based on the data ofparticipation rates have driven the MSU’s PAL program to mutate towards a revised model wehave of a common setting for all PAL courses in recognized and stable locations on campus. Inkeeping with other programs at MSU, we are calling this the “neighborhood approach” to thePAL component. The table in Appendix B shows the chronology of PAL developments at MSUand how the results noted for one semester leads to component changes that follow.Until Fall 2010, we did not have results for objective learning outcomes from PAL/SI. In Fall2010, we reached a stage of stability with the PAL program that supported studying the learningoutcomes for PAL at MSU, and concurrently studying the learning outcomes for SI at LCC.Preliminary
more about this? b. If they do not mention media… Are there any media sources that influenced the picture that you have in your head? Media Articles are show to participants 4. So, if you had a picture in your head just based on these media articles, could you describe that picture to me? 5. From what you just watched...can you tell me about similarities and differences between the picture in your head and the one that is presented in these articles? 6. Would you identify yourself as an engineer?Selection of Media Articles/Bias A significant portion of the interview process centered on the use of several mediaarticles. The media articles were meant to represent a typical portrayal
Paper ID #6665Outcome, Economic and Operational Benefits of Hybrid Courses - A PublicResearch University PerspectiveDr. David J. Dimas, The University of California, Irvine Dr. Dimas has over 25 years of experience which centers on consulting in simulation and design and developing and teaching a curriculum of related engineering analysis and product development courses in both commercial and academic settings. He served in a number of top-level management positions at both PDA Engineering and MSC Software including director of training services, customer support, educational sales and product documentation in the
unstructured epistemological realm of its own in which “anyone has a right to his own opinion,” a realm which he sets over against Authority’s realm where right-wrong still prevails, or (b) the student discovers qualitative contextual relativistic reasoning as a special case of “what They want” within Authority’s realm. Position 5: The student perceives all knowledge and values (including authority’s) as contextual and relativistic and subordinates dualistic right-wrong functions to the status of a special case, in context. Position 6: The student apprehends the necessity of orienting himself in a relativistic world through some form of personal Commitment (as distinct from unquestioned or unconsidered commitment to simple belief in
-148.6. M. J. Karcher, G. P. Kuperminc, S. G. Portwood, C. L. Sipe and A. S. Taylor, Mentoring programs: A framework to inform program development, research, and evaluation, Journal of Community Psychology, 34(6), 2006, pp. 709-725.7. H. J. Mitchell, Group mentoring: Does it work?, Mentoring & Tutoring, 7(2), 1999, pp. 113-120.8. E. S. Scott and S. D. Smith, Group mentoring: A transition-to-work strategy, Journal for Nurses in Staff Development, 24(5), 2008, pp. 232-238.9. T. Waller, S. Artis and B. Watford, The Pact: A framework for retaining 1st year African- American engineering men, ASEE Annual Conference & Exposition June 24-27, 2007 of Conference.10. S. Davis, G. Jenkins and R. Hunt
. Edward D. McCormack, ‘The Use of Small Unmanned Aircraft by Washington State Department of Transportation,’ Research Report Agreement T4118, Task 04, prepared for Washington State Transportation Commission, Department of Transportation, June 2008 3. Suman Srinivasan, et. al., ‘Airborne Traffic Surveillance Systems – Video Surveillance of Highway Traffic,’ VSSN’04, ACM 1-58113-934-9/04/0010, New York, October 2004 4. Southern Polytechnic State University Honors Program [http://www.spsu.edu/honors/] 5. Raymond B. Landis, Studying Engineering: A Roadmap to a Rewarding Career, Discovery Press, 3rd Page
. Page 23.12.11 (a) Front panel(b) Zero-crossing algorithm for speed detection Page 23.12.12 Figure 6. LabView Program.III. Course Outcome AssessmentThe assessment presented here with a total of 24 student responses (student distribution: 33%EE, 42% ME minoring in EE, and 25% regular ME) is based on our collected data from teachingthe dynamic system modeling and analysis/feedback control system in spring 2011 and spring2012. At the end of each semester, we conducted a student self-assessment. A student survey wasgiven before the final exam to ask each student to evaluate his/her achievement for each courselearning outcome listed in Table 1. Students were asked to make the
