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Ikseon Choi, University of Georgia; Yi-Chun Hong, National Central University; David K. Gattie, University of Georgia; Nadia N. Kellam, University of Georgia; Melissa G. Gay, University of Georgia; Lucas John Jensen, University of Georgia; Hyojin Park, University of Georgia; YounSeok Lee, University of Georgia

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the Graduate Institute of Network Learning Technology at National Central University in Taiwan. Her research interests include reflective thinking, learning envi- ronments design, engineering design problems, ill-structured problem solving, and game-based learning in formal education.Dr. David K. Gattie, University of GeorgiaDr. Nadia N. Kellam, University of Georgia Nadia Kellam is an Assistant Professor and engineering educational researcher in the Department of Biological and Agricultural Engineering at the University of Georgia. She is Co-director of the CLUSTER research group with faculty members from engineering, art, and educational psychology. Her research interests include interdisciplinarity, creativity

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2012 ASEE Annual Conference & Exposition

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Stephen J. Krause, Arizona State University; Jacquelyn E. Kelly, Arizona State University; Dale R. Baker, Arizona State University

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of thermal expansion.Figure 2. One student's Pre and Post Topic Quizzes for the area of atomic bonding.Daily Post-Class Assessment Time ScaleToward the end of each class students' experience in the classroom that day was assessed with DailyPoints of Reflection writings on students' points of interest, muddiness and learning as seen in Figure 3. Page 25.1114.4Results were entered into an Excel spreadsheet and then summarized and discussed at the verybeginning of the next class with a Reflection Point Commentary. For many students the discussion ofthe major Muddiest Points helped clarify understanding of difficult concepts and clear up

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2012 ASEE Annual Conference & Exposition

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Larry J. Shuman, University of Pittsburgh; Mary E. Besterfield-Sacre, University of Pittsburgh; Karen M. Bursic, University of Pittsburgh; Natasa S. Vidic, University of Pittsburgh; Nora Siewiorek, University of Pittsburgh

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improving students’ ability to recognize and resolve those types of ethical dilemmas that arise in the engineering workplace.In using MEAs as a learning tool - we have focused on two additional activities:• Assessing the effectiveness of MEAs in various dimensions including improving conceptual learning and problem solving: We have developed a series of assessment instruments to bet- ter understand and measure the educational benefits of using MEAs. Specifically, we are tri- angulating across three assessment instruments, which we created for this project: (1) pre- and post- concept inventories (or knowledge tests) to assess gain in conceptual understand- ing, (2) an online reflection tool to assess process, and (3) a grading

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2012 ASEE Annual Conference & Exposition

Authors

Stephen J. Krause, Arizona State University; Jacquelyn E. Kelly, Arizona State University; Dale R. Baker, Arizona State University

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another to promote development of their own deep conceptual of content and aframework for understanding, recalling, and using that knowledge. One tool for this is clickerquestions, for which 104 multiple-choice questions were created that cover the nine coursetopics. Another tool to promote conceptual development is a set of Homework Preview ProblemConcept Map Quizzes where students must fill in blanks on diagrams of conceptual connectionsof materials structure and properties. Also, to engage students in content from mini-lectures,engagement activities were created for every class. Finally, the third principle is for instructors tofoster student metacognition. This was done with an end-of-class Reflection Points question setthat requests students

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2012 ASEE Annual Conference & Exposition

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James P. Abulencia, Manhattan College; Margot A. Vigeant, Bucknell University; David L. Silverstein, University of Kentucky

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Page 25.1446.3and critically compare them to actual results. This approach has demonstrated success in bothphysics and engineering education. Another approach demonstrated in chemistry is ScientificConcept Construction and Reconstruction, where the emphasis is on encouraging students toapply logical scientific reasoning to repair alternate conceptions about science (She and Liao,2010). Pugh et al report that students having a deep level of engagement and transformativeexperience with the subject matter are more likely to engage in conceptual change (Pugh et al.,2010). More traditional active learning has also been shown to have a positive effect onconceptual learning in physics (Baser, 2006). Finally, in the process of reflective writing

