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2015 ASEE Annual Conference & Exposition

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Eric Constans, Rowan University; Shivakumar I Ranganathan, Rowan University; Wei Xue, Rowan University

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. The planetary gearset is the centerpiece of thebench-scale hybrid powertrain, which our students are completing over the course of fivesemesters. With the successful completion of the planetary gearset, our students are now readyto begin tackling the challenge of developing an overall control strategy for the hybrid system.AcknowledgementsThe authors wish to acknowledge the support of the NSF-TUES program in the Division ofUndergraduate Education, DUE-1044532, which made this research possible. Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the National Science Foundation.Bibliography[1] Toyota Motor Corporation, "Toyota Hybrid

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Tzu-Liang Bill Tseng, University of Texas, El Paso; Aditya Akundi, University of Texas at El Paso; Richard Chiou, Drexel University (Eng. & Eng. Tech.); Ryan Wicker, University of Texas, El Paso; Zhonghua Hu, University of Texas at El Paso

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and reflecting student feedback. New Generation Rapid Prototyping Final Version of Rapid Prototyping Simulator developed based on Simulator Software uPrint® SE Plus 3D Printer Rapid Prototyping Simulator integration in: Additive Manufacturing, Rapid

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2015 ASEE Annual Conference & Exposition

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Saryn R Goldberg, Hofstra University; Jennifer Andrea Rich, Hofstra University; Amy M. Masnick, Hofstra University

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circumstances, writing is used not so much to demonstrate knowledge as toachieve an understanding of the students’ own learning process. Writing-to-learn assignments inthese contexts usually attempt to make the student aware of his or her learning issues, asking thestudent to reflect on what he or she may not understand. The modalities for doing so are various:students have been required to write step-by-step explanations of their problem-solving; 5, 6, 7they have been asked to explain how to do a problem to a relative neophyte in the class; 8 andthey have been encouraged to embed literary modes in their problem-solving narratives (e.g.explaining how to solve stoichiometric equations through baking analogies).9 While theseapproaches have resulted in

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2015 ASEE Annual Conference & Exposition

Authors

Narayan Bhattarai, North Carolina A&T State University; Courtney Lambeth, North Carolina A&T State University; Dhananjay Kumar, North Carolina A&T State University; Cindy Waters, North Carolina A&T State University; Devdas M. Pai, North Carolina A&T State University; Matthew B. A. McCullough, North Carolina A&T State University; Caroline S. Booth, North Carolina A & T State University

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content assessment was revised to 13items to better reflect the refined course content. Some of the questions asked in Spring 2013were removed in second year assessment. Sample items from this assessment are shown below.2) The correct nanometer size scale in increasing order (smallest to largest) in terms of wellknown species is:3) Which nanomanufacturing category to pulsed laser deposition (PLD) and mechanical attrition Page 26.673.11(MA) fall under? 10The results from these assessments for Spring 2014 are shown below. NUE Content Assessment Pre & Post-Test Scores by Question

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2015 ASEE Annual Conference & Exposition

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Joseph A Daraio, Rowan University; Ralph Alan Dusseau P.E., Rowan University

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an estimated $2.7 billionin damages as a direct result of the storm.[4] Storm water sewer lines were blocked contributingto flooding, almost 100 wastewater treatment systems either failed or had significantinterruptions in service, and the loss of electrical power caused many others to go offline.[4]Over 70% of New Jersey’s water supply systems were impacted by the storm, mostly due to lossof power, and approximately 360,000 residents were under a boil water advisory, of whicharound 10,000 homes in Ocean County were still under a boil water advisory after one month.[4]New Jersey’s coastal counties are home to approximately 60% of the state’s total population[5],and this is reflected in the student population at Rowan University. Many of our

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2015 ASEE Annual Conference & Exposition

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Nicolas A. Brown, Florida Atlantic University; Khaled Sobhan, Florida Atlantic University; Edgar An, Florida Atlantic University; Ryne A Sherman, Florida Atlantic University; Nancy Romance, Florida Atlantic University

