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2013 ASEE Annual Conference & Exposition

Authors

Dazhi Yang, Boise State Univeristy; Inanc Senocak, Boise State University

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Foundation (NSF) for supporting this project: A SynergisticApproach to Prevent Persistent Misconceptions with First-year Engineering Students (EEC-1232761). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NSF.Reference1. Prince, M., Vigeant, M., & Nottis, K. Assessing misconceptions of undergraduate engineeringstudents in the thermal sciences. International Journal of Engineering Education, 2010, 26(4),880-890.2. Yang, D., Streveler, R. A., &Miller, R. L. Can instruction reinforce misconceptions?Preliminary evidence from a study with advanced engineering students. Paper presented at theAnnual Meeting of the American Educational

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NSF Grantees' Poster Session

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2013 ASEE Annual Conference & Exposition

Authors

Kimberly A Warren, University of North Carolina at Charlotte; Chuang Wang, University of North Carolina, Charlotte

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design and conduct experiments (ABET student outcome [b]), and 3) theability to identify, formulate, and solve engineering problems (ABET student outcome [e]). It isimportant that engineering faculty of all disciplines continuously push the envelope and work toelevate student learning and comprehension so that they can apply the fundamental concepts inengineering design and decision making. The existence of various learning styles has also been well documented and multipleclassification systems have been developed. For example, the Felder-Silverman model7separates learning styles into four dichotomous categories: student learning can be 1) sensory orintuitive, 2) visual or verbal, 3) active or reflective, and 4) sequential or global

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2013 ASEE Annual Conference & Exposition

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Cynthia J. Finelli, University of Michigan; Kenyon M Richardson; Shanna R. Daly, University of Michigan

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and do not necessarily reflect the views of the National ScienceFoundation. The authors gratefully acknowledge the support of the faculty and students from theCollege of Engineering who participated in the project.References1. Eccles, J. S., Barber, B.L., Updegraff, K., & O’Brien, K.M. (1998). An expectancy-value model of achievement choices: The role of ability self-concepts, perceived task utility and interest in predicting activity choice and course enrollment. In L. Hoffmann, A. Krapp, K. A. Renninger, & J. Baumert (Eds.), Interest and learning: Proceedings of the Seeon Conference on Interest and Gender (pp. 267-279), Institute for Science Education at the University of Kiel: IPN.2. Finelli, C. J., & Daly

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Authors

Marilyn Barger, National Science Foundation ATE Centers; Richard Gilbert, University of South Florida; Marie A. Boyette, FLATE

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with both Resources  for  Student  Tours  of  Manufacturing  Facilities   the number of tour events and total number of student participants per year. The sharp decline in the number of participants in 2007 and 2008, strongly reflected the very unstable fiscal environments that schools Find  pre-­‐tour  lesson  plans,  post-­‐tour

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NSF Grantees' Poster Session

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2013 ASEE Annual Conference & Exposition

Authors

Joanna Tsenn, Texas A&M University; Daniel A. McAdams, Texas A&M University; Julie S Linsey, Texas A&M University

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found in Figure 1. For each task-specific self-concept, a nine-item scale was developed using the design process. The first item asked for the participant’sself-percept towards conducting engineering design as a whole (giving the engineering designscore) while the other eight items reflected each step of the engineering design process (averagedto be the engineering design process score)2. Page 23.30.3 Figure 1. Steps of the engineering design process12.Self-efficacy affects a person’s behavior towards an activity, and their self-percepts can affectthe thought patterns and neurophysiological reactions13. Those with high self

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2013 ASEE Annual Conference & Exposition

Authors

Alan D. Niemi, LeTourneau University; Matthew G. Green, LeTourneau University; Melanie Roudkovski, LeTourneau University

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Enhancement (FIRE), is supportedby the National Science Foundation under Grant No. 0969382. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the authors and do notnecessarily reflect the views of the National Science Foundation.1.0 Project Activities1.1 Overall Goal Page 23.551.2The most specific and immediate goal of this project is to increase the School of Engineering andEngineering Technology (SEET) graduation rate from its 2009 five-year average of 42% to animproved five-year average of 65%.1 To achieve this target, 1-year retention of new studentsmust be increased to 85% from its 2009 level of 68