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2012 ASEE Annual Conference & Exposition

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Noe Vargas Hernandez, University of Texas, El Paso; Linda C. Schmidt, University of Maryland, College Park; Gül E. Okudan Kremer, Pennsylvania State University, University Park

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Electrical Resistance Combiination Tribology Microgeometry Optical Reflection Figure 3. Merged Genealogy Tree for the Traffic Light Redesign Problem.Figure 4 presents the data collected at UTEP and Maryland for both groups: Control and TRIZ.The numbers indicate the total number of ideas at each branch in the Genealogy Tree. Page 25.612.10 UTEP UTEP UMD UMD

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2012 ASEE Annual Conference & Exposition

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Punit Deotale, Texas A&M University; Sheng-Jen Hsieh, Texas A&M University

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system, consisting of two cameras mounted on a stereo head andan infrared (IR) pod (Figure 1). The IR pod emits infrared light, which is reflected off users’eyes; the reflection is recorded by the cameras to track the eye movements.A software package called Facelab 5.0, which comes bundled with the system, was used torecord data. A software suite called Eyeworks from Eyetracking Inc. was used along withFacelab for data collection and analysis. The Eyeworks suite includes three softwareapplications: • Eyeworks Design is used to design custom scripts to be used in the experiments. • Eyeworks Record records the data necessary for analysis. • Eyeworks Analyze is an analysis tool that can be used to do visual analysis on the eye

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Nathan W. Klingbeil, Wright State University; Karen A. High, Oklahoma State University; Michael W. Keller, University of Tulsa; Ian M. White, University of Maryland; Bradley J. Brummel, University of Tulsa; Jeremy S. Daily P.E., University of Tulsa; R. Alan Cheville, Oklahoma State University; Jennifer Wolk, University of Maryland, College Park

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day. Each topic will be covered over two weeks and each topic has anengineering analysis project and an engineering design project. How each topic starts, beginningon Tuesdays, and is taught over two weeks is shown on the right-hand side of the figure. Figure OSU-2. ENGR 1113 Course StructureAt the conclusion of a four week module (this is for the three major topics, Algebra,Trigonometry, and Calculus) each team submitted a report and each individual studentcompleted a reflection paper. Topics included in the team reports and reflections will include: thestudent’s contribution to lab, summary of data, and what the student learned in the lab. The

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2012 ASEE Annual Conference & Exposition

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Christine Ehlig-Economides, University of Houston (CoE); Sukesh K. Aghara, Prairie View A&M University; Sarma V. Pisupati, Pennsylvania State University, University Park; Reza Toossi, California State University, Long Beach; Anthony R. Kovscek, Stanford University; Mehmet Ayar, Texas A&M University; Emily Binks-Cantrell, Texas A&M University; Don R. Gilman P.E., Texas A&M University; Dennie L. Smith, Texas A&M University; Timothy Allen Robinson, Pennsylvania State University; Bugrahan Yalvac, Texas A&M University

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the authors and do not necessarily reflect the views of the National Science Foundation. Page 25.901.9References 1. Bransford, J. D., Brown, A. L., & Cocking, R. R., (2000). How people learn: Brain, mind, experience, and school. Washington DC: National Academy Press. 2. National Academy of Engineering (2004). The engineer of 2020: Visions of engineering in the new century. Washington, DC: National Academy Press. 3. Toossi, R., (2011). Energy and the Environment: Choices and challenges in a changing world. Los Angeles, CA: Verve Publishers. 4. Aubrecht, G. J., (2006). Energy: Physical, environmental, and social impact

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2012 ASEE Annual Conference & Exposition

Authors

Donald Plumlee, Boise State University; Steven W. Villachica, Boise State University; Linda Huglin, Boise State University; Shannon Rist, Boise State University

