Thole, K
tRAT is keyto help students to correct misconceptions in real time, and the points-scale gives the studentsmotivation to learn to work together effectively as a team without instructor input. After allteams have completed the tRAT, the instructor can give a short—typically 5 to 10 minutes—lecture clearing up any remaining confusion about the topic. Students are given an opportunity tosubmit a written appeal, as a team, of any RAT question they believe might be ambiguous. Anexample of a RAT is given in the appendix.The applications–on which the most time is spent in class–are problems that the students mustsolve as a team. The applications follow a 4-S format: Same problem, Significant problem,Specific choice, and Simultaneous report. A class
STEM outreach with a full engineering design, build, and test cycle. GlobalJournal of Engineering Education. 2012;14(3):225-232.15. Brown JS, Collins A, Newman S. Cognitive apprenticeship: Teaching the crafts of reading,writing, and mathematics. In: Resnick L, ed. Knowing, learning, and instruction: Essays inhonor of robert glaser. Vol 487. Psychology of Education and Instruction Series ed. LawrenceErlbaum; 1989.16. Tillman D, Kjellstrom W, Smith S, Yoder E. Digital fabrication scaffolds for developingpreservice elementary teachers’ mathematics pedagogy. Society for Information Technology &Teacher Education International Conference. 2011;2011(1):892-897.17. Tillman D, Ducamp G, Dejaegher C, Cohen J, Kjellstrom W, Smith S. A role for
improving the aesthetics and life of those (although we didn‟t domuch in this direction). Another example is to study Van Phillips‟s prosthetic leg and analyze itas a curved beam. This was also modeled in NX9.0 to compare the results.In this paper the author will enumerate different examples and present the assessment andlearning outcomes of using real life examples in the classes.IntroductionAs instructors, we routinely try to use several real life examples in the classes we teach, whetherthey are engineering or non-engineering subjects. Other fields such as medical, fine arts, media Page 26.1075.2and communication, etc., cannot do away without
-stateproblem (Fig. 1) was adopted from an exercise at the end of Chapter 4 (“Two-Dimensional,Steady-State Conduction”) of Incropera et al.’s textbook25, while the transient, semi-infinitemedium problem (Fig. 2) was adopted from an exercise at the end of Chapter 4 (“Transient HeatConduction”) of Çengel and Ghajar’s textbook13.After the introduction of the problem statement and summaries of the educational objectives andrelevant FE and course theory, each ALM includes the following solutions steps (these steps areapplicable to thermal ALM’s using SolidWorks and SolidWorks Simulation, but similar steps arefollowed for ALM’s that use other software packages): 1. Using SolidWorks to create a 3-D model. The steps required to draw the model in
. Boekaerts, M., P. R. Pintrich and M. Zeidner, London: Elsevier, 2000.2. J. Barsch, Barsch Learning Style Inventory, New York: Academic Therapy Publications, 1996.3. P. R. Pintrich, D. A. Smith, T. Garcia and W. J. McKeachie, Motivated strategies for learning questionnaire, Ann Arbor, MI: The University of Michigan, NCRIPTAL, 1991.4. Guglielmino, L. M., Development of the self-directed learning readiness scale, Doctoral dissertation, University of Georgia, Dissertation Abstracts International, Vol. 38, No. 6467A, 1978.5. Hoban J. D., Lawson S. R., Mazmanian P. E., Best A. M., and Seibel H. R., “The Self-Directed Learning Readiness Scale: a factor analysis study,” Med Educ, Vol. 39, No. 4, pp. 370–379, 2005.6. M. Miller, P
some assumptions. Identifies context(s) when presenting a position. May be more aware of others’ assumptions than one’s own (or vice versa).By necessity the rubrics are written such that they can be applied to the broad range of topics thatfall under the FYS umbrella. Particularly relevant to the premise of the FYS Bridge course arethe rubrics that address establishing the background, exploring ambiguity, questioningassumptions, and identifying context, but applied to challenges in engineering, technology, andscience in society. With this in mind, the authors have selected tentatively the following topicsand readings for the course:The questions that science, engineering, and the humanities can answer… and those theycan’t. Selected