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2013 ASEE Annual Conference & Exposition

Authors

Christine Ehlig-Economides, Texas A&M University; Bugrahan Yalvac, Texas A&M University; Emily Binks-Cantrell, Texas A&M University; Don R Gilman PE, PMP, Texas A&M University; Mehmet Ayar, TUBITAK; Timothy Allen Robinson, Penn State University; Anthony R Kovscek, Stanford University; Reza Toossi, California State University, Long Beach; Sarma V. Pisupati, Pennsylvania State University, University Park; Sukesh K Aghara, University of Massachusetts Lowell

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can create online and Page 23.871.13dynamic course materials that can be updated easily and frequently as needed. The workpresented in this paper and the instruments described will also guide any systematic evaluation ofa pedagogical novelty on similar student learning outcomes.AcknowledgementThis material is supported by the National Science Foundation under TUES Phase-II Grantnumber 1022932. Any opinions, findings, conclusions, or recommendation presented are thoseof the authors and do not necessarily reflect the views of the National Science Foundation.References1. Bransford, J. D., Brown, A. L., & Cocking, R. R., (2000). How people

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Authors

Arthur Ornelas Jr., Arizona State University; John Sadauskas, Arizona State University; Sandra Houston, Arizona State University; Wilhelmina C. Savenye Ph.D., Arizona State University; Eddy Ramirez; Claudia E. Zapata, Arizona State University

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Arizona State University. His research interests include social media, narrative storytelling, cyberlearn- ing, embodied mixed-media learning, affective computing, and instructional design. He holds a M.Ed. in Curriculum and Instruction from Arizona State University and is a former middle/high school English teacher. His work is steeped in a multi-disciplinary background including education, design, filmmaking, music, programming, sociology, literature and journalism. He is a member of ASU’s Advancing Next Generation Learning Environments (ANGLE) and Reflective Living research groups.Dr. Sandra Houston, Arizona State University Dr. Sandra Houston is a member of the Geotechnical Engineering faculty in the School of

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2013 ASEE Annual Conference & Exposition

Authors

Ying Tang, Rowan University; Sachin Shetty, Tennessee State University; Kauser Jahan, Rowan University; John P Henry, Sustainable Learning Systems; S. Keith Hargrove, Tennessee State University; Talbot Bielefeldt, International Society for Technology in Education

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. Theirimpact on student learning was also partially reflected in student responses to other open-endedquestions. For instance, students were able to provide important justifications when prompted todiscuss energy sources with an advocate of a particular approach, such as “You have to factor inthe cost, the power it supplies, and the effectiveness over X amount of years.” “The best way toselect an energy source is to focus on being environmentally friendly first. Then find the mostcost effective that will produce enough energy for your needs.” Students also commented on themost important things they learned through the game such as “The most important thing that Ilearned was to be environmentally friendly rather than being the most cost and energy

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2013 ASEE Annual Conference & Exposition

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Kathleen Meehan, Virginia Tech; Justin Adam Cartwright, Virginia Tech

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Page 23.842.2issues. The experimental skills in circuits and electronics of many graduate students are stilldeveloping and not all of the graduate students in the GTA pool are interested in the subjectmatter. This lack of experience and interest is much more difficult to overcome, yet is quicklysensed by the undergraduates taking the course who will reflect this in their comments on thequality of instruction at the end of the semester. Thus, the selection of the instructor for thelectures has been a critical factor to the successful introduction of guided self-learning inexperimental techniques using LiaB.Development of online circuits laboratory course for on-campus studentsMotivation: While a physical lecture was also incorporated in the

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2013 ASEE Annual Conference & Exposition

Authors

Andreas S Spanias, Arizona State University; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy, Arizona State University; Mahesh K Banavar, Arizona State University; Suhas Ranganath, ASU; Xue Zhang; Deepta Rajan, Arizona State University; Girish Kalyanasundaram, Arizona State University