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student motivation tended to have students that earned higher grades. Data collection is ongoing to fully investigate the relationship among Faculty Knowledge of SDT, the classroom learning environment, and students’ objective and subjective learning outcomes.AcknowledgementsThis material is based upon work supported by the National Science Foundation (ResearchInitiation Grant in Engineering Education) under Grant No. 1340304. Any opinions, findings,and conclusions or recommendations expressed in this material are those of the authors and donot necessarily reflect the views of the National Science Foundation. Page 26.1041.9

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2015 ASEE Annual Conference & Exposition

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

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State model (including all course materials for EGR 101) can befound at www.cecs.wright.edu/engmath/. Textbook information is available atwww.wiley.com/college/rattan.AcknowledgmentThis work has been supported by the NSF Division of Engineering Education and Centers undergrant number EEC-0343214 (Department-Level Reform Program), by the NSF Division ofUndergraduate Education under grant numbers DUE-0618571 (CCLI Phase 2), DUE-0622466(STEP Type 1) and DUE-0817332 (CCLI Phase 3), and by a Teaching Enhancement Fund 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

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Authors

Ricardo Jose Morocz, Georgia Institute of Technology; Bryan D Levy, Georgia Institute of Technology; Craig R. Forest, Georgia Institute of Technology; Robert L. Nagel, James Madison University; Wendy C Newstetter, Georgia Institute of Technology; Kimberly Grau Talley P.E., Texas State University; Julie S Linsey, Georgia Institute of Technology

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presentation in more formal webinars.AcknowledgementsThe authors gratefully acknowledge that the support for this work was provided by the NationalScience Foundation Award No. DUE-1432107/1431923/1431721. 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.Reference1. National Academy of Engineering. The Engineer of 2020: Visions of Engineering in the New Century. 2002.2. Torrance, E.P. "Can we teach children to think creatively?" Journal of Creative Behavior 6 114-143 1972.3. Mansfield, R.S. "The effectiveness of creativity training." Review of Educational Research 48(4): 517-536 1978.4. Ma

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Sheng-Jen Hsieh, Texas A&M University

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-faculty and student-student engagement. Every effort was made to ensure thatthe speaker diversity reflected that of the REU students, so that students could envisionthemselves taking the speakers’ paths. Further student-faculty interaction was provided throughweekly faculty research seminars. Each week, one faculty member presented brief vignettes oftheir research interests to the group, enabling students to learn of other imaging related researchbeyond their own projects.In addition to the program-related activities, students participated in several University-wideenrichment events. These activities included a weekly brown-bag seminar series on topics suchas Ethics, GRE preparation, Getting into Graduate School, and Abstract Writing

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Joseph Jinlee Kim P.E., California State University, Long Beach

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Diversity, NSF Grantees Poster Session

Page 26.352.8groups of students who receive multiple years of exposure to sustainability grow and deepen intheir understanding of sustainability and how to improve BIM skills. The challenge in evaluatingan intervention using a quasi-experimental design is in managing a non-random sample andanalyzing the data to allow for causal inference to be made from the findings.AcknowledgmentThis material is based upon work supported by the National Science Foundation under Grant No.DUE-1140941. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author and do not necessarily reflect the views of the National ScienceFoundation.References1. Davis, K. A., and Casey Cline, R. (2009). “Improving course

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Jessica Mary Smith, Colorado School of Mines; Juan C. Lucena, Colorado School of Mines

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Environmentalengineering by Fall in spite of her FoK in mechanics. She was extremely frustrated with the step-by-step formulaic process that her teacher taught in statics as it removed all creativity and desirefor understanding of the physical phenomena. Realizing that most of her 18-yr old classmates areaccustomed to this process and “just listen and do it” [her tone of voice actually hints that theydo this uncritically], in contrast, she says: “I actually stop and wonder if this is the right thingthat I should be doing [amazing sense of ethical responsibility towards her knowledge] or if thisprocess is actually going to teach me what the professor wants to teach me [amazing sense ofmeta-cognition].” Realizing that her critical reflection takes more time and

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2015 ASEE Annual Conference & Exposition

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Nancy K Lape, Harvey Mudd College; Rachel Levy, Harvey Mudd College; Darryl H Yong, Harvey Mudd College; Rebecca M Eddy, Cobblestone Applied Research & Evaluation, Inc.; Nancy Hankel, Cobblestone Applied Research & Evaluation, Inc.