Roundtable, 2005, Tapping ’ P Th E I v I v , Business Roundtable: Washington, D.C. 3. Blue, C. E., Blevins, L. G., Carriere, P., Gabriele, G., Leader, S. K. G., Rao, V. and Ulsoy, G., 2005, The Engineering Workforce: Current State, Issues, and Recommendations. Final Report to the Assistant Director of Engineering., National Science Foundation: Arlington, VA. 4. National Academy of Engineering, 2004, The Engineer of 2020: Visions of Engineering in the New Century. National Academy of Engineering: Washington, D.C. 5. National Academy of Engineering, 2004, E h E 2020 p E E h N wC y. National Academy of
education and cognitive psychology, pp. 109–119, 1987.9. L. Davis, S. Luster-Teasley, F. Samanlioglu and L. Parrish. 2007. AGGRIEMENTOR: Improving the retention of undergraduates in STEM areas vie e-mentoring. Proceedings of the American Society for Engineering Education Annual Conference & Exposition. 2007, AC 2007-769.10. S. Brainard and L. Carlin, “A longitudinal study of undergraduate women in engineering and science.” in Proceedings of the Frontiers in Education Conference, November 1997, pp 134-143.11. Ryan Cavanaugh, Matt Ellis, Richard Layton, and Mark Ardis. “Automating the process of assigning students to cooperative-learning teams.” American Society for Engineering Education Annual Conference & Exposition
combination of BayesianKnowledge Tracking and Performance Factor Analyses approaches, are also briefly described.IntroductionGames can be effective learning tools in classroom settings. This fact has been demonstrated ina wide variety of disciplines, across a large range of ages, and over a long period of time. Indeed,successful examples of ‘computerized’ games used in university settings may be identified as farback as the 1960’s when computers were still in their infant stages. For example, Raia1 describesthe effective utilization of a computerized game to teach business management skills at theUniversity of Maryland in 1966.A consistent plea from industries over decades has been the need for universities to train studentsto handle the complexities
assigned book. I also tried to get notes from a classmate to see what concepts are emphasized • Follow up with Professor and got class notes. • Usually nothing, I hope I can learn it the next lecture. A very bad habit, I do admit. I intend to now review it over with a classmate, and do readings that may cover the topics I missed in class. • Look at the corresponding sections in the book(s) that I missed, and Google anything I’m unsure about. • I generally ask the professor what I miss. Then I go home and review the material. If I am confused with any of the material then I’ll come back and ask questions. • Watch topic on You Tube and work through problems on my own
actual acoustic particle velocity of the pressure wave: Vs 3 = ρ s ωs ω 2 V0 1 + 2 1 − + 2iζ s ρ0 ω ω where: ρs is the density of the acoustic velocity sensor; ρo is the density of sea water; ωs is the mounted sensor natural frequency, ωs = keq meq ; ω is the circular frequency of = the signal to be detected, (ω 2π f , 100 Hz ≤ f ≤ 2,000 Hz ); and ζ is the damping ratio of the sensor mount. Goal of Task 1: Provide
students to excelin their individual performance within groups, are expected to provide improved outcomes.References1. Paulino A, Babb P, Saar C, Friesen S, Brandon J, Ieee. Engaging high school students in an engineering thermodynamics project. Paper presented at: IEEE Global Engineering Education Conference; 2014, Apr 03-05, 2014; Istanbul, TURKEY.2. Tebbe PA, Ross S, Pribyl JR, Ieee. Work in Progress - Engaging Students in Thermodynamics with Engineering Scenarios. Paper presented at: 40th Annual Frontiers in Education Conference; 2010, Oct 27- 30, 2010; Arlington, VA.3. Mulop N, Yusof KM, Tasir Z. A Review on Enhancing the Teaching and Learning of Thermodynamics. International Conference on Teaching
Page 26.978.2 student centered teachingThe most commonly prevailing model in engineering education, being practiced from 1950’s, isthe large student in-class lecture delivery system. This norm particularly involves a lecturer’sdiscretion on how a class is organized along with how the student interactions in a class takeplace. The interactions here are defined by a debate, student to student discussion, student tolecturer discussion and so forth 3. Such interactions play an important role in quantifying &analyzing if the goal of improving a student’s knowledge is achieved. Over the past few years,current trends are being observed in stimulating various interaction patterns among students andlecturers in an educational setting