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thefuture. During thesefocus groups, the Incident Cardteam will use the Describe an incident in the workplace that occurred within the first six months toCritical Incident three years after you’d first started working.Method (CIM) [3] to Does this incident reflect (check one):gather data on jobs  Where you successfully performed a job task that you’d learned about in school?and tasks that are  Where you were unsuccessful in performing a job task because your engineering education hadn’t prepared you to do it?essential forengineering. CIM What were the general circumstances leading up to this incident?involves gathering

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2012 ASEE Annual Conference & Exposition

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Eric Constans, Rowan University; Jennifer Kadlowec, Rowan University; Hong Zhang, Rowan University; Bonnie Angelone, Rowan University

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testing will be conducted to assess a) change in retention between courses and b)change in student problem-solving and design skills.BackgroundMany sources have made the case for reforming engineering education to reflect modern trends.Most notably, a recent National Academy of Engineering (NAE) report found that2 Engineering education must avoid the cliché of teaching more and more about less and less, until it teaches everything about nothing. Addressing this problem may involve reconsideration of the basic structure of engineering departments and the infrastructure for evaluating the performance of professors as much as it does selecting the coursework students should be taught.The report also stressed the importance of teaching

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2012 ASEE Annual Conference & Exposition

Authors

Lin Li, Prairie View A&M University; Yongpeng Zhang, Prairie View A&M University; Lei Huang, Prairie View A&M University

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not necessarily reflect the views of the National Science Foundation.Bibliography1. Allen, I. E.; Seaman J., “Class Difference: Online Education in the United States, 2010”, Sloan Consortium of Individual, Institution and Organizations Committed to Quality Online Education, http://www.sloan- c.org/publications/survey/staying_course, 20102. Bell, J. T.; Fogler, H. S., “Virtual Reality Laboratory Accidents”, Proceedings of the American Society for Engineering Education (ASEE) Annual Conference and Exposition, Albuquerque, New Mexico, June 20013. Valera, A.; Diez, J. L.; Valles, M.; Albertos, P., “Virtual and Remote Control Laboratory Development”, IEEE Control Systems Magazine, pp. 35- 39, February 2005.4. Chen, X.; Song, G.; and

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2012 ASEE Annual Conference & Exposition

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Lawrence E. Whitman, Wichita State University; Karen V. Reynolds, Wichita State University

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plans for success will be implemented.Acknowledgment This material is based upon work supported by the National Science Foundation underGrant No. 0807019. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. Page 25.683.6

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Elaine P. Scott, Seattle Pacific University; Denise Wilson, University of Washington; Rebecca A. Bates, Minnesota State University, Mankato

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styles. The index of learning styles include reflective or active learners (processing), sensing or intuitive learners (perception), visual or verbal learners (input), and sequential or global learners (understanding).  Personality Styles: A brief overview of different personality styles in terms of strengths and weaknesses. The Myers-Briggs Type Indicator (MBTI) test is used to provide psychological preferences for four categories with opposite pairs.  Defining Purpose and Goals: A brief overview on how to best define your personal purpose and goals to achieve maximum satisfaction. The module looks at the challenges of the 21st century workplace, and helps students to recognize their

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2012 ASEE Annual Conference & Exposition

Authors

Milo Koretsky, Oregon State University; David L. Silverstein, University of Kentucky; John L. Falconer, University of Colorado, Boulder; Ronald L. Miller, Colorado School of Mines

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works areparticularly noteworthy. First, the Force Concept Inventory (FCI) provided an instrument tomeasure students’ fundamental conceptual understanding of Newtonian mechanics. 1,2 The Page 25.322.2questions were designed to test a student’s ability to apply the fundamental laws and principlesin a way that does not require computation. Second, Eric Mazur published his book PeerInstruction, which describes the use of ConcepTests to engage students in conceptual learningduring lecture.3 This structured questioning process actively involves all students in the class.Peer instruction encourages students to reflect on the problem, think through

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2012 ASEE Annual Conference & Exposition

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Daniel Osakue, Texas Southern University; Xuemin Chen, Texas Southern University; Chenyu Wang, Texas Southern University; Osman Ahmed