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experiences.MethodDesignThe quasi-experimental study design was developed to compare students from inverted sectionswith those in control sections (i.e., traditional course model). Treatment and control students Page 26.1253.2completed the same measures (e.g., content assessments and student attitude surveys) and facultymembers, who taught in both conditions, also completed reflection papers related to theirexperiences. The guiding research questions for the study and an overview of the assessmentmeasures are shown in Table 1 below (more details on assessment measures are included in asubsequent section of this paper).  Table  1.  Evaluation  Questions  and  Outcome

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Authors

Seda McKIlligan, Iowa State University; Meisha Nicole Berg, Iowa State University; Shanna R. Daly, University of Michigan; Kathryn W. Jablokow, Pennsylvania State University, Great Valley; Eli M. Silk, Rutgers, The State University of New Jersey; Wesley Teerlink, Penn State University

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compensate for missing information and using it toconstruct the problem space5.Forster et al. have examined how different preparations, variations in goal setting, and alternativetask instructions impact performance6. By framing given design tasks in either a novel or afamiliar manner or by priming participants with reflection on novel or familiar events prior tocompleting a task, it was found that participants with less direct experience associated with agiven problem were more open to being primed in a particular manner. Chen et al. investigatedhow different facilitation effects correlate with the creative performance across differentcultures7. They tested Chinese college students and US college students by providing explicitinstructions to half

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2015 ASEE Annual Conference & Exposition

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Susan M Lord, University of San Diego; Matthew W. Ohland, Purdue University; Richard . Layton, Rose-Hulman Institute of Technology

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drawn are of particular interest, sincethese affect persistence studies in all disciplines.AcknowledgementsThis material is based upon work supported by the National Science Foundation (NSF) underGrant 1129383 in the Research on Engineering Education (REE) program. The opinionsexpressed in this article are those of the authors and do not necessarily reflect the views of NSF.References1 Lord, S. M., R. A. Layton, and M. W. Ohland, “Trajectories of Electrical Engineering and Computer Engineering Students by Race and Gender,” IEEE Transactions on Education, 54(4), 610-618 (2011).2 Orr, M. K., S. M. Lord, R. A. Layton, and M. W. Ohland, “Student Demographics and Outcomes in Mechanical Engineering in the U.S.,” International Journal of

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Qichao Wang, Virginia Tech; Montasir Abbas P.E., Virginia Tech; Lisa D. McNair, Virginia Tech

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interests include interdisciplinary collaboration, design education, communication studies, identity theory and re- flective practice. Projects supported by the National Science Foundation include exploring disciplines as cultures, interdisciplinary pedagogy for pervasive computing design; writing across the curriculum in Statics courses; as well as a CAREER award to explore the use of e-portfolios to promote professional identity and reflective practice. Page 26.60.2 c American Society for Engineering Education, 2015 A Knowledge-Delivery Gravity Model to Improve Game-Aided

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Elif Ozturk; Bugrahan Yalvac, Texas A&M University; Michael Johnson, Texas A&M University; Xiaobo Peng, Prairie View A&M University; Ke Liu, Prairie View A&M University

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of work in industry, engineers multiple perspective characteristicswere enhanced. However, besides having significant results, the number of theparticipating engineers (N=14), which was a relatively small sample, was a limitation ofthat work. Future studies with an increased number of engineers should generate moreprecise and clear results.AcknowledgementThis material is supported by the National Science Foundation under EEC GrantNumbers 1129403 and 1129411. Any opinions, findings, conclusions, orrecommendations presented are those of the authors and do not necessarily reflect theviews of the National Science Foundation. Page 26.155.8

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Authors

Ravi P. Ramachandran, Rowan University; Liang Hong, Tennessee State University; Sachin Shetty, Tennessee State University; Kevin D. Dahm, Rowan University; Richard J. Kozick, Bucknell University; Robert M Nickel, Bucknell University; Robi Polikar, Rowan University; Ying Tang, Rowan University; Steven H Chin, Rowan University

