project may have been a way to encourage theteam to slow down and work more intentionally on their designs.AcknowledgementThis material was supported by the National Science Foundation under Grant DRL-1513175.References[1] H. A. Simon, The sciences of the artificial, 3rd ed. Cambridge, MA: MIT Press, 1996.[2] B. Lawson and K. Dorst, Design expertise. Abingdon, Oxon: Architectural Press, 2009.[3] V. Goel and P. Pirolli, "The structure of design problem spaces," Cognitive Science, vol. 16, pp. 395-429, 1992.[4] D. H. Jonassen, "Toward a design theory of problem solving," Educational Technology Research & Development, vol. 48, pp. 63-85, 2000.[5] C. J. Atman, R. S. Adams, M. E. Cardella, J. Turns, S. Mosborg, and J
category is defined to characterize the different perspectives on engineeringfrom the participants on what engineering is. As shown in the quotes under “different ideas aboutengineering”, T1 talked about engineering at the system level, while T2 talked about applyingconcepts to build something. In addition, each counselor also had unique ideas: C1 talked about“manipulating things for a certain outcome” and C2 mentioned that engineering is “mathy” andsciency”. These show that there are differences in how the four participants thought aboutengineering. T1, T2 and C1 had specific definitions about engineering. These are all in contrastwith C2’s comments that engineering is “mathy” and “sciency”. For context, both T1 and T2were teachers that expose
items (see Table 2), determine if scores on the 3C’s varied by product choice, andidentify which aspects of an entrepreneurial mindset are most targeted by Product Archaeology(and likewise, which aspects need further development in regards to EML). The results aresummarized in Figures 1 and 2 and Table 3 below. Table 2. KEEN-related Rubric ItemsKEEN 3C’s Rubric Item(s)Mapped to Curiosity Historical Research (information, sources, and research questions)Mapped to Connections Experimental/Technical WorkMapped to Creating Value Analysis Figure 1. Average Rubric Scores for Final Report color coded by general (yellow), Curiosity(blue), Connections (green), and Creating
identify whether certaininteraction styles better serve specific purposes. These insights are valuable for defining andpracticing research skills for undergraduate and graduate students. Our findings could alsoinform training programs for graduate and undergraduate students as well as for faculty andothers who work with multilevel research teams. 11References[1] B. Latour and S. Woolgar, Laboratory life: the construction of scientific facts, 2nd ed. Princeton, NJ: Princeton University Press, 1986.[2] A. Johnson, Hitting the brakes: engineering design and the production of knowledge. Durham, NC: Duke University Press, 2009.[3] J. Lave and E
). pcaratozzolo@tec.mxAlvaro Alvarez-Delgado, Language Department, School of Social Sciences, Tecnologico de Monterrey, Mexico Alvaro Alvarez-Delgado was born in San Luis Potosi, Mexico. He obtained his PhD in Hispanic Literature from El Colegio de Mexico in 2009 with the thesis, Compa˜neros de viaje (1959): The First Jaime Gil de Biedma. Since 2009, he has been a member of the faculty at Tecnologico de Monterrey, Santa Fe campus, in the Languages Department from the School of Education and Humanities. He is the Coordinator at the Writing Center, Santa Fe Campus. His literary interests are related to literature written by women from the middle ’50’s to the middle ’70’s in Mexico, especially focused on the works by Elena Garro. His
project has a common set ofspecifications that all student groups work toward. After brainstorming and selection of aprototype idea, the teams design their part(s) in SW and begin 3D printing, and redesigniterations. In 2013 - 2015, students carried simple analytical calculations of the performance,although some ambitious students did SW simulations. Starting in 2016, SW simulations were arequired part of the design process. Figure 4Schematic of semester-long design project in Materials Performance. Table 2Team design projects titles and specificationsYear Materials Performance (Fall) Materials Processing (Spring)2013/14 Backpack
University. Prior to that, he was working as a Research Specialist in the Department of Physiology at University of California, San Francisco. He has authored over 85 peer-reviewed publications in journals such as Langmuir, Biomaterials, Journal of Orthopedic Research, Journal of Biomedical Materials Research, etc. and has and h-index of 37. He has also presented his work at numerous national and international level conferences. He received his Ph.D. in Bioengineering from University of Illinois at Chicago in 2003, M.S. in Chemical Engineering from Illinois Institute of Technology, Chicago in 2000 and B.E. in Chemical Engineering from M. S. University in India in 1998.Dr. Kimberly Catton P.E., Colorado State University
recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundationReferences [1] C. Conrad and M. Gasman. Educating a Diverse Nation: Lessons from Minority Serving Institutions. Cambridge, MA: Harvard University Press, 2015. [2] National Science Foundation (NSF), “Science and engineering indicators 2014,” 2014, Available: http://www.nsf.gov/statistics/seind14/ [Accessed: October, 15, 2018]. [3] S. L. Colby, and J. M. Ortman, “Predictions of the size and composition of the U.S. population 2014 to 2060: Population estimates and projects,” U. S. Census Report #P25-1143. Washington, DC
Systematic Innovation in Engineering Education: Ensuring U.S. engineering has the right people with the right talent for a global society. Washington, DC: American Society for Engineering Education.Borrego, M., Culter, S., Prince, M., Henderson, C., & Froyd J. E. (2013). Fidelity of implementation of research-based instructional strategies (RBIS) in engineering science courses. Journal of Engineering Education, 102, 294–425.Borrego, M., Froyd, J. E., & Hall, T. S. (2010). Diffusion of engineering education innovations: A survey of awareness and adoption rates in U.S. engineering departments. Journal of Engineering Education. 99(3), 185-207Bryne, B. M. (2006). Structural equation modeling with EQS
the same subject.Second, build a spreadsheet model to solve the calculated problem to test out your formulasbefore you put them into the LMS question. Have a data block in the spreadsheet that shows thelabels for all problem variables, identifies the randomized parameters by name, and includesyour settings for those parameters’ minimum value(s), maximum value(s), and number ofdecimal places. If your LMS has other potential settings for algorithmic parameters, includethose as well. While the formulas and functions are obviously different for a spreadsheet than anLMS formula answer, this step is still valuable for building the question.Having the parameter value settings worked out in advance makes constructing the calculatedquestion in the LMS
in a course. Examining students’ self-reportedconfusion also allows us to assess their understanding with regard to procedural or conceptualissues in statics. We can distinguish whether students’ questions focus on deeper conceptualissues or on more surface procedural tasks. This distinction is relevant, as recent work suggeststhat conceptual questions are most helpful for improving understanding24.In this work-in-progress, we provide initial findings with respect to students’ capacity foraccurate monitoring in statics. Data are drawn from an ongoing study in which students wereasked to reflect and write about their problem-solving ability in an engineering statics course3.Specifically, they were asked to identify the source(s) of their
@jo&V = s (Eq. 13) r N(.@-(trNq?.@@which is second order. Comparing this result to Equation 2, the identified coefficients agree withthe expected form. The identified parameters correspond to a value of 𝐵 ≈ 0.0688Ns/m, usingthe known values of 𝑀 = 0.57kg and 𝑘 = 15N/m.The identified transfer function for the two-cart system is j(.??(-r w Nq@.(pr s j@?.qqrN-@-q 𝐺-jo&V = x (Eq. 14) r N(.@yqqr w N@(yr s Np.zAprN-@?@which is fourth order, and appears similar to the expected form. The consistency of
National Conference. www.nacua.orgBlank, S., & Dorf, B. (2012). The startup owner's manual: K&S; Ranch.Boh, W. F., De-Haan, U., & Strom, R. (2012). University technology transfer through entrepreneurship: faculty and students in spinoffs. The Journal of Technology Transfer, 1-9.Carney, S. (2001). Faculty Start-Ups: The Tangled Web. Paper presented at the National Association of College and University Attorneys. www.nacua.orgCreed, C. J., Suuberg, E. M., & Crawford, G. P. (2002). Engineering Entrepreneurship: An Example of A Paradigm Shift in Engineering Education. Journal of Engineering Education, 91(2), 185-195.Duderstadt, J. J. (2001). Preparing Future Faculty For Future Universities. Paper
; Sheppard, S., & Atman, C. J., & Chachra, D. (2011, June), Motivation Makes a Difference, but is there a Difference in Motivation? What Inspires Women and Men to Study Engineering? Paper presented at 2011 Annual Conference & Exposition, Vancouver, BC. https://peer.asee.org/1881613. Canney, N. E. and Bielefeldt, A. R. (2015). Differences in Engineering Students’ Views of Social Responsibility between Disciplines. Journal of Professional Issues in Engineering Education and Practice, 04015004.14. Watson, M., Ghanat, S., Michalaka, D., Bower, K., Welch, R. (2015) Why Do Students Choose Engineering? Implications for First-Year Engineering Education. Paper presented at 2015 FYEE Conference, Roanoke, Virginia
