AC 2010-2420: A SERVICE LEARNING CASE STUDY: AN EFFICIENCY STUDYOF A METROPOLITAN BUS TRANSIT SYSTEMCynthia Forgie, University of Southern Indiana Cynthia C. Forgie is an Assistant Professor of Engineering at the University of Southern Indiana, located in Evansville, Indiana, USA. She received a B.S., M.S., and Ph.D. in Industrial Engineering from the University of Louisville. She also earned a M.S. in Software Engineering from Kansas State University. Dr. Forgie has served as a lecturer at the University of Louisville and Kansas State University. She also has over ten years experience as an Operations Research Specialist for the U.S. Army Operational Test Command and five years experience as
students enrolled in the following two existingundergraduate manufacturing courses: (1) Industrial Robotics and Automation and (2) AdvancedMaterial Handling Systems. An array of assignments and projects will be assigned andfacilitated to allow a framework of design that can be researched and presented in these subjectareas.In the Industrial Robotics and Automation course, students are exposed to topics including (a)robot geometry; (b) robot motion and drive systems; (c) motion control, performancespecifications, and precision of movement; (d) robot tooling, sensors and sensing capability; (e)designing for automation process stabilization; and (f) control systems and industrial logic.These topics deal with the study, programmability, and general
AC 2010-894: A META STUDY OF DISCRETE EVENT MODELING ANDSIMULATION (DES) USED BY HEALTHCARE INDUSTRIESSamuel Guccione, Eastern Illinois UniversityThomas McDonald, Eastern Illinois University Page 15.52.1© American Society for Engineering Education, 2010 A Meta Study of Discrete Event Modeling and Simulation (DES) Used by Healthcare IndustriesIntroductionDiscrete event modeling and simulation (DES) is a popular tool in widely varying fields foridentifying and answering questions about the effects of changes on processes. Themanufacturing and business sectors have been using DES since the early 1980’s. Because ofissues related to economic and social
% IE version A MfgE Version A (8.8%) (8.8%) 64.4% 64.4% IE Version B MfgE Version B (8.9%) (9.0%) 63.2% 66.7% IE Version C Mfge Version C (7.9%) 7.7% 65.3% 67.1% All IE versions All MfgE
, Cambridge, Massachusetts, USA, June 11-14, 20074. Crawley, E., J Malmquist, S. Ostlund, D. Brodeur, Rethinking Engineering Education: The CDIO Approach, Springer, New York, 2007.5. Johnson, S. A, B. A. Norman, J. Fullerton, S. Pariseau, “Using Hands-On Simulation to Teach Lean Principles: A Comparison and Assessment across Settings”, Proceedings of the 2008 ASEE Annual Conference, Pittsburgh, PA: June 20-23, 2008.6. Krathwohl, D. R., “A Revision of Bloom’s Taxonomy: An Overview”, Theory into Practice, 41(4), 212-218, 2002.7. McGinniss, L., “A Brave New Education”, IIE Solutions, 34(12), 27-31, 2002.8. McManus, H. L., E. Rebentisch, E. M. Murman, A. Stanke, “Teaching Lean Thinking Principles through Hands-on Simulations
fairly popular with the studentsincludes data collection on the time between orders of a fruit cup at a popular on-campus coffeebar. Prior to the exercise, students are asked to review the expectation and distribution modulesand explore distribution shapes via the plotter/calculator. Students are then given basicinformation, a timeline for data collection, and detailed information as to how data is to becollected. Following data collection, students are assigned to informal groups in class and askedto a) determine the underlying distribution and b) determine the parameters for the distribution.This is an enormously tough concept for students and initially, groups are slow to respond.Eventually however, one of the groups will propose a histogram