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various conferences, international journal articles, book chapters in research and pedagogical techniques. He is the director of the Cyber Defense and Security Visualization LaboratoryDr. Kevin D. Dahm, Rowan University Kevin Dahm is a Professor of Chemical Engineering at Rowan University. He earned his BS from Worces- ter Polytechnic Institute (92) and his PhD from Massachusetts Institute of Technology (98). He has pub- lished two books, ”Fundamentals of Chemical Engineering Thermodynamics” and ”Interpreting Diffuse Reflectance and Transmittance.” He has also published papers on effective use of simulation in engineer- ing, teaching design and engineering economics, and assessment of student learning.Dr. Richard J. Kozick

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Srividya Kona Bansal, Arizona State University; Odesma Onika Dalrymple, University of San Diego; Ashraf Gaffar, Arizona State University

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system (IMODS) using user-centered design methodology 2. Assess the effectiveness, efficiency, and usability of IMODS in creating outcome-based course designThis paper describes our efforts in the High-level (Conceptual) Design phase of the UCDmethodology. This phase follows the collection of data from potential users on what theycurrently do and will need in the future. The main goal of high-level design is to create an earlyblueprint of the system. We have identified 2 tools that will be most suitable for this phase of theproject: Navigation Model and Prototyping. The navigation model will illustrate how all userinterface screens should be connected. Ideally this should reflect the user’s mental model tofacilitate

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2015 ASEE Annual Conference & Exposition

Authors

Holly M Matusovich, Virginia Tech; Rachel E McCord, University of Tennessee, Knoxville; Cheryl Carrico, Virginia Tech; Danielle Almetria Smalls, Virginia Tech Engineering Department; Philip Reid Brown, Virginia Tech Department of Engineering Education

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he or she did not see how learning could be applied. In contrast, “thermo 2” was difficult tounderstand yet seemed to be more “practical” with “interesting applications.” A seeminglystraight forward question about interest results in a complex answer using words and expressionsthat reflect motivation constructs generally without direct statements. The one statement using aconstruct says the applications were interesting, but not the course.Implications and ConclusionsOur findings demonstrated that each clinical interview approach was problematic for revealingmotivation for conceptual understanding; in both cases, the interview context and dialog arecritically important and can directly shape motivation. While problem solving interviews offer

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2015 ASEE Annual Conference & Exposition

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John D. Carpinelli, New Jersey Institute of Technology; Linda S Hirsch, New Jersey Institute of Technology; Raquel Perez-Castillejos, New Jersey Institute of Technology

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example, the statement “The research requirementsnecessary to complete a graduate degree are undesirable” is phrased negatively so thatdisagreement becomes a positive response. For the overall scoring of the survey, responses tothe negatively phrased statements are reversed so that higher average scores reflect more positiveattitudes toward graduate studies.The AGSS also includes several open-ended questions about students’ major, their GPA, howstudents chose their major, whether they have decided to pursue an advanced degree, how theycame to their decision(s) if they decided to pursue another degree in a field other than their Page

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Nolan Alexander LaSota, University of Pittsburgh; Robert S. Parker, University of Pittsburgh; Cheryl A Bodnar, University of Pittsburgh

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thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. Author Robert Parker also acknowledges financial support from the B.P.America Faculty Fellowship of the Swanson School of Engineering at the University ofPittsburgh. We also thank the Department of Chemical and Petroleum Engineering for staffsupport of this REU program.References1. Congressional Budget Office. Technological Change and the Rate of Health Care Spending. Pub. No. 2764. The Congress of the United States, 2008.2. Besterfield-Sacre, M., Gerchak, J., Lyons, M., Shuman, L.J., Wolfe, H. (2004). Scoring Concept Maps: An Inegrated Rubric for Assessing Engineering Education. Journal of Engineering Education. 93(2), 105-115.3

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Authors

Fabien R Durand, Georgia Institute of Technology; Jin Woo Kim, Georgia Institute of Technology; Dorian Henao; Joanna Tsenn, Texas A&M University; Daniel A. McAdams, Texas A&M University; Julie S Linsey, Georgia Institute of Technology; Michael Helms, Georgia Institue of Technology

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of effective ideas.AcknowledgmentsThe authors acknowledge the support provided by the National Science Foundation NSF EEC1025155/1338383. Any opinions, findings, and conclusions or recommendations expressed inthis paper are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References1. Kim JW, Tsenn J, Durand F, et al. Does the Mechanical Engineering Curriculum Hurt Engineering Creativity and Design Self-Efficacy? Journal of Engineering Education. in preparation.2. Fabien Durand MEH, Joanna Tsenn, Erin McTigue, Daniel A. McAdams, Julie S. Linsey. Teaching Students to Innovate: Evaluating Methods for Bioinspired design and Their Impact on Design Self Efficacy IDETC