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of the e-book and the proposed learning environment.The J-DSP Simulation EnvironmentJ-DSP, a web-based DSP education software, is a block-based environment where simulationsare established by choosing blocks through a drag-n-drop process and connecting them toestablish signal flow. Any change in the simulation parameters are automatically reflected in thefollowing blocks. An example simulation established in the J-DSP interface along withvisualization of the output is shown in Figure 1. A set of DSP laboratories have been developedin J-DSP that cover several DSP concepts including the z-transform, digital filter design, spectralanalysis, multirate signal processing, and statistical signal processing along with a rich set ofvisualization

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2013 ASEE Annual Conference & Exposition

Authors

Andy S Zhang, New York City College of Technology; Sidi Berri, New York City College of Technology; Iem H Heng, New York City College of Technology; Farrukh Zia, New York City College of Technology CUNY

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, Page 23.232.6 H2 and others) in the public and in private gathering places. This mobile handheld device then can relay the information detected to smart phones or tablets or laptops in any place at any time. Applications of this useful mobile device include coal mine explosion prevention, detections of natural gas and other industrial and explosive chemical leaks, and detection of harmful gases in the public gathering places such as subway stations, shopping malls, and airports. Figure 8 shows a prototype of the handheld device. a) Prototype of HCDD b) Case Design Figure 8. Mobile Handheld Chemical Detection DeviceAs can be seen, the scope of all the design projects reflected

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NSF Grantees' Poster Session

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2013 ASEE Annual Conference & Exposition

Authors

Amod A. Ogale, Clemson University and Hoowaki LLC; Sarah Kelley Hulseman, Hoowaki LLC; Andrew Hampton Cannon, Hoowaki LLC; Byron S. Villacorta, Clemson University; Ralph Allen Hulseman, Hoowaki LLC

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large base,providing a large cross-sectional area to overcome material draw down and shrinkage in order tocreate extrudate that results in more straight walled structures. The films were extruded at 220°Cin a custom-built cast-film line consisting of a 25-mm single screw extruder to the micro-textured dies. The films were produced at a constant throughput of 0.8 cc/min by using a gear-pump and a take-up speed of 100 mm/min.Microstructural CharacterizationTo obtain sharp cross-section samples, the films were mildly cooled in liquid N2 and cut withspecial scissors (Kevlar® cutter grade). The resulting profile was analyzed by reflective opticalmicroscopy (Olympus BX 60) and scanning electron microscopy (SEM, Hitachi S-4800 FieldEmission Scanning

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2013 ASEE Annual Conference & Exposition

Authors

John Krupczak, Hope College; Kate A Disney, Mission College

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Page 23.1160.10the authors and do not necessarily reflect the views of the National Science Foundation.Bibliography 1. Pearson G., and A. T. Young, Technically Speaking: Why All Americans Need to Know More about Technology. National Academies Press (2002). 2. Pearson G., and E. Garmire, Tech Tally: Approaches to Assessing Technological Literacy. National Academies Press, (2006). 3. Bransford, J.D., A.L. Brown, and R.R. Cocking, (Editors). How People Learn: Brain, Mind, Experience, and School, Washington D.C.: National Academy Press, (1999). Page 279. 4. National Assessment of Educational Progress (NAEP), U.S. Department of Education, Institute of Education Sciences, National Center for Education

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Authors

Pnina Ari-Gur, Western Michigan University; Pavel Ikonomov, Western Michigan University; Roman Rabiej, Western Michigan University; Peter Thannhauser, Western Michigan University; Marwa M Hassan, Louisiana State University; Daniel M. Litynski, Western Michigan University; Renee Schwartz, Western Michigan University

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completion rate is less than half5,6. Oneof the primary reasons undergraduates choose to leave science and engineering majors is the lossof interest in the field5 prompted by inadequate motivation and background knowledge fromschool level. Among our sophomore engineering students, only about 50% are passing with therequired C or better. Many of the unsuccessful students could become successful if teachingmethods would better fit their different learning styles7,8.Students have different preferred learning styles7-9. These styles relate to the type of informationaccessed, the manner in which information is accessed (e.g., visual, verbal), the processesinvolved in accessing information (e.g. active, passive, reflective), and the sequence in