ABET standards and major coursework.Table 2 (next page) Listing of courses in the undergraduate Leadership Engineering program Page 23.982.11Page 23.982.121. Florman, Samuel C. (2010). The Introspective Engineer. New York City: St. Martin's, 1996. Graham, R.,Crawley, E., & Mendelsohn, B. (2010). Engineering leadership education: A snapshot review of international goodpractice. [White paper]. Retrieved from http://web.mit.edu/gordonelp/elewhitepapter.pdf2. Schoephoerster, R.T. and Golding, P. (2010). A New Program in Leadership Engineering, TransformingEngineering Education, Creating Interdisciplinary Skills for Complex Global Environments, Paper #1569268596,Dublin, Ireland, April 6-9, 2010.3. Muller, G. 2013, Didactic
Paper ID #6124Project based learning in engineering economics: Teaching advanced topicsusing a stock price prediction modelingDr. Lizabeth T Schlemer, California Polytechnic State University Page 23.991.1 c American Society for Engineering Education, 2013 Project based learning in engineering economics: Teaching advanced topics using a stock price prediction modelAbstract: A graduate level advanced engineering economics class taught at CaliforniaPolytechnic State University, San Luis Obispo, includes a thorough
thecorrect way to perform an engineering design process, but it strips away opportunities by notallowing students to be more engaged and learn by doing it themselves. Students in theapprentice model learn by observing, while students in the autonomous model learn by doing.Furthermore, as these groups of students continue to develop, we can suggest that those whoparticipated in a more heavily mentor team may become dependent and mold into a teammember, whereas a student who participated in a less mentorship team is more likely to becomeindependent and develop into a team leader. Page 23.1130.12References1. Barker, S. B., Ansorge, J. (2007). Robotics
of lesson plans developed through the internship experience that can be shared with colleagues to provide classroom-ready materials designed to be delivered through a STEM-learning platform. The teachers are required to incorporate two different aspects within the lesson plan design. a. The teacher must use new knowledge gained about the EDP and 21st century skills within the delivery of the lesson plan. The lesson must be implemented in a new manner that models how the corporation uses processes to solve technological problems. b. The teacher must use information specific to the companies products and infrastructure when design the content. The teacher must find a
of the data provided by the raters.Each lab report was evaluated in a double-blind read by three trained, graduate-levelevaluators: two from UA, and one from UT-Tyler. To prepare for these evaluations,evaluators were trained, calibrated, or ‘normed’ via double-blind reads of several lab reports Page 23.1173.7organized in the same manner as shown in the Coach: Abstract, Background, Methods andMaterials, Results, Discussion, Conclusion, References, Appendices. Raters read accordingto the four-point rubric shown below, and were determined to be in ‘agreement’ if theirevaluations in any category (a) were identical or (b) differed by a factor of one
Extended Abstract with Poster Reverse Engineering through Simulation of a Conceptual Design Process of Supermarine Spitfire George Kitamura, Kristin Milam, Elvin Hii, Chris Kniffin, Alexander Graves, Amit Oza, Bernd Chudoba Department of Mechanical and Aerospace Engineering University of Texas at Arlington AbstractThis paper is a report documenting the experience of participating in a Senior Design Capstonecourse in which the Supermarine Spitfire Mk Vb was reversed engineered. Instituting multi-disciplinary analysis, first
heightened curriculum demands, it is important that engineering students are readyand willing to spend time preparing for class by reviewing material, completing assignments andstudying. The purpose of this study was to gain a better understanding of behaviors and attitudestoward homework and studying that students developed in high school and whether thosebehaviors and attitudes changed when students were faced with more challenging classes and theincreased distractions college brings.The motivation for this study came from the results of a preliminary study that analyzed datafrom the Cooperative Institutional Research Program (CIRP) Freshman Survey taken by a cohortof engineering students in 2010 at the J. B. Speed School of Engineering at
Program Learning Outcomes 2 Outcomes a-k Outcomes a-k Outcomes Outcomes Analytical Ability a,c,f 1,2,4 Oral Communication e,g 6 Teamwork e,f 6,7 Written Communication e,g 6 Project Management b,e 6,7 Visual Communication e,g 6 Math
