percentage (25-30%) of the students in a courseexhibit unsatisfactory performance, i.e., they do not meet a minimum required standard, extrameasures must be taken. For example, the instructor may be asked to devise a plan of improvingthe course for its next offering.Table 2. ABET student outcomes [7] and their equivalent CEAB graduate attributesNo. Student outcome Equivalent graduate attribute (s)1. an ability to identify, formulate, and solve complex engineering 2); 1) is implied problems by applying principles of
Journal of Higher Education 2016, 87 (5), 605-634.5. Reith, F.; Seyfried, M., Balancing the Moods: Quality Managers’ Perceptions and Actions Against Resistance. Higher Education Policy 2018, 32 (1), 71-91.6. Kolb, D. M.; Williams, J.; Frohlinger, C., Her Place at the Table: A Woman's Guide to Negotiating Five Key Challenges to Leadership Success. Wiley: 2010.7. Kolb, D. M.; Coolidge, G. G., HER PLACE AT THE TABLE. Journal of State Government 1991, 64 (2), 68-71.8. Kolb, D. M., Her Place at the Table: Gender and Negotiation after Trump. Negotiation Journal 2019, 35 (1), 185-189.9. Slank, S., Rethinking the Imposter Phenomenon. Ethical Theory and Moral Practice 2019, 22 (1), 205-218.10. Silbiger, N. J.; Stubler, A. D., Unprofessional
College (e.g., engineering career fairs) or University, ensuring equal representation on a departmental and/or major level. • When creating opportunities, it is important to understand that larger student attendance does not equate to larger major enrollment (via student retention or addition). Events that feature a smaller, but more focused student audience and events that feature smaller student-to-faculty ratios were more successful in terms of retaining and adding students. • The source(s) of potential added students should be recognized when creating major exploration opportunities. Added CE students most commonly intended to major in mechanical engineering while added EVEG
, mind, experience, and school: Expandededition. National Academies Press, 2000.[4] C.C. Bonwell and J.A. Eison, "Active Learning: Creating Excitement in the Classroom." 1991ASHE-ERIC Higher Education Reports. ERIC Clearinghouse on Higher Education, 1991.[5] M. Prince, "Does active learning work? A review of the research." Journal of engineeringeducation, 93(3), pp.223-231, 2004.[6] C. Brame, Active learning. Vanderbilt University Center for Teaching, 2016.[7] S. Freeman and S.L. Eddy et al, "Active learning increases student performance in science,engineering, and mathematics." Proceedings of the National Academy of Sciences, 111(23),pp.8410-8415, 2014.[8] E. Seymour and N.M. Hewitt, Talking About Leaving: Why Undergraduates Leave theSciences
six topic areas and develop personalized learning plans to overcome these areas ofweakness. These lesson plans consisted of a subset of six learning modules related to theprerequisite material that students were required to complete outside of the classroom. Each topiccovered on the pre-test was associated with a specific module and students were only assignedcomplete the modules associated with corresponding question(s) that they did not answercorrectly. For example, if a certain student correctly answered the questions on descriptivestatistics and summing variables, they were then only assigned the modules associated with thestandard normal distribution, confidence intervals, z-test of equality and chi-square test. Each ofthe modules were
Mechatronics (REM), IEEE, pp. 69-74, 2018.[8] S. N. Cubero, "Developing the Creativity and Design Skills of Mechatronic Engineering Students with Labs and Robot Competitions," Machine Vision and Mechatronics in Practice, J. Billingsley and P. Brett, eds., pp. 287-306, Berlin, Heidelberg: Springer, 2015.[9] C. A. Berry, S. L. Remy, and T. E. Rogers, “Robotics for All Ages: A Standard Robotics Curriculum for K-16,” IEEE Robotics & Automation Magazine, vol. 23, no. 2, pp. 40-46, 2016.[10] S. Nilsson, “Enhancing Individual Employability: The Perspective of Engineering Graduates,” Education + Training, vol. 52, no. 6/7, pp. 540-551, 2010.[11] C. Mohtadi, O. McAree, and J. Scholosser, “Bridging the Skills Gap in STEM
learning objectives, instructional strategies, and assessments forsustainable infrastructure topics. Subsequent problem-based learning activities are being revisedand improved.AcknowledgmentsThis work was funded by the Scholarship of Teaching and Learning grant from the University ofNorth Carolina at Charlotte.References[1] A. Steinemann, "Implementing Sustainable Development through Problem-Based Learning:Pedagogy and Practice," Journal of Professional Issues in Engineering Education and Practice,vol. 129, no. 4, pp. 216-224, 2003, doi: 10.1061[2] S. A. Gallagher, B. T. Sher, W. J. Stepien, and D. Workman, "Implementing Problem-BasedLearning in Science Classrooms," School Science and Mathematics, vol. 95, no. 3, pp. 136-146,1995, doi: 10.1111/j
University.References[1] C. Seemiller and M. Grace, “Educating and engaging the next generation of students,” About Campus, vol. 22, pp. 21-26, 2017.[2] J. Cruz and N. Kellam, “Beginning an Engineer’s Journey: A Narrative Examination of How, When, and Why Students Choose the Engineering Major,” Journal of Engineering Education, vol. 107, no. 44, pp. 556-582, 2018.[3] P. C. Rickes, “Generations in flux- How Gen Z will continue to transform higher education Space,” Planning for Higher Education Journal, vol. 44, no. 4, 2016.[4] L. S. Nadelson et al., “Knowledge in the making: What engineering students are learning in makerspaces,” in Proceedings, 2019 ASEE Annual Conference and Exposition, June 2019, Tampa, FL.[5] R. M. Carbonell, M. E
]. Computationalthinking (CT) as defined by Jeannette Wing, who first brought it to the attention of the computerscience education community in 2006 [2], and later refined the definition, “is the thought processesinvolved in formulating a problem and expressing its solution(s) in such a way that a computer-human or machine-can effectively carry out” [3]. In other words, is a methodology that can beemployed to plan and formulate the solution to a problem so that the steps necessary can be carriedout by either a computer or a person. One characterization that is used to define the CT involvesthe following four core cornerstones: 1) decomposition, 2) pattern recognition, 3) abstraction, and4) algorithms [4]. Decomposition involves breaking the original problem into
this field including learning and predictive analytics for student success, S-Stem NSF grant, Research Practitioner Partnership NSF grant, and Spatial Reasoning Impact Study in CS1.Nasrin Dehbozorgi, University of North Carolina at Charlotte Researcher and Ph.D. candidate in the department of Computer Science at University of North Carolina at Charlotte. Conducting research in the area of CSE by applying AI/NLP to do learning analytics, devel- oping models to operationalize attitude in collaborative conversations and pedagogical design patterns.Aileen Benedict, University of North Carolina at Charlotte Aileen Benedict is a Ph.D. student and GAANN Fellow at UNC Charlotte, who has been mentored in teaching since 2016
and biomedical engineering) get ademonstration of this sculpture so that they can appreciate the beauty of the Civil Engineeringdiscipline when they learn through this sculpture.Conclusion: Students learn best by doing. Teaching using demonstration is a significant part of thepopular and proven ExCEEd teaching model. The combination of a student independent study, aclassroom demonstration and an addition to the decor of the university is hitting a trifecta. Thepresented project helped a civil engineering student to learn multiple aspects of civil engineeringusing a hands-on project. In addition, the result of the project is used in multiple civilengineering courses.Acknowledgement:We would like to thank Alumnus, Mr. John S. McGrath
mathematicalmodeling and developing specific content knowledge, and how engineering can provide avaluable context for the application of mathematical modeling.Introduction Mathematical modeling is a critical component of math, science, and engineeringeducation [1]–[7]. Both the Common Core State Standards for Mathematics (CCSSM) and theNext Generation Science Standards (NGSS) emphasize the importance of mathematicalmodeling [1]. Mathematical modeling in the classroom helps to develop the critical thinking andmath skills required for engineering [2]. It allows students to “revise their preconceptions and…understand the underlying principle[s] of mathematics” [8] and integrate topics similar toprofessionals in the field [1]. Students are expected to
. 9. R. J. T. Klein, R. J. Nicholls, F. Thomalla, “Resilience to natural hazards: How useful is this concept?” Environmental Hazards, vol. 5, pp.35-45, 2003. 10. C. Folke, S. Carpenter, T. Elmqvist, L. Gunderson, C. S. Holling, B. Walker, “Resilience and Sustainable Development: Building Adaptive Capacity in a World of Transformations,” Ambio, vol. 31, no. 5, pp. 437-440, 2002. 11. C. Béné, A. Newsham, M. Davies, M. Ulrichs, R. Godfrey-Wood, “Review Article: Resilience, Poverty, and Development,” Journal of International Development, vol. 26, pp. 598-623, 2014. 12. J. Park, T. P. Seager, P. S. C. Rao, M. Convertino, I. Linkov, “Integrating Risk and Resilience Approaches to Catastrophe Management in
NASA NSF NIST Labs Tech* of Science Research Science and Research Ag Grants Survey Tech Copyright © 2019 American Association for the Advancement of Science*Nuclear, fossil, renewables, efficiency, grid, ARPA-E. Source: agency budget documents and appropriations. | AAAS 2020 3 Select Federal S&T Spending Since FY 2010 Percent change from FY10 levels, constant dollars30
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., Reeping, D. & Spingola, E.: “A Taxonomy for Introduction to Engineering Courses,” International Journal of Engineering Education, Vol. 35, No. 1, 2018.2. Honor Code Policy and Manual, URL: https://honorsystem.vt.edu/honor_code_policy_test.html, accessed 2/1/20203. A Theory for Detecting Software Similarity, URL: https://theory.stanford.edu/~aiken/moss/, accessed 2/1/2020.4. Roth, N.L. and McCabe, D.L., "Communication Strategies for Addressing Academic Dishonesty," J. College Student Development, vol. 36, n. 6, 1995, pp. 531-541.5. McCabe, D.L. and Makowski, A.L., "Resolving Allegations of Academic Dishonesty," About Campus, March-April, 2001, pp. 17-216. Carpenter, D., Harding, T., Finelli, C., Montgomery, S
● Identify areas of themes, ideas, or concepts that designers might need to be aware of when designing Learn about different design models, specific research results Session 3 Design Models and about design processes, and visualization of design. The goals of &4 Design Research these sessions are to: Findings Investigation ● Investigate how we might help designers notice/be more aware of “something” within the design process Choosing one (or couple of) idea(s), concept(s), and theme(s) Session 5 Brainstorm Session that are interesting, most frequent, and or exciting relating to
demonstrations of and introductions to two engineering-specificresources, Engineering Village and Knovel, and two general science resources, Web of Scienceand ScienceDirect. The session ended with a series of assessment questions and a briefintroduction to citation management software. See Table 1 for more detail.Table 1: Lesson Plan and Assessment Questions for WorkshopsLesson Plan Topics Covered Changes for 2019- Assessment 2020 academic year question(s), 2019-2020Introduction to How to get help from a librarian N/A N/Athe Library
National Academy ofEngineering (NAE) and the National Research Council (NRC) defined technological literacybroadly as encompassing three dimensions: knowledge; ways of thinking and acting (criticallyanalyzing, assessing); and capabilities.12Since as early as the 1970’s, environmental science educators and education professionals havealso emphasized the application of skills and knowledge in attempting to set criteria forenvironmental literacy. Environmental literacy lacks a clear definition, but the TbilisiDeclaration of 197715 was modified in 199016 to state: “Environmental literacy is a basicfunctional education for all people, which provides them with the elementary knowledge, skills,and motives to cope with environmental needs and contribute
Management Science, APR, vol. 28, no. 2, pp. 143-153. 6. Reeves, G, and E Hickman, (1992) "Assigning MBA Students to Field Study Project Teams: A Multicriteria Approach". Interfaces, Linthicum, Sep/Oct, vol. 22, no. 5, pp. 52-58. 7. Glover, F, and F Laguna. (1997) Tabu Search. Kluwer Academic Publishers, July, pp. 408pp. 8. Weitz, R, and S Lakshminarayanan. (1997) "An empirical comparison of heuristic and graph theoretic methods for creating maximally diverse groups, VLSI design, and exam scheduling". Omega, Oxford: Aug, vol. 25, no. 4, pp. 473-482. 9. Chen, S, and L Lin. (2004) "Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering". IEEE Transactions
condensed matter. Positrons can be obtained from β+-decayof radioactive isotopes or from nuclear reactions. For the investigation of the electronicstructure of defects in solids they are implanted into the sample and move through themedium until they reach thermal equilibrium. As the antimatter counterpart to the electron,the positron remains only a short time (10-10 s) in the sample before annihilating with anelectron under emission of annihilation gamma rays that escape the system without anyinteraction. The spectrum of these gamma quanta holds information about the electronicenvironment around the annihilation site 9. The principle of the method lies in the analysis ofthe positron annihilation line shape, which directly corresponds to the
AC 2007-1113: CRAFTING AN INTERNATIONAL ROAD MAP TO GLOBALLEARNING AND PROJECT MANAGEMENTBahman Motlagh, University of Central Florida Bahman S. Motlagh is an associate professor at the University of Central Florida and the Program Coordinator for Information System Technology program. He received his B.S. from Istanbul Academy of Sciences, M.S.Cp.E, and Ph.D. degrees in Computer Engineering from the University of Central Florida, in 1993 and 1997, respectively. He is a member of the American Society for Engineering Education (ASEE), and senior member of the Institute of Electrical and Electronics Engineers (IEEE). He has served as Chairman of the IEEE Cape Canaveral section and is currently
, (Blacks burg, VA: College of Engineering).14. Michael Alley, Jenny Lo, and Whitney Edmister, 2005, Promoting undergraduate research through a research option in a technical communication course, (Blacksburg, VA).15. Michael Alley and Alicia Williams, 2005, A pilot symposium to highlight undergraduate research in engineering, 2005 American Society for Engineering Education Conference & Exposition, paper 1267 (Portland OR).16. Michael Alley and Kathryn A. Neeley, 2005, Rethinking the design of presentation slides: a case for sentence headlines and visual evidence, Technical Communication, 52 (4): 417–426.17. Tracy P. Ng, W. R. Bussone, and S. M. Duma, 2006, The effect of gender and size on linear accelerations of the head
just ethics,which we believe to be both a strength (in light, for instance, of Dvorak and Fulle’s suggestionthat ethics instruction is improved when it’s placed in larger social contexts4) and a necessity (inlight of our institution-specific circumstances).Assessment designIn designing and conducting our assessment of this course, we have come to agree with Shumanet al.’s observations about the challenges of assessing “Professional Skills”-type learningoutcomes9. With limited funding for this project (in the form of a small, University-awardedstudent/faculty fellowship for independent research), we were unable to employ some of themore sophisticated assessment techniques Shuman and his colleagues suggest. Nevertheless, weimplemented an
Teaching engineering: A beginner's guide, M.S. Gupta, Editor. 1987, IEEE Press: New York.10. Gibbs, G., Using assessment strategically to change the way students learn, in Assessment matters in higher education: choosing and using diverse approaches, S. Brown and A. Glasner, Editors. 1999, The Society for Research into Higher Education & Open University Press: Buckingham, UK & Philadelphia, PA. p. 41-53.11. Mehta, S.I. and N.W. Schlecht, Computerized assessment technique for large classes. Journal of Engineering Education, 1998. 87(2): p. 167-172.12. Black, P. and D. Wiliam, Inside the black box: raising standards through classroom assessment. Phi Delta Kappan, 1998. 80(2): p. 139-148.13
Priviledges and Watchlist Statistic Black List s and (IP or Block Loggin Username ) User Group (Different Projects ) PACE Global Vehicle
Solving: The Path-Mapping Approach,” Cognitive Science, Vol. 25, 2001, pp. 67-110.[14] Mayer, R. E., “Cognitive, Metacognitive, and Motivational Aspects of Problem Solving,” Instructional Science, Vol. 26, 1998, pp. 49-63.[15] Cho, K.L., and D. H. Jonassen, “The Effects of Argumentation Scaffolds on Argumentation and Problem Solving,” Educational Technology: Research & Development, Vol. 50, No. 3, 2002, pp. 5-22.[16] Dunkle, M.E., G. Schraw, and L. D. Bendixen, “Cognitive Processes in Well-Defined and Ill-Defined Problem Solving,” Paper presented at the annual meeting of the American Educational Research Association, San Francisco, USA, 1995.[17] Hong, N.S., D. H. Jonassen, S. McGee, “Predictors of Well
.Hoyles, C. and Sutherland, R. Logo Mathematics in the Classroom. Routledge, Chapman and Hall, New York, NY, 1989.Papert, S. Mindstorms: Children, Computers and Powerful Ideas. Basic Books Inc., New York, NY, 1980.Papert, S. Children’s Machine: Rethinking School in the Age of the Computer. Basic Books Inc., New York, NY, - 1993.Watt, D. Learning with Logo. McGraw-Hill Book Company, New York, NY, 1983.Watt, M. & Watt, D. Teaching with Logo: Building Blocks for Learning. Addison-Wesley Publishing Co., Menlo Park, CA, 1986.Weir, S. Cultivating Minds: A Logo Casebook. Harper & Row Publishers, New York, NY, 1987.Appendix B: Example of a
cross walking techniques continue to help us make progress while providing us withthe flexibility to adapt to rapid changes in the volatile environment.Bibliography1. M. Bakia, “The Cost of Computers in Classrooms: Data from Developing Countries.” Mimeograph, Page 13.39.14Washington: The World Bank. 2000.2. E. Brewer, M. Demmer, B. Du, M. Ho, M. Kam, S. Nedevschi, J. Pal, R. Patra, S. Surana, K. Fall, “The Case forTechnology in Developing Regions,” IEEE Computer Society, June 2005.3. A. Cawthera, “Computers in Secondary Schools in Developing Countries: Costs and Other Issues” (2001)http://www.dfid.gov.uk/Pubs/files/computersinsecschoolsedpaper43