formative assessments to favordifferent learning styles (Wang, Wang et al. 2006). Both learning style and formativeassessment strategy significantly affected student achievement, though, consistent with Pashler etal.’s conclusion, the interaction between these factors was not significant. Additionally, as thisstudy was performed with a web-based middle-school biology course, a gap remains with regardto undergraduate engineering education.As the primary motivation for this study is increasing the diversity of the engineering graduatesthat colleges and universities prepare for the workforce, some evidence demonstrates thatvarying teaching approaches to favor a multitude of learning styles may aid in achieving thatparticular end. A validation study of
gained.References1. Bandura, A. (1982). Self-Efficacy Mechanism in Human Agency. American Pyschologist, 37(2), 122-147.2. Basawapatna, A., Repenning, A., & Koh, K. H. (2015). Closing The Cyberlearning Loop. Proceedings of the 46th ACM Technical Symposium on Computer Science Education - SIGCSE '15, (pp. 12-17).3. Bean, N., Weese, J. L., Feldhausen, R., & Bell, R. (2015). Starting From Scratch: Developing a Pre- Service Teacher Program in Computational Thinking. Frontiers in Education.4. Bell, R. S. (2014). Low Overhead Methods for Improving Capacity and Outcomes in Computer Science. Manhattan, KS: Kansas State University.5. Brennan, K., & Resnick, M. (2012). Using artifact-based interviews to study the
organizations often tend to amplify the moral and political values that are lacking and need to be further enhanced in developing contexts. They view technologies as instruments for well-being rather than profits.As engineering educators who are interested in preparing future engineers for the increasinglyglobalized future, we need to be careful about what kind(s) of “global engineers” we are training.Emphasizing one or two approaches to engineering ethics over others represents an incompleteapproach that fails to project an appropriately comprehensive view of global engineering practice.Obviously, we are not training every student to become a professional engineer working in amultinational business company, nor do we expect that
Exposition, 2004.[2] Hertzberg, J., “Seeing Fluid Physics: Outcomes From a Course on Flow Visualization,” Bulletin of the American Physical Society, Paper # QE3, 55 (16), 2010.[3] Poon, M., Todd, J., Neilson, R., Grace, D., Hertzberg, J., “Saffman-Taylor Instability in a Hele-Shaw Cell,” Physics of Fluids, 16 (9), p. S9, 2004.[4] Settles, G. S., “On the Fluid Dynamicist as Artist,” Proceedings of the 12th International Symposium on Flow Visualization, Gottingen, Germany, 2006.[5] Kleine, H., Settles, G. S., “The Art of Shock Waves and Their Flow Fields,” Shock Waves, 17, pp. 291-307, 2008.[6] Samuel, M., Henley, J., and Shakerin, S., “Development of a Wet Wall: An Undergraduate Research Project,” Paper #37677
affiliates.References[1] J. M. Bekki, M. Huerta, J. S. London, D. Melton, M. Vigeant, and J. M. Williams, “Opinion: Why EM? The potential benefits of instilling an entrepreneurial mindset,” Advances in Engineering Education, vol. 7(1), 2, 2018.[2] C. J. Creed, E. M. Suuberg, and G. P. Crawford, “Engineering entrepreneurship: An example of a paradigm shift in engineering education,” Journal of Engineering Education, vol. 91(2), pp. 185-195, 2002. https://doi.org/10.1002/j.2168-9830.2002.tb00691.x[3] National Science Foundation, NSF Innovation Corps (I-Corps™), 2019. Available: https://www.nsf.gov/news/special_reports/i-corps/index.jsp[4] A. Huang-Saad, J. Fay, and L. Sheridan, “Closing the divide: Accelerating technology
when it successfully fulfills a human request and the individual has confidence thechatbot can perform well. Figure 1. Conceptual Framework for the User Interface Perceived Trust Efficacy of Chatbots for Future Faculty Mentoring. Adapted from “Enhancing User Experience with Conversational Agent for User Satisfaction Movie Recommendation: Effects of Self- Disclosure and Reciprocity,” by S. Y. Lee
learning approaches with ever-changing