enrollment in STEM areas has beendeclining; this is particularly true for minority and Appalachian students. This project workedwith two batches of twenty students each. Each batch was organized into four teams of fivestudents. All students were first provided instruction in logic circuits and ladder logic. Ladderlogic circuits for four tasks were created; a) simulation of automatic garage door, b) simulationof four way traffic light, 3) controlling a light via a physical switch, and 4) physical control offive lights. The five lights mimicked traffic lights (red, yellow, yellow left, green, and green left)at an intersection. The students were asked to control the timing sequence of the lights. Uponcompletion of the eight hour lecture/laboratory
career efficacy, as predicted in the fifthhypothesis. Students who had higher satisfaction scores with the course had significantly higherefficacy scores (F = 8.66, p < 0.001). Additionally, satisfaction and career efficacy scores were Page 15.697.6significantly correlated (r = 0.335, p < 0.001). The grade a student received in the introductorycourse also had a significant impact on career efficacy (F = 2.74, p = 0.030). Students whoreceived an “A” in the course had significantly higher efficacy scores than those who received a“B.”Table 5. Impact of Student Satisfaction on Average Career Efficacy Variable Sample
-k Learning Outcomes Outcomes a-k Outcomes OutcomesAnalytical Ability a,c,f 1,2,4 Oral Communication e,g 6Teamwork e,f 6,7 Written Communication e,g 6Project Management b,e 6,7 Visual Communication e,g 6Math Skills b
, (3), 303-322.3. Jones, M. G., Minogue, J., Oppewal, T., Cook, M., & Broadwell, B. (2006). Visualizing without vision at the microscale: Students with visual impairment explore cells with touch, Journal of Science Education and Technology, 15, 1573-1839.4. Grow, D. I., Lawton, V., & Okamura, A. M. (2007). Educational haptics. American Association for Artificial Intelligence (AAAI) 2007 Spring Symposia- Robots and Robot Venues: Resources for AI Education.5. Okamura, A. M., Richard, C., & Cutkosky, M. R. (2002). Feeling is believing: Using a force-feedback joystick to teach dynamic systems. ASEE Journal of Engineering Education, 91(3), 345–349.6. He, X. (2003). Haptics-augmented undergraduate
AC 2010-1619: STRATEGIES FOR USING TECHNOLOGY WHEN GRADINGPROBLEM-BASED CLASSESSusan Murray, Missouri University of Science and TechnologyRuwen Qin, Missouri University of Sceinece and TechnologyIvan Guardiola, Missouri University of Science and TechnologyAbhijit Gosavi, Missouri University of Science and Technology Page 15.1100.1© American Society for Engineering Education, 2010 Strategies for Using Technology when Grading Problem-Based ClassesAbstractMore and more work is being done today using technology. Email and digital drop boxes areuseful tools for professors; however the challenge comes when one is teaching a quantitativeclass. The issue of using technology to
AC 2010-267: WHAT IS SYSTEMS ENGINEERING?Jane Fraser, Colorado State University, Pueblo Jane M. Fraser is chair of the Department of Engineering at Colorado State University-Pueblo. She was formerly on the faculty at the Ohio State University and Purdue University. She has a BA in mathematics from Swarthmore College and MS and PhD in industrial engineering and operations research from the University of California-Berkeley.Abhijit Gosavi, Missouri University of Science and Technology Abhijit Gosavi is an Assistant Professor at Missouri University of Science and Technology. He was formerly on the faculty at Colorado State University-Pueblo. His BS and MS is in Mechanical Engineering
AC 2010-550: INDUSTRIAL ENGINEERING: IDEALLY POSITIONED TOADDRESS THE SUSTAINABILITY CHALLENGETerri Lynch-Caris, Kettering UniversityJohn Sutherland, Purdue University Page 15.729.1© American Society for Engineering Education, 2010 Industrial Engineering: Ideally Positioned to Address the Sustainability ChallengeAbstractIndustrial Engineers (IEs) have embraced efficiency principles in the design and improvement ofmanufacturing systems. The lean concept defined by the Toyota Production System hasaugmented traditional Work Design courses as a tool for eliminating waste in the manufacturingenvironment. As systems thinkers, the unique