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2013 ASEE Annual Conference & Exposition

Authors

Ashland O. Brown, University of the Pacific; Richard H. Crawford, University of Texas, Austin; Daniel D. Jensen, U.S. Air Force Academy; Joseph J. Rencis P.E., Tennessee Technological University; Jiancheng Liu, University of the Pacific; Kyle A. Watson, University of the Pacific; Kathy Schmidt Jackson, Pennsylvania State University, University Park; Rachelle Kisst Hackett, University of the Pacific; Paul Henry Schimpf, Eastern Washington University; Chuan-Chiang Chen, California State Polytechnic University, Pomona; Ismail I Orabi, University of New Haven; Firas Akasheh, Tuskegee University; John J Wood, U.S. Air Force Academy; Brock U Dunlap, University of Texas, Austin; Ella R. Sargent, University of the Pacific

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. Page 23.224.7 4  Figure 1. Kolb Learning CycleLearning StylesEach FE ALM developed in this work is designed to span a spectrum of different characteristicsin which students learn. The Felder-Soloman Index of Learning Styles25 is composed of fourdimensions: active/reflective, sensing/intuitive, visual/verbal, and sequential/global [Table 1].Active learning tools are designed to meet the needs of students with a range of learning styles.Particular approaches to teaching often favor a certain learning preference. Therefore it isimportant to incorporate a variety of teaching approaches This index can assist instructors increating active learning modules

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2013 ASEE Annual Conference & Exposition

Authors

Erin M Fitzgerald, Museum of Science; Christine M Cunningham, Museum of Science

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professor and chairperson of the Childhood Education Department at SSU,works to ensure that the students from Dr. Bade’s course are later placed in practicumexperiences with teachers who have been trained in engineering and technology content andproblem-based pedagogy. There are many players involved in an elementary teacher’s preservicepreparation, but when there is fluid communication and collaboration between them all, newteachers enter the classroom confident that they can teach engineering and technology to theirstudents, and committed to the importance of doing so.How do we measure success?Measurement of the BEST project’s success has centered on two main areas that reflect theoverarching goals of the grant: • How helpful does the faculty

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2013 ASEE Annual Conference & Exposition

Authors

Monica E Cardella, Purdue University, West Lafayette; Marisa Wolsky, WGBH Educational Foundation; Christine Andrews Paulsen, Concord Evaluation Group; Tamecia R. Jones, Purdue University, West Lafayette

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reflect the views of the NationalScience Foundation.References 1. Bureau of Labor Statistics, US Department of Labor. (2006). Occupational Outlook Handbook, 2010-11 Edition, Bulletin 2800. Washington DC: U.S. Government Printing Office. 2. National Science Foundation. (2006). Science and Engineering Degrees: 1966–2004. Arlington, VA: Division of Science Resources Statistics. 3. National Science Board (2010) Science and Engineering Indicators 2010. Arlington, VA: National Science Foundation. 4. Stevens, R. Bransford, J. and Stevens, A. (2005). "The LIFE Center's Lifelong and Lifewide Diagram". Accessed from: http://life-slc.org). 5. Bell, Philip, Lewenstein, A.W., Shouse, A.W. & Feder, M.A. (Eds

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Authors

Kenneth A Connor, Rensselaer Polytechnic Institute; Kathleen Meehan, Virginia Tech; Bonnie H. Ferri, Georgia Institute of Technology; Dianna Newman; Yacob Astatke, Morgan State University; Mohamed F. Chouikha, Howard University; Deborah Walter, Rose-Hulman Institute of Technology

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.29. A. Prades, S. Espinar, “Laboratory Assessment in Chemistry: An Analysis of the Adequacy of the Assessment Process,” Assessment & Evaluation In Higher Education [serial online]. vol. 35, no. 4, pp. 449-461, July 2010.30. J. Robertson et al, “Exploiting a Disruptive Technology to Actively Engage Students in the Learning Process,” 2013 ASEE Conference.31. J. Rodd, D. Newman, G. Clure, M. Morris. “Moving the Lab to the Classroom: The Impact of an Innovative Technological Teaching Tool on K-14 Learning and Cognition,” SITE Conference, San Diego, CA, March 2010, 2807-2813.32. D. Schon (1995), The Reflective Practitioner: How Professionals Think in Action, Ashgate Publishing.33. J. Selingo, “Connecting the Dots,” ASEE