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for streaming instrumentation data, and fast client-side,JavaScript based cross-browser graphing/plotting.AcknowledgmentsThis work is partially supported by the National Science Foundation under Grant Numbers DUE-0942778, EEC-0935008, EEC-0935208 and HRD-0928921.Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the authors and do not necessarily reflect the views of the National Science Foundation.Bibliography 1. I. E. Allen, and J. Seaman, “Going the distance: Online education in the United States, 2011.” The Sloan Consortium, 2011. 2. X. Chen, G. Song and Y. Zhang, "Virtual and Remote Laboratory Development: A Review," in Proceedings of Earth and Space 2010, pp. 3843-3852

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2012 ASEE Annual Conference & Exposition

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Ying Tang, Rowan University; Sachin Shetty, Tennessee State University; Xiufang Chen, Rowan University

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toolssuggested some necessary refinement for students to get most benefit from the game experiences.ACKNOWLEDGMENTThis work is supported under a Innovations in Engineering Education, Curriculum,and Infrastructure grant EEC#0935089 from the National Science Foundation.BIBLIOGRAPHY[1]. Bowen, B. A., “Four puzzles in adult literacy: Reflections on the national adult literacy survey,” Journal of Adolescent and Adult Literacy, 42, 314-323, 1999[2]. Klemp, R., “Academic Literacy: Making Students Content Learners,” http://www.greatsource.com/rehand/6-8/pdfs/Academic_Literacy.pdf[3]. Stothard, S. E. and Hulme, C., “A comparison of reading comprehension and decoding difficulties in children,” Cornoldi C. and

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

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27 3.0 1.2 41% crunching 3. Book didn’t complement the problem set 25 2.8 1.4 32% 4. Spreadsheets took focus away from 27 2.8 1.3 26% concepts 5. One group member did the work, but all 27 2.3 1.3 27% got credit 6. Too much repetition of concepts 26 2.3 1.1 11%The feedback from the students regarding interferences provided us with opportunitiesfor reflection and adjustments. Given the size of the class and support budgets for thecourse, it is difficult to see what can be done to reduce class wait time for help. A moreeffective way to run the course, especially given all the other inductive

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2012 ASEE Annual Conference & Exposition

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Yi Guo, Stevens Institute of Technology; Shubo Zhang, Stevens Institute of Technology; Arthur B. Ritter FAIMBE, Stevens Institute of Technology; Hong Man, Stevens Institute of Technology

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Page 25.1251.7delivery and teaching pedagogy. Evaluation results show positive learning experiences.Future work includes more pilot-testing in biomedical engineering courses.AcknowledgmentPartial support for this work was provided by the National Science Foundation's Course,Curriculum, and Laboratory Improvement (CCLI) program under Award No. 0837584. Anyopinions, findings, and conclusions or recommendations expressed in this material are thoseof the authors and do not necessarily reflect the views of the National Science Foundation.Bibliography1. Y. Guo, S. Zhang, H. Man, and A. Ritter, “A Case Study on Pill-Sized Robot in Gastro-Intestinal Tract to Teach Robot Programming and Navigation”, Proceedings of ASEE Annual Conference and

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2012 ASEE Annual Conference & Exposition

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Jason Yao, East Carolina University; Loren Limberis, East Carolina University; Steve Warren, Kansas State University

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expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. External Power Supply MyDAQ RASCL Board Laptop with ELVIS (a) (b) Figure 1. The portable electronics experiment kit (PEEK): (a) kit setup and (b) PEEK with a case (Figure excerpted from [1]).During Fall 2011, these toolsets were applied to laboratory activities associated with two courses:ENGR 3014—Circuit Analysis and ENGR 3050—Instrumentation and Controls; specifics of each aredescribed below:ENGR

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2012 ASEE Annual Conference & Exposition

Authors

Brian F Martensen, Minnesota State University; Deborah K. Nykanen P.E., Minnesota State University, Mankato; Marilyn C. Hart, Minnesota State University, Mankato; Rebecca A. Bates, Minnesota State University, Mankato