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2015 ASEE Annual Conference & Exposition

Authors

Renee M Clark, University of Pittsburgh; Autar Kaw, University of South Florida; Mary E. Besterfield-Sacre, University of Pittsburgh; Andrew Scott, Alabama A&M University

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. Age Group: {22 or under, over 22} 2. Gender: {male, female} 3. Under-Represented Minority: {yes, no} 4. Transfer Status: {admitted to engineering as a freshmen, transferred to engineering from a community college with an associate’s degree, other transfer status} 5. Pell Grant Recipient: {yes, no} 6. Combined Work and Credit Hours/Effort: {under 40, 40-65, over 65}The age categories reflect our interest in traditional vs. non-traditional engineering students, withthe traditional student starting college at age 18. The students in this course were juniors andseniors. The under-represented minority students consisted of Hispanic, American Indian,Black/African American, or Hawaiian/Pacific Islander students. The work and credit

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2015 ASEE Annual Conference & Exposition

Authors

John R. Reisel, University of Wisconsin, Milwaukee; Luciana Cancado, University of Wisconsin-Milwaukee; Cindy M Walker, University of Wisconsin - Milwaukee; Dian Mitrayani , University of Wisconsin Milwaukee

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researchexperience so that the students can learn how to conduct their own research projects.While other benefits may be experienced by particular students, they should not beconsidered an expectation for a successful URE for most engineering students.AcknowledgementsPartial support for this work was provided by the National Science Foundation's ResearchInitiation Grants in Engineering Education program under Award No. 1340324. Anyopinions, findings, and conclusions or recommendations expressed in this material arethose of the authors and do not necessarily reflect the views of the National ScienceFoundation. The authors would also like to thank Al Ghorbanpoor and Wendy Pero atUWM for their assistance with this project.Bibliography1. National Science Board

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Authors

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

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 consider how we might provide resources and education to parents to help them support their children’s engineering­related interests.  Acknowledgement  Page 26.962.10 This material is based upon work supported by the National Science Foundation, Division of Engineering Education and Centers, Grant Number 1129342. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.    References  1​  Bureau of Labor Statistics, US Department of Labor. (2014). ​ Occupational outlook handbook, 2014 edition​ . Washington DC: U.S

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Authors

Cheryl Carrico, Virginia Tech; Matthew Arnold Boynton PE P.E., Virginia Tech Department of Engineering Education; Holly M Matusovich, Virginia Tech; Marie C Paretti, Virginia Tech

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team to use existing codebooks to analyzethese responses. Although the number of pilot responses limits our ability to fully test thesecodebooks on the responses, our preliminary review suggests meaningful overlap.Remaining pilot data is planned for collection and analysis in early 2015 along with any changesdeemed necessary. Deployment of the final survey to the larger participant population is plannedfor the spring of 2015.AcknowledgementsThis paper is based on research supported by the National Science Foundation under Grant No.EEC-1232629. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation.Bibliography1

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2015 ASEE Annual Conference & Exposition

Authors

Gisele Ragusa, University of Southern California

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reflect emerging themes and patterns. Categories wereintegrated to form grounded theory (selective coding), to clarify concepts and to allow forinterview interpretations, conclusions and taxonomy development. Frequency distribution of thecoded and categorized data were obtained using a computerized qualitative analytical tool,Hyperrresearch® version 3.5.2. The intent of this intensive qualitative analysis was to identifypatterns, make comparisons, and contrast one transcript of data with another during ourtaxonomy and CPPI refinement.First Year Study Findings and Discussion To our knowledge, there is no coherent (mutually exclusive and collectively exhaustive)taxonomy of pedagogical practices that may contribute to student success in

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2015 ASEE Annual Conference & Exposition

Authors

Amelito G Enriquez, Canada College; Erik N Dunmire, College of Marin; Tom Rebold, Monterey Peninsula College; Nicholas Langhoff, Cañada College; Eva Schiorring, EduData4Action

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