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2013 ASEE Annual Conference & Exposition

Authors

Jianyu Dong, California State University, Los Angeles; Huiping Guo, California State University, Los Angeles

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”). Clearly, all knowledge outcomes received much higher scoresin post survey and in general we can observe a greater growth in year 2012, which reflected theeffect of improved implementation of CPBL in the 2nd project year. Analysis of two year dataconsistently proved that most of the biggest growths occurred in the learning outcomes directlyrelated by the class projects. For example, in CS470 offered in Winter 2012, the biggestincrements of the rating occur on the following outcomes, and all of them are directly addressedby in-class or after-class projects. • Knowledge of ARQ (Pre=1.58, Post=4.44, growth=2.86) • Knowledge of TCP flow control and congestion control (Pre=1.35, Post=4.11, growth=2.76) • Knowledge of network

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2013 ASEE Annual Conference & Exposition

Authors

Maria Teresa Napoli, UC Santa Barbara; Arica Lubin, Center for Science and Engineering Partnerships, University of California, Santa Barbara; Liu-Yen Kramer, Center for Science and Engineering Partnerships, University of California Santa Barbara; Jens-Uwe Kuhn, Santa Barbara City College; Nicholas Arnold, Santa Barbara City College; Ofelia Aguirre, Center for Science and Engineering Partnerships; Megan T. Valentine, University of California, Santa Barbara

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, INSET’s success lies in recognizing the importance of involving CCfaculty in all aspects of program planning and execution. In particular, CC faculty’s input hasbeen key to the design of appropriate student activities, which are non-threatening, motivating,and which address the particular needs of CC students, while providing an environment thatoffers them greater opportunity to prosper and succeed. CC faculty also play a particularlycrucial role in identifying and recruiting high-potential candidates, especially those whose gradesmay not reflect their abilities and initiative. It is thanks to their encouragement and support thatthese students, who often lack both self-confidence and role models, are led to view INSET asnot only valuable but also

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2013 ASEE Annual Conference & Exposition

Authors

Nathan W. Klingbeil, Wright State University; Anthony Bourne, Wright State University

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grantat Wright State University. Any opinions, findings, conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation or Wright State University.Bibliography1. McKenna, A., McMartin, F. and Agogino, A., 2000, "What Students Say About Learning Physics, Math and Engineering," Proceedings - Frontiers in Education Conference, Vol. 1, T1F-9.2. Sathianathan, D., Tavener, S., Voss, K. Armentrout, S. Yaeger, P. and Marra, R., 1999, "Using Applied Engineering Problems in Calculus Classes to Promote Learning in Context and Teamwork," Proceedings - Frontiers in Education Conference, Vol. 2, 12d5-14.3. Barrow, D.L. and Fulling, S.A., 1998, "Using

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2013 ASEE Annual Conference & Exposition

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Kathleen Alfano, College of the Canyons

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any two of the selection criteria. These letters must reflect academic, employment or community experiences that relate to the energy technology field and highlight leadership and teamwork abilities of the representative. These letters must accompany the application package, not be sent separately.Part E: Certification Page 23.934.13 12  Applicant's Certification I certify that the information I have provided in this document is accurate. I understand that if I am chosen to participate in the CREATE US – Australia Renewable Energy Learning Exchange and Network, I will be representing both my organization

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2013 ASEE Annual Conference & Exposition

Authors

Jacob P. Moore, Virginia Tech; Christopher B. Williams, Virginia Tech; Chris North, Virginia Tech; Aditya Johri, Virginia Tech