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-term surveys is comparedto past feedback. Reflections by faculty mentors will be used to highlight challenges andattempts to address them. Reflections on the process of transitioning mentoring and cohortleadership to faculty in permanent and temporary roles will also be included.BackgroundStudents in our program are selected on a competitive basis with an eye towards supporting adiverse working group. Here, diversity includes majors, years, gender, race, socioeconomicbackground and cultural experience. During the weekly seminar, students engage with eachother and the faculty mentors as a large group, in smaller teams and in various affinity groups.Our program has demonstrated past successes in addressing issues important to the field

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2012 ASEE Annual Conference & Exposition

Authors

Ashland O. Brown P.E., University of the Pacific; Daniel D. Jensen, U.S. Air Force Academy; Joseph J. Rencis P.E., Tennessee Technological University; Kristin L. Wood, Singapore University of Technology and Design; Kyle A. Watson, University of the Pacific; Chuan-Chiang Chen, California State Polytechnic University, Pomona; Vladimir A. Labay, Gonzaga University; Ismail I. Orabi, University of New Haven; Firas Akasheh, Tuskegee University; John J. Wood, U.S. Air Force Academy; Rachelle Kisst Hackett, University of the Pacific; Kathy Schmidt Jackson, Pennsylvania State University; Jiancheng Liu, University of the Pacific; Paul Henry Schimpf; Alexis Pham, University of the Pacific; Krystian Zimowski, University of Texas, Austin; Kristin Taylor, University of the Pacific

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of Learning Styles25 as shown inTable 1 is composed of four dimensions: active/reflective, sensing/intuitive, visual/verbal, andsequential/global. Active learning tools are designed to meet the needs of students with a rangeof learning styles. Particular approaches to teaching often favor a certain learning preference.Therefore, it is important to incorporate a variety of teaching approaches. This index can assistinstructors in creating active learning modules that impact all student learning styles effectively. Page 25.752.7 Table 1. Learning styles categories.Myers Briggs Type Indicator (MBTI) Personality

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Christopher Papadopoulos, University of Puerto Rico, Mayaguez; Aidsa I. Santiago-Román, University of Puerto Rico, Mayaguez; Genock Portela-Gauthier, University of Puerto Rico, Mayaguez; Rosaurelis Marín Ramírez, University of Puerto Rico, Mayaguez; Paola Pacheco Roldan, University of Puerto Rico, Mayaguez

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. Retrospective interviewing will occur immediately after the think-aloud to help participants reflect on and verbalize their thought processes during the think-aloud, drawing from both long-term and short-term memory (e.g., “Describe the process you used to think about the case”). In addition, interviews will include questions to clarify comments participants made during the process and to explicate how knowledge and experiences were used. Transcriptions will be examined using a constant comparison methodA3, with specific attention given to participants’ references to prior knowledge and experiences. Initially, each researcher will conduct an analysis of a single transcription, looking for evidence

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2012 ASEE Annual Conference & Exposition

Authors

Amy Javernick-Will, University of Colorado, Boulder; Jessica Kaminsky, University of Colorado, Boulder; Cathy Leslie P.E., Engineers Without Borders - USA ; Kaitlin Litchfield, University of Colorado, Boulder

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necessarychanges to engineering curriculum to attract a more diverse student and practitioner population. Page 25.321.6Engineering IdentityThe construction of professional or personal identity is dynamic and multiple. In other words,identity reflects membership in many groups and changes over time. Socialization into aprofession may be done via many avenues. However, it is commonly suggested that havingexamples of people like oneself may be a strong contributor. In STEM fields with low femalemembership, this may hinder the entry and retention of females into engineering38–40.STEM study and work is perceived by students as more difficult than many social

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2012 ASEE Annual Conference & Exposition

Authors

James P Becker, Montana State University; Carolyn Plumb, Montana State University; Richard A. Revia, Montana State University

