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- Industrial Engineering Division Tech Session 2, IE-ing the Classroom
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- 2017 ASEE Annual Conference & Exposition
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Nadiye O. Erdil, University of New Haven
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Diversity
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Engineering Economy, Engineering Management, Industrial Engineering
class, did not have any work experience.These numbers are important to show why it is essential to design the class with a focus onpractical applications, and structure the assignments with this focus, which we will discuss inmore detail below. Table 1 Learning Outcomes Covered by Each Assignment/Activity Targeted Learning Assignment/Activity Description Outcome(s) Case studies, quantitative problems, conceptual short Homework
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- Industrial Engineering Division Tech Session 2, IE-ing the Classroom
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- 2017 ASEE Annual Conference & Exposition
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Dia St. John, University of Arkansas; Eric Specking, University of Arkansas
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Engineering Economy, Engineering Management, Industrial Engineering
on each step. Figure 1: Project Adaptation Framework Cyclea. Identifying Outreach DetailsBefore jumping into activity development, it is important to understand the details of the event.The desired objective(s), location, time frame, budget, number of participants, and participants’age range are all important pieces of information required to shape an effective and appropriateactivity.[13] Misunderstanding or misinterpreting any of these details could produce anineffective activity that squanders resources and provide an inadequate experience forparticipants. For example, 5th and 6th graders would need more explanation and completion timeto successfully complete a project that was designed for 7th and 8th grade
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- Industrial Engineering Division Tech Session 1: IE-ing a Broader Perspective
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- 2017 ASEE Annual Conference & Exposition
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Faisal Aqlan, Pennsylvania State University, Erie (Behrend College); Yuan-Han Huang, Pennsylvania State University, Erie (Behrend College); E. George Walters III P.E., Pennsylvania State University, Erie (Behrend College); Osama T. Al Meanazel, The Hashemite University
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Engineering Economy, Engineering Management, Industrial Engineering
0.0613 0.0613 0.0077 Total 29 38.1737 S = 0.0875595 R-Sq = 99.84% R-Sq(adj) = 99.42% Figure 10. Analysis of Variance (ANOVA) summary for the response variable “Moment”7. Learning Analysis and AssessmentGiven that the two courses, Product Design and Work Design, are offered for junior levelstudents, the majority of students had taken to the Introduction to Engineering Design coursewhere students are taught the basics of engineering design processes, methods, and decisionmaking using team design projects. Moreover, several students in the Product Design and Workdesign classes had previous internship experiences. The knowledge and experiences of thestudents brought in different ways of thinking and
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- Industrial Engineering Division Tech Session 2, IE-ing the Classroom
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- 2017 ASEE Annual Conference & Exposition
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Paul C. Lynch, Penn State Erie, The Behrend College; Joseph Wilck, College of William & Mary; Amanda Elizabeth VanBuskirk
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Engineering Economy, Engineering Management, Industrial Engineering
members. In addition, it could be supplemented with tracking howmany students actually emailed the instructor (or teaching assistant) and the frequency of thoseemails. Also, a correlation between some of the student’s communication, attendance, andlearning management system usage versus their course grades could be completed. Further, anANOVA could be completed if demographic, ANGEL usage, email usage, and coursesatisfaction were known for each individual student (currently these are known only at theaggregate level).Bibliography 1. Pew Internet and American Life Project, Available at: http://www.pewinternet.org/. Accessed July 1, 2016. 2. Jones, S. (Principal Author), The Internet Goes to College: How students are living in the future
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- Industrial Engineering Division Tech Session 2, IE-ing the Classroom
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- 2017 ASEE Annual Conference & Exposition
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Alejandro Salado, Virginia Tech; John Ray Morelock, Virginia Tech; Arash Baghaei Lakeh, Virginia Tech
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Diversity
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Engineering Economy, Engineering Management, Industrial Engineering
work supported by the National Science Foundation Graduate ResearchFellowship Program under Grant No. 1651272. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation.ReferencesAmbrose, S. A. (2010). How learning works : seven research-based principles for smart teaching. San Francisco, CA: Jossey-Bass.Ammar, S., & Wright, R. (1999). Experiential learning activities in Operations Management. International Transactions in Operational Research, 6(2), 183.Anfara, V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative Analysis on Stage: Making the Research Process More Public. Educational
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- Industrial Engineering Division Tech Session 1: IE-ing a Broader Perspective
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Susan O. Schall, SOS Consulting, LLC
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Engineering Economy, Engineering Management, Industrial Engineering
operational definitions, sampling, andreproducibility of measurement between two or more assessors [12]. Good operationaldefinitions are needed to ensure consistent measurement and evaluation over time. Whileincluded in the Engineering Student Outcomes, ABET does not define multidisciplinary teams(d), contemporary issues (h), life-long learning (i), or modern engineering tools (k). Since ABETdoes not define these terms, it is up to the program to define them in the context of their uniquePEOs and identify indicator(s) appropriately for consistent decision-making (otherwise risk TypeI or Type II errors). Care should also be taken not to combine assessment data from differentlevels of maturation of student knowledge, skills and abilities. “Scores” for
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- Industrial Engineering Division Poster Session
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- 2017 ASEE Annual Conference & Exposition
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Behin Elahi, Purdue University, Fort Wayne
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Industrial Engineering
. Conceptual Elements for Performance Assessment for Faculty and Student Learning. In Assessing Competence in Professional Performance across Disciplines and Professions, edited by Wimmers, P.F., and Mentkowski, M., 11–38. Innovation and Change in Professional Education 13. Springer International Publishing. doi:10.1007/978-3-319-30064- 1_2.Patil, P., Mulimani, D., Desai, B.L., 2015. Prominent Assessment of Students Learning and Statistical Analysis of Quizzes, in: Natarajan, R. (Ed.), Proceedings of the International Conference on Transformations in Engineering Education. Springer India, 567–568. doi:10.1007/978-81-322-1931-6_69.Tishkovskaya, S., Lancaster, G., 2012. Statistical education in the 21st century: a
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- Industrial Engineering Division Tech Session 1: IE-ing a Broader Perspective
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- 2017 ASEE Annual Conference & Exposition
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Cristina D. Pomales-Garcia, University of Puerto Rico, Mayaguez campus; Christopher Papadopoulos, University of Puerto Rico, Mayaguez campus
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Engineering Economy, Engineering Management, Industrial Engineering
/showAward?AWD_ID=1449489Cech, E. (2014). Culture of Disengagement in Engineering Education. Science, Technology & Human Values, 39(1), 42-72.Cech, E. A. (2010). Trained to Disengage? A Longitudinal Study of Social Consciousness and Public Engagement Among Engineering Students. American Society for Engineering Education.Cruz, J., & Frey, W. (2003). An Effective Strategy for Integrating Ethics Across the Curriculum in Engineering: An ABET 2000 Challenge. Science and Engineering Ethics, 9, 543-568.Deneulin, S. (2014). Wellbeing, Justice and Development Ethics. New York and London: Taylor and Francis.Dunlap, J. C. (2005). Changes in students' use of lifelong learning skills during a problem
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- Industrial Engineering Division Tech Session 1: IE-ing a Broader Perspective
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- 2017 ASEE Annual Conference & Exposition
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Alejandro Salado, Virginia Tech
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Engineering Economy, Engineering Management, Industrial Engineering