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as to what is contained at more detailed levels.  Cross-Course Effects on Learning: The power of the Adaptive Map tool is its emphasis on connections. So far, the tool has been limited to a single course, but by developing content for related courses (e.g., Dynamics, Strength of Materials, etc.) researchers could explore how this tool could help students develop knowledge that crosses course boundaries.6. AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No.NSF TUES-1044790. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience

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2013 ASEE Annual Conference & Exposition

Authors

Jana Fattic, Western Kentucky University; Andrew N.S. Ernest, Western Kentucky University; Joseph Lee Gutenson, University of Alabama

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reflect functional capacity.Water Utility Management and Human Intended to provide the learner with anRelations overview of the management and human relations aspects of water and wastewater utilities. A learner in this course will gain industry-based insight into the special operations and management functions of a water or wastewater utility with emphasis on the human relations activity.Modern Technology & Water

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2013 ASEE Annual Conference & Exposition

Authors

Lisa Benson, Clemson University; Adam Kirn, Clemson University; Beshoy Morkos, Florida Institute of Technology

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Monthly Email Advisor. 2008;6(8):2–3.22. Nickerson RS. The teaching and thinking of problem solving. In: Sternberg RJ, editor. Thinking and Problem Solving. 2nd ed. San Diego, CA: Academic Press; 1994. page 409– 49.23. Wankat P. Reflective Analysis of Student Learning in a Sophomore Engineering Course. Journal of Engineering Education. 1999;88(2):195–203.24. Jonassen DH. Toward a Design Theory of Problem Solving.pdf. Educational Technology Research & Development. 2000;48(4):63–85.25. Bowman D, Benson L. MuseInk : Seeing and Hearing a Freshman Engineering Student Ink and Think. ASEE Annual Conference Proceedings. Louisville, KY: American Society

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Authors

John R. Reisel, University of Wisconsin, Milwaukee; Marissa Jablonski, University of Wisconsin, Milwaukee; Ethan Munson, University of Wisconsin, Milwaukee

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and do not necessarily reflect the viewsof the National Science Foundation. The authors would also like to thank Shuwen Tang,Cindy Walker, Todd Johnson, Tina Current, Sharon Kaempfer, and Jennie Klumpp (all atUWM) for their assistance with this project.Bibliography1.National Science Board. 2003. The Science and Engineering Workforce: Realizing America’s Potential.Publication NSB 03-69. (www.nsf.gov/nsb/documents/2003/nsb0369/nsb0369.pdf)2. Augustine, N. “Rising Above the Gathering Storm: Energizing and Employing America for a BrighterEconomic Future”, Committee on Science, Engineering, and Public Policy (COSEPUP), 2007.3. Good, J., Halpin, G., and Halpin, G. “A Promising Prospect for Minority Retention: StudentsBecoming Peer Mentors”, J

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Authors

Steven W Villachica, Boise State University; Anthony Wayne Marker, Boise State University; Donald Plumlee, Boise State University; Linda Huglin; Amy Chegash, Boise State University

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) Grant No. 1037808Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of the National Science Foundation. Page 23.1166.2AbstractPublished research has provided a robust set of documented tools and techniques fortransforming individual engineering courses in ways that use evidence-based instructionalpractices. Many engineering faculty are already aware of these practices and would like to use 2them. However, they still face significant implementation barriers. The E R2P effort

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2013 ASEE Annual Conference & Exposition

Authors

Kathleen L Kitto, Western Washington University; Debra S. Jusak, Western Washington University

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thestudents would know most of the answers before we began the assessment as thequestions are indeed very basic. The students overall performed the worst on the basicchemistry questions (only 44%), while they only did only somewhat better on thequestions reflecting on hands-on learning (55%).We also examined whether the students’ scores in these three content areas made adifference in their performance on four low stakes quizzes and the two mid-term exams.Only one minor difference was noted on the first three quizzes in that on quiz three, thestudents scoring higher in basic science knowledge, scored higher than their peers. But,on quiz 4, student outcomes were different for those students scoring higher (upper 50%)on their pre-course assessment

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2013 ASEE Annual Conference & Exposition

Authors

DeLean A Tolbert, Purdue University, West Lafayette; Monica E Cardella, Purdue University, West Lafayette

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