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develop an engineering mindset. Mentors tookan approach of teaching students by first having them watch or closely assist them before settingthem free to continue the task on their own. Mentors’ goal was to have students doing most ofthe decision making and work. Mentors realized the FRC activity was complex and wanted theirstudents to push their limits but still work within their means in order to produce the bestproduct. “If you take somebody and you try to teach them how to do something, about the fourth iteration they start unlearning it. This is occasionally called “drill and kill.” If you teach somebody how to do something by doing it with them and then you back off and let them do it and then go through a Q/A
AREA 40cm 15cm 15cm RED AREA Figure 3: The working area layout of the projectAs Aristotle19 mentioned, “the kind of questions we ask are as many as the kinds of things whichwe know,” the students deepened their understanding of the problem through these Q&Asessions. And the importance of understanding the problem in a design procedure wasstrengthened during the discussion as well. Then, a more detailed project description was givenas: design a sorting system to satisfy the following requirements: 1. The whole system (except arms/sensors) should be placed in the shaded green area in
generate and solve linearcircuit analysis problems, which accepts a rich variety of student inputs. Three tutorials usingthe software have been implemented. Laboratory based studies show a statistically significantand large (~1.21 standard deviation) increase in student learning as a result. Further work willfocus on expansion and completion of this system.AcknowledgmentThis work was supported by the National Science Foundation through the TransformingUndergraduate Education in Science, Technology, Engineering and Mathematics Program underGrant No. DUE-1044497. We thank Daniel Sayre of John Wiley & Sons, Inc. for providing thetextbook copies used in our laboratory experiment.References1 C. D. Whitlatch, Q. Wang, and B. J. Skromme, “Automated
, R.A., “Persistence, Engagement, and Migration in Engineering Programs,” Journal of Engineering Education, vol 97, 2008.2 Singer, J. D., & Willett, J. B. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence , First edition. NY, New York: Oxford University Press, 2003.3 Singer, J. D., & Willett, J. B. "Modeling the Days of our Lives: Using Survival Analysis When Designing and Analyzing Longitudinal Studies of Duration and the Timing of Events," Psychological Bulletin, vol. Page 23.1187.12 110, pp. 268–290, 1991.4 Li, Q., Swaminathan, H. & Tang, J., “Development of a
, the behavioral change is possible because the involved systems haveknowledge about other co-located wireless devices. Page 23.1244.13References1. Gang Zhao, Network Protocols and Algorithms, 2011, Vol. 3, No. 1, Wireless Sensor Networks for Industrial Process Monitoring and Control: A Survey.2. L. Q. Zhuang, K. M. Goh and J. B. Zhang, 1-4244-0826-1/2007 IEEE , The Wireless Sensor Networks for Factory Automation: Issues and Challenges.3. http://www.ni.com/white-paper/7142/en, published May 05, 20124. Javad Shakib, Mohammad Muqri ,118th Annual ASEE Conference, Session: AC 2011- 389, Wireless Technologies in Industrial
13 Manning, A., Brueck, T., Isbell, M., & Brink, P. (2008). Workforce planning for water utilities - successfulrecruiting, training, and retaining of operators and engineers. Denver, Colorado: AWWA Research Foundation.Retrieved from http://www.waterrf.org/PublicReportLibrary/91237.pdf14 Lacey, M. & Boepple-Swider, T.M. (2008). A regulator’s perspective on workforce issues: Water andwastewater operators. Journal: American Water Works Association, 132-135. Retrieved fromhttp://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CD4QFjAB&url=http%3A%2F%2Fwww.workforwater.org%2FWorkArea%2Flinkit.aspx%3FLinkIdentifier%3Did%26ItemID%3D307&ei=kTHmUNCnEcjY2gWQzoGYBA&usg
;RQIHUHQFH RI WKH $PHULFDQ 6RFLHW\ RI (QJLQHHULQJ (GXFDWLRQ [21] G. Olivier, J. Raynauld, R. Camarero, “Learning Outcomes in a Model-based approach toCurriculum Design,” Fifth International Conference on Internet and Web Applications andServices, 2010.[22] E. Lawson, W. Stackpole, “Does a Virtual Networking Laboratory Result in Similar StudentAchievement and Satisfaction”, SIGITE'06, Minneapolis, Minnesota, USA, 2006.[23] Y. Shang-ying, Lan Rui-le, Q. Li, “On Network Curriculum Design and Practice,” The 7thInternational Conference on Computer Science & Education, Melbourne, Australia, Jul. 2012.[24] Z