multimedia learning principles in production. 4. Choose the appropriate interactive activities for your video. 5. When determining an appropriate video length, somewhere in the range of 5-15 minutes is recommended. 6. Make educational video production a team effort rather than a solo activity. 7. Don’t rely just on online videos.Table 3. Re-statement of the seven research-based recommendations for producing onlineeducational videos.References1. Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: seven research-based principles for smart teaching. John Wiley & Sons.2. Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: instructional
-Mona, I. & Abd-El-Khalick, F. (2006). Argumentative discourse in a high school chemistry classroom. School Science and Mathematics, 106(8), 349–361. http://doi.org/10.1111/j.1949- 8594.2006.tb17755.x18. Latour, B. & Woolgar, S. (1986). An anthropologist visits the laboratory. In Labor life: The construction of scientifc facts (pp. 43–103). Princeton University Press.19. Fink, F. K. (2001). Integration of work based learning in engineering education. In Frontiers in Education Conference, 2001. 31st Annual. Reno, NV: IEEE. http://doi.org/10.1109/FIE.2001.96374720. Jonassen, D. & Shen, D. (2009). Engaging and supporting problem solving in engineering ethics. Journal of Engineering Education, 98(3), 235
Paper ID #15385Systems Engineering and Capstone ProjectsDr. Fred J. Looft, Worcester Polytechnic Institute Prof. Looft earned his B..S, M.S. and Ph.D. degrees in Electrical Engineering at the University of Michi- gan. After a brief period on industry, he joined the faculty of WPI 1n 1980 where he is now a professor in the ECE department and a founder of, and Academic Head of the Systems Engineering program. His interests include projects based education, curriculum development, international study abroad programs and mentoring, and autonomous robotic systems.. c American Society for Engineering
usto reallocate staff resources from grading to providing walk-in clinic hours to serving studentswho did have difficulties.A typical year's operation saw over 122,000 problems graded automatically – not including theadditional grading resulting from student retries. We attempted to keep the entire class on asingle schedule of due dates, but this imposed significant swings in the load on the autogradingsystem. Fortunately our system administrators were able to deploy adequate server power tohandle our size class. Nevertheless, system performance requires careful attention in courseswhere significant resources are needed for autograding.Lessons learned from the first version of the course – limitations of the original formatMaple T.A.'s grading
Paper ID #13669A Mixed Instructional Methods Approach to Teaching a Circuits and Instru-mentation CourseMr. Stephen Keith Holland, James Madison University S. Keith Holland received his PhD in Mechanical and Aerospace Engineering from the University of Virginia in 2004. He served as the Vice President for Research and Development with Avir Sensors, LLC prior to joining the Department of Engineering at James Madison University (JMU). At JMU, he developed statics, dynamics, circuits, instrumentation, controls, renewable energy, and engineering study abroad courses. His current research interest include material development
factors that affect the schedule. The learners’ motivation was measured through the useof an adapted pre- and post-test called the OnLine Motivation Questionnaires.[21] The assessmentresults have proven the VCS3’s capability to motivate the students and increase their generalknowledge of the construction planning process.[2] However, while the VCS positively affectedstudents’ overall learning and motivation, the results still do not fully reveal the VCS3simulator’s ability to promote higher order thinking skills.3. Instructional design of the virtual construction simulator 4 The past experiences of the VCS3 have demonstrated that the game has great educationalpotential. This potential is being addressed with a new phase of research and
sevenquestions on the topic of drift. Broad questions were used first (e.g., #1 and #2 below), moving to more specific questionsthat capture other aspects of the phenomenon (e.g., #3 below). Page 26.558.5 1. Describe the movement of the electron(s) in the semiconductor when the electric field is on and off. Use as much detail as possible. 2. Based on your knowledge of physics and electrons, what determines how and where the electrons move in the semiconductor when the electric field is on/off? Use as much detail as possible. 3. Imagine an electron, in a similar semiconductor, under the same scenario, moving again. How