Teacher, 1992, 141-158.5. A. Elby, American Journal of Physics, 1999, S52.6. R. M. Felder and R. Brent, Journal of Engineering Education, 2005, 57-72.7. Merit Review Broader Impacts Criterion: Representative Activities: 2007, http://www.nsf.gov/pubs/gpg/broaderimpacts.pdf.8. Transforming Undergraduate Education in Science, Technology, Engineering and Mathematics, http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5741, Accessed January 1, 2013, 2013.9. C. Crouch, J. Watkins, A. Fagen and E. Mazur, Research-Based Reform of University Physics, 2007.10. A. Fagen, C. Crouch and E. Mazur, The Physics Teacher, 2002, 206-209.11. M. Koretsky and B. Brooks, Chemical Engineering Education, 2012, 46, 1, 289-297.12. E. M
. November 2011. ISBN13: 9781613501863 5. E.W. Maby, A.B. Carlson, K.A. Connor, W. C. Jennings, P.M. Schoch, “A Studio Format for Innovative Pedagogy in Circuits and Electronics,” Proc. of 1997 Frontiers in Education Conference, 12/1997. 6. J. M. Wilson, “The CUPLE Physics Studio,” The Physics Teacher, Vol. 32, p518, 1994. 7. D Millard, M Chouikha & F Berry, “Improving student intuition via Rensselaer’s new mobile studio pedagogy” ASEE Annual Conference, 2007, AC 2007-1222. 8. R W Hendricks, K M Lai & J B Web, “Lab-in-a-box: Experiments in electronic circuits that support Page 23.576.13
project management tools to develop implementation strategies, characterize contemporary technology projects, understand system perspective of projects, align projects with strategic objectives and learn advanced tools and techniques used in projects. Examples and case studies from a wide range of fields are utilized.To be admitted into the program a student must meet the following prerequisites: a) 1 SemesterObject Oriented Programming (sophomore level or above); b) 1 Semester Statistics/Probability(sophomore level or above); and c) 2 Semesters Calculus (Differential and Integral), or 1Semester discrete mathematics or numerical methods (sophomore level or above).Table 5
Selves Among Ninth Grade Latino Youth. Applied developmental science, 2002. 6(2): p. 62-72.32. Shepard, B., Creating selves in a rural community. Connections, 2003. 3: p. 111-120.33. Lee, S.J., Expecting to Work, Fearing Homelessness: The Possible Selves of Low-Income Mothers. Journal of applied social psychology, 2009. 39(6): p. 1334-1355.34. Robinson, B.S., Motivational attributes of occupational possible selves for low-income rural women. Journal of Counseling Psychology, 2003. 50(2): p. 156-164.35. Creswell, J.W., Qualitative inquiry & research design : choosing among five approaches. 2nd ed2007, Thousand Oaks: Sage Publications.36. Lent, R.W. and S.D. Brown, On conceptualizing and assessing social cognitive
Paper ID #7538Measuring intercultural sensitivity: A case study of the REU program atUPRMDr. Saylisse Davila, University of Puerto Rico, Mayaguez Campus Dr. D´avila research interests includes the application and development of data mining methods in the early detection of anomalies. She is currently working on the development of a variety of methods in- volving feature selection and pseudo-permutation tests in the early detection of disease outbreaks. Her current work also targets high-dimensional approaches to characterize anomalies with applications to public health surveillance and statistical process control. Other
with Impact. Washington, D.C.: American Society for Engineering Education. Available at http://www.asee.org/about-us/the- organization/advisory-committees/Innovation-With-Impact/Innovation-With-Impact-Report.pdf.20. Cohen, A. (1996). The Shaping of American Higher Education. San Francisco, CA: Jossey-Bass.21. Kolb, D. A. (1984). Experiential Learning. Upper Saddle River, NJ: Prentice-Hall.22. Beichner, R. J., Saul, J. M., Abbott, D. S., Morse, J., Deardorff, D., Allain, R. J., . . . Risley, J. (2007). The student-centered activities for large enrollment undergraduate programs (SCALE-UP) project. Research-based reform of university physics, 1(1), 2-39.23. Kohl, P. B., & Kuo, H. V. (2012). Chronicling a successful secondary
Paper ID #6020Promoting Academic Excellence Among Underrepresented Community Col-lege Engineering Students through a Summer Research Internship ProgramDr. Amelito G Enriquez, Canada College Amelito Enriquez is a professor of engineering and mathematics at Ca˜nada College. He received his Ph.D. in Mechanical Engineering from the University of California, Irvine. His research interests in- clude technology-enhanced instruction and increasing the representation of female, minority and other underrepresented groups in mathematics, science and engineering.Prof. Wenshen Pong, San Francisco State University Wenshen Pong received
Paper ID #7951Stimulating Interest in Technological and Engineering Literacy Using a Mul-tidimensional Desktop Virtual Reality FrameworkDr. Magesh Chandramouli, Purdue University, Calumet (Tech) Magesh Chandramouli is currently an Asst. Professor in Computer Graphics Techology at Purdue Univer- sity, Calumet. Earlier, he was a Frederick Andrews Fellow at Purdue University, West Lafayette, where he completed his doctoral studies at the Department of Computer Graphics Technology. He completed his Master of Science from the University of Calgary and his Bachelor of Engineering from the College of Engineering, Guindy, India.Dr