Page 23.857.20 speaking techniques. presentation.Table 6.2: Criteria for Idea Pitch and Final Exam Presentation Delivery SkillsCriteria Very poor = 1 Good = 3 Excellent = 53. How well did the Speaker did not Speaker understood the Speaker’s answers tospeaker answer the answer questions or questions and helped judge's questionsquestions from the provided answers clarify points in the strengthened the casejudges? which were not presentation to which for the idea andDid the Q/A period relevant to the the judge referred. speaker repeated
for the idea andDid the Q/A period relevant to the the judge referred. speaker repeated thestrengthen the case judge's questions or Speaker repeated the question.for the idea? defensive. question and made sure Speaker clarified the the question was questions points answered effectively and added evidence to an already Page 23.379.20
. Paper presented at theASEE/IEEE Frontiers in Education Conference. San Diego, CA, October 28-31.7.Mena, I., Zappe, S., & Litzinger, T. (2012). Preparing the Engineer of 2020: Analysis of Alumni Data. Paperpresented at the American Society for Engineering Education Conference and Exposition. San Antonio, TX, June10-13.8.Creswell, J. W. (2008). Educational Research. Upper Saddle River, New Jersey: Pearson Education, Inc.9.Benson, J. & Clark, F. (1983). A Guide for Instrument Development and Validation. The American Journal ofOccupational Therapy. 36(12): 789-800.10.Patton, M. Q. (2002). Qualitative Research & Evaluation Methods. Thousand Oaks, CA: Sage Publications, Inc.11.Kilgore, D., Chachra, D., Loshbaugh, H., McCain, J., Jones, M
numerical digits in the query sequence should either be removed or replaced by appropriateletter codes (e.g., N for unknown nucleic acid residue or X for unknown amino acid residue).The nucleic acid codes supported are:A adenosine C cytidine G guanineT thymidine N A/G/C/T (any) U uridineK G/T (keto) S G/C (strong) Y T/C (pyrimidine)M A/C (amino) W A/T (weak) R G/A (purine)B G/T/C D G/A/T H A/C/TV G/C/A - gap of indeterminate lengthFor those programs that use amino acid query sequences (BLASTP and TBLASTN), theaccepted amino acid codes are:A alanine P prolineB aspartate/asparagine Q glutamineC cystine
.) What is TOXMAP? Retrieved January 5, 2013, from http://toxmap.nlm.nih.gov/toxmap/faq/2009/08/what- is-toxmap.html10.) Toxics Release Inventory. Retrieved January 5, 2013, from http://www.epa.gov/tri/11.) C. Hochstein and M. Czczur, TOXMAP: A GIS-Based Gateway to Environmental Health Resources, Med Ref Serv Q, 25(3), pp. 13–31, 2006.12.) D. Kelley, The Fate and Transport of Toxic Releases: A GIS Case Study. Retrieved January 5, 2013, from http://sciencecases.lib.buffalo.edu/cs/files/tri_gis.pdf13.) About Us - National Center for Case Study Teaching in Science. Retrieved January 5, 2013, from http://sciencecases.lib.buffalo.edu/cs/about/14.) D. Kelley, Case Teaching Notes. Retrieved January 5, 2013, from http
). Leaving engineering: A multi-year single institution study. J. Engineering Education, 101(1), pp. 6–27.4. Li, Q., Swaminathan, H., & Tang, J. (2009). Development of a classification system for engineering student characteristics affecting college enrollment and retention. J. Engineering Education, 98(4), pp. 361–376.5. Eris, O., Chachra, D., Chen, H.L., Sheppard, S., Ludlow, L., Rosca, C., Bailey, T. & Toye, G. (2010). Outcomes of a longitudinal administration of the persistence in engineering survey. J. Engineering Education, 99(4), pp. Page 23.669.13 371–395.6. Jones, B.D., Paretti, M.C., Hein, S.F
design.Following these guidelines, this type of space may be replicated to inspire the next generation ofengineers.References1. Maltese, A. V. & Tai, R. H. (2011). Pipeline Persistence: Examining the Association of Educational Experiences with Earned Degrees in STEM Among U.S. Students. Science Education, 95(5), 877-907.2. National Research Council. (2009). Ch. 1: Introduction, Ch. 2: Theoretical perspectives. In Philip Bell, Bruce Lewenstein, Andrew W. Shouse, and Michael A. Feder (Eds.), Learning Science in Informal Environments: People, Places, and Pursuits (pp. 11-53). Washington, DC: The National Academies Press.3. Tai, R. H., Liu, C. Q., Maltese, A. V., and Fan, X. (2006). Planning early for careers in science. Science, 312, 1143
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vertical change, (y – y1), between any variable point on the line and afixed point on the line is always m times the horizontal change, (x – x1). This form provides away to obtain the equation of a line when any point, P1(x1 , y1) on the line and the slope, m, ofthe line are given. y Q(10, 5) Rise = (Qy -Py) = 5 - (-1) = 6
/significant-local-incentive-program/, accessed 21 November 2012.22. Princeton University, “Sustainable Engineering and Developments Scholarship (SEADS) Program.” http://ewb.princeton.edu/?q=seads, accessed 21 November 2012.23. Santa Clara University. “Engineering Students Visit Honduras; Plan Water Distribution Project. http://www.scu.edu/sustainability/sustainabilityupdatearchives.cfm?c=14195, accessed 21 November 2102.24. Sarper, H, S. Dede, R. Malhotra, N. Jaksic, D. Lehmpuhl, H. Carrasco, W. Sauer, and P. Wallace, "Use of Biomass and Fly ash in Briquette Production for Power Plants", CD-ROM Proceedings of 15th European Biomass Conference and Exhibition, 2440-2448, Berlin, Germany, 2007.25. Sarper, Hüseyin, and Hector Carrasco
? Journal of Engineering Education 97, 467–479 (2008).12. Li, Q., Swaminathan, H. & Tang, J. Development of a Classification System for Engineering Student Page 23.310.12 Characteristics Affecting College Enrollment and Retention. Journal of Engineering Education 98, 361–376 (2009).13. Hilpert, J. C., Stump, G. & Husman, J. A Brief Manual for the use of the Pittsburgh Engineering Attitudes Scale – Revised. 1–6 (Arizona State University, 2010).14. R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2012). at 15. Fox, J. An R companion to
, and design project extensions. A sample semesterschedule is provided below in Table 2. Table 2. Course structure. Class Class Activity Number 1 Introduction Mentor Q&A 2 Aerospace Jeopardy Receive Project 1 3 Guest Lecture on Aerodynamics Project 1: Guest Lecture on Structures
relationships and in particular how the brain supports processes of vision and attention.The assignment was to read the provided materials and submit three or more questions for Dr.Appelbaum, who was the guest speaker in class the following week. Dr. Appelbaum’s lecture,“Reverse Engineering the Brain: Primer for EEG and BCI approaches”, provided an introductionto electroencephalography (EEG) signal and data collection, and brain computer interfacetechniques. In addition to providing some general technical background and a discussion of someof the challenges often encountered, Dr. Appelbaum discussed some of his research on visualattention. The lecture was followed by a lively Q&A session.Stage 2 (Restatement) and Stage 3 (Relation to Engineering
precursor to other design topics such as mechanical design andanalysis. Program management and systems engineering concepts and topics were also includedin the other courses that were identified. Although all of these topics are important, the eSETProgram’s goal was to focus exclusively on the product development life cycle and the processesused by industry to innovate, develop, support and terminate products within their companies. With the fundamental framework in place, the eSET team then contacted a number oflarge and small companies in the Dallas, Austin, Houston triangle to set up meetings and Q&Asessions about their product development processes. In addition to size variation, the team wasinterested in having input from a range of
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mapped the key elements of a typicalundergraduate engineering curriculum onto each of the four selected systems. The particles (quanta) areconserved, but particle attributes are not conserved. We hypothesized the following relations to model therespective quanta. E(t)j=E(0)j + Beta i (1 – exp(-t%ji/tc%i)) (1) E(t)j=E(0)j exp(-t%ji/tc%i) + Beta i (1 – exp(-t%ji/tc%i)) (2) E(t)j=E(0)j+1 (3)Where E(t)j represents the exergy or information of a particle “j” at time ”t”; t%ji: residence time at compartment “j” for particle “i”; Time constant (tc%i) = 1+Beta i/Heat loss(Qi), if Q = 0, tc=1; and, Beta i is the exergy
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surveyquestions are provided below. These results were selected because they show interestingdifferences and similarities between the transfer and non-transfer students. Also, these resultsshow the views of all the students of the SAS scholarship program.In Figure 1, a clear difference is shown in the timing of when students decided to major inengineering or computer science. The transfer students were split across all responses, while thenon-transfer students mostly chose to major in the last two years of high school. These resultsmay give insight on how to improve recruitment of transfer students.Figure 1: Decision to major in engineering or computer science. Q 14. When did you decide to major in engineering or Tranfers computer