, give us apiece of advice that is important to you, and use 6 to 10 pictures to tell us a story.The intention of Gaver et al.’s use of cultural probes was to support creativity and imagination,while amplifying the participants’ existing pleasures. Cultural probes also explored howtechnology could support the participants’ values. Image 1- Left: a disposable camera with requests for specific pictures. Right: postal cards.Key characteristics of Cultural Probes:Cultural probes have been widely adopted and adapted by several industrial and academicresearch and design groups. Many researchers took the original cultural probes as an inspiration
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
.'#:(*'# .%4,%..',%4#3&*):< !>"#G-6.#:(*#2.1-+-).;,+-11:#0'.0-'.)#2('#&5.#.%4,%..',%4#+(*'3.3# -&5.#B%6.'3,&:#(2#C,'4,%,-< !D"#?(#:(*#2..1#-#0-'(2#&5.#B%,6.3,&:#(2#C,'4,%,-A3#R+5((1#(2# S%4,%..',%4#-%)#@001,.)#R+,.%+.#+(;;*%,&:< !F"#$2#&5.'.#7-3#(%.#&5,%4#:(*#+(*1)#+5-%4.#-L(*&5.#&'-%32.'# 0'(+.33#:(*#./0.',.%+.)J#75-(*1)#&5-L.< Findings Students interviewed represented John Tyler and Piedmont Valley, both community colleges of the Virginia Community College System. Each community college has a Guaranteed Admission Agreement in place
students to 4. Re-tell the performance of a possible solution. 5. Analyze possible solution(s) according to several types of evidence, including results of physical tests, data from scientific investigations, information from external sources, and critique by other children or adults. 6. Purposefully choose how to move forward to improve the proposed solution.Table 1. Alignment of proposed definition of reflective decision-making in engineering withsupporting research and elementary engineering curriculum learning tasksElements of reflective decision- How engineering design practitioners Related learning tasks in the EiEmaking exhibit the element curriculumDuring initial
addition of a single cubic term whosecoefficient is a . This fact renders the cubic law as a simple extension of the traditional result.Some sample trajectories are displayed in Figs. 4 and 5 for (respectively) o 45 and 60 . Thetrajectory cases correspond to 1.5 , 1.0 , 0.5 , and 0.0 in each figure. Also, vo 10 m/s andg 9.81 m/s 2 were utilized to generate these particular results. These figures were created withthe chart-production capabilities available within an EXCEL® workbook. The solid and dashedcurves identify results generated with the approximate and exact solutions (respectively), but itwas not possible to obtain experimental results for a comparison with the exact and approximateresults, given the limitations imposed for
-generation peers when a given situation causes opposing valuesto confront, such as prioritizing familial responsibilities versus individual responsibilities.Further analyses of the survey and other measures, such as the VAI, will help better understandthese connections.Many of the FIG mentors commented how much they enjoyed incorporating the DEI panel torepresent a more diverse group of students. Though many of the same themes reoccurred fromone panel to another, such as seeking tutoring services and getting involved on campus, eachgroup of panelists was dynamic and unique.The panelists were interviewed to get their perceptions about the DEI panel(s) and suggestionsfor future panels. Interesting subjects emerged from the interviews that offered some
concepts to improve on their quizzes and tests andthe class as a whole.Acknowledgements This material is based upon work supported by the National Science Foundation underGrant No. #1254006. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. Page 26.239.11References1 Nottis, K. E. K., M. Prince, M. Vigenat, S. Nelson, and K. Hartsock, 2009, “Undergraduate Student’sUnderstanding of Heat, Temperature and Radiation,” Northeastern Educational Research AssociationAnnual Conference Proceedings.2 Miller, R.L
framework will serve as a case study of how CMprograms could implement BIM education more effectively and efficiently.References1. Azhar, S. (2011). Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry. Leadership and Management in Engineering, 11(3), 241-252.2. Badger, W. and Robson, K. (2000). Raising Expectations in Construction Education. Proceedings of the 6th Construction Congress, Orlando, FL, February 20-22, 2000, pp 1151-1164.3. Barison, M.B. and Santos, E.T. (2010a). An Overview of BIM Specialists. Proceedings of the 2010 International Conference on Computing and Civil and Building Engineering, Nottingham, UK, June 30 - July 2, 2010.4. Barison, M.B., and Santos, E.T
rate is controlled by changing the position of a ball valvemounted before the meter. Calibration showed that this meter’s rotation rate increases linearlywith increased flow rates within this range tested. Details of the experimental apparatus areprovided in Appendix A and the lab manual is provided in Appendix B. Mass Flow Rate vs Electronic Meter Reading 0.40 0.35 Mass Flow Rate (kg/s) 0.30 0.25 0.20 0.15 0.10