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Page 25.1351.8surprising that students in any semester do not have knowledge of the outcome coming in toEELE 201.Two-sample t-tests comparing the Fall 2011 student responses on the pre-survey to the responseson the post-survey produced significant results (post-survey responses being higher) for alloutcomes questions of interest (p < .05).All pre- and post- survey results (average survey responses) are shown in Table III below: Table III. Pre- and Post-Survey Results (Means) for Fall 2010 and Fall 2011 Learning Outcomes of InterestLearning Outcome: Please complete the followinganonymous survey by selecting the statement thatbest reflects your current knowledge in a given area. 1 = Strongly Disagree 2

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2012 ASEE Annual Conference & Exposition

Authors

Gamze Ozogul, Arizona State University; Amy M. Johnson, Arizona State University; Martin Reisslein, Arizona State University; Kirsten R. Butcher, University of Utah

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diagrams. The current results also reflect earlier findings from58, in which the AA conditionperformed significantly better than the CC condition. Overall, these results support the notionthat abstract representations foster learning through allowing learners to focus on the underlyingstructure of the problem at hand, rather than the superficial elements of each individual problem.Thus, these learners do not observe worked-example problems considering, for example, abattery and a light bulb, rather noting that any type of voltage source and any type of electricaldevice could be present. Since these college students, although novices to electric circuitanalysis, have the requisite experience to know what objects can serve as electrical

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2012 ASEE Annual Conference & Exposition

Authors

Stephanie Elizabeth August, Loyola Marymount University

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Page 25.569.2 recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.Components of TAILS Lab ExperimentsTAILS will deliver the tale of each AI algorithm or concept through a story with nine parts,including a description of the concept, relevant applications, sample test data, design description,exercises that guide the student in implementation, a test driver, suggested experiments, sourcecode that implements the algorithm, and complexity analysis. This choice of components ispatterned after the organization found in the files of software support that accompany Winston'sapproach4 and standard software engineering practice. Previous work5 identified

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2012 ASEE Annual Conference & Exposition

Authors

Nasser Alaraje, Michigan Technological University; Aleksandr Sergeyev, Michigan Technological University; Fred Scheu, College of Lake County

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well the course objectives wereachieved on a scale of 1 to 5 with 5 being Strongly Agree and 1 being Strongly Disagree. Table 1reflects student feedback regarding access to new, effective curriculum modules and labs thatmore accurately reflect the needs of industry. Overall feedback was extremely positive.Measurable Outcomes Overall RateStudents will learn how to model basic digital circuits in hardware description 4.73languages.Students will learn how to use VHDL to model common digital hardware 4.64circuits - combinational and sequential circuitsStudents will learn how to use to use VHDL CAD Tools (editors, debug designs 4.25and perform logic simulation

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2012 ASEE Annual Conference & Exposition

Authors

Hakan Gurocak, Washington State University, Vancouver; Ashley Ater Kranov, ABET; Rochelle Letrice Williams, ABET

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increase in the learninggain. We are encouraged by the positive and enthusiastic feedback from the students on the newmodule. In the future, the entire set will be offered and more details will be reported separately.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.DUE-TUES-0941035. 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 Foundation.References[1] Gurocak, H., “Mechatronics course with a two-tiered project approach,” 2007 ASEE Annual Conference and Exposition.[2] Giurgiutiu, V. and Mouzon, B., “Functional Modules for Teaching Mechatronics to

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2012 ASEE Annual Conference & Exposition

Authors

Kirsten A. Davis, Boise State University; Sondra M. Miller, Boise State University; Ross A. Perkins, Boise State University

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members.AcknowledgementsThis material is based upon work supported by the National Science Foundation, EngineeringEducation and Centers (EEC) division, IEECI program, under Grant No. EEC-1037729. Anyopinions, findings, and conclusions or recommendations expressed in this material are those ofthe authors and do not necessarily reflect the views of the National Science Foundation. Theauthors gratefully acknowledge the support of Dr. Marcia Belcheir, Coordinator of InstitutionalAssessment and Associate Director of Institutional Analysis, Assessment and Reporting forsummarizing administrative and data management support with the self-report survey discussedin this paper.References1. Ford, G.S., T.M. Koutsky, and L.J. Spiwak. (2007). "A Valley of Death in the Innovation