Practice. New York, McGraw-Hill Inc., 2007.27. Goldenberg, Jacob and David Mazursky. “The Voice of the Product: Templates of New Product Emergence.” Creativity and Innovation Management. Vol.8, No.3, Sept. 1999.28. Grønsund, Tor. http://torgronsund.com/2011/11/29/7-proven-templates-for-creating-value-propositions-that- work/.29. Guerra, Lisa, David Allen, Richard Crawford, and Cheryl Farmer. "A Unique Approach to Characterizing the Engineering Design Process." Proceedings of the 2012 ASEE Annual Conference. AC 2012-4130. San Antonio, June 2012.30. Hirtz, Julie, Robert B. Stone, Daniel A. McAdams, Simon Szykman, and Kristin L. Wood. “A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts.” Research in
) (b) (c) (d) Figure 3. Example Screenshots from AUV Workbench UsvBoxTest X3D View Simulation 1Figure 3 shows four independent views of the UsBoxTest demonstration mission.1 3(a) is arepresentation of the operations area, whereas 3(b) shows the grid area. 1 The starboard view canbe seen in 3(c) while 3(d) is a bird’s-eye view of the vehicle from 100 m above. 1 Telemetry dataand plots become available to the user once the mission is stopped or finished. 12-14 Telemetryplots for missions include data such as real Cartesian (X,Y) coordinates (see Figure 4), pitch,roll, yaw or the rudder angle to name a few. 1-5
students for the following elevenstudent outcomes: (a) an ability to apply knowledge of mathematics, science, and engineering; (b) an ability to design and conduct experiments, as well as to analyze and interpret data; (c) an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability; (d) an ability to function on multidisciplinary teams; (e) an ability to identify, formulate, and solve engineering problems; (f) an understanding of professional and ethical responsibility; (g) an ability to communicate effectively; (h) the broad education necessary to
degrees, and 2-year curriculums that tie into terminal degree programs at other Penn State locations. Retentionin STEM degree programs in general, and engineering in particular, is lower among studentswho begin their Penn State education at a regional campus compared to students who start at themain campus.To address the need for STEM retention services at the regional campuses, this project carriesout four strategies, three interventions plus one assessment and evaluation strategy. The threeinterventions include: (a) tutoring programs that serve three foundational mathematics coursesrequired by STEM majors (Algebra II, Trigonometry, Calculus I); (b) a freshman toy-basedengineering design course (called Toy FUN-damentals) in which dissection and
23.714.10students manufacture a proof-of-concept, look-alike, work-alike or comprehensive prototype.Examples of student projects and prototypes that they built in this course are shown in Figure 3. (c) (a) (d) (b) (e)Figure 3: Examples of the prototypes of the products (re)designed and manufactured by thestudents: (a) Heated, lighted, magnetic outdoor hand gloves, (b) Healthy meal plate, (c) iSaver Page 23.714.11cell phone
to TTL levels for devicecontrol. For engineering programs that lack resources for extensive neural interface research,this game offer a less expensive, but no less educational, laboratory experience forundergraduates. The possibility for adaptation of these toys to control various devices for neuralinterface demonstrations is limited only by the imagination of an engineer.References (note to reviewers - references need formatted to ASEE standards)1 Reyes, Janet F. and Tosunoglu, Sabri, “An Overview of Brain-Computer Interface Technology Applications in Robotics” Florida Conference on Recent Advances in Robotics. May 2011.2 Velliste, Meel; Perel, Sagi; Spalding, M. Chance; Whitford, Andrew S.; Schwartz, Andrew B. “Cortical control of
, psychological, andcognitive reasons for choices, particularly in academic settings. Simply put, the model suggeststhat academic motivation is influenced by perceived competence beliefs (“Can I do this task?”)and beliefs about the worth of the task (“is this task useful/interesting/etc?”). The model predictsthat student motivation for engineering is influenced by both students’ expectancy for successand their values. Figure 1 illustrates the general framework (A) as well as this study’sinterpretation of the EVT applied to student motivation for engineering (B). Figure 1. Expectancy-Value Theory of Achievement Motivation: general framework (A) and applied to this particular project context (B). Modified from Finelli and Daly (2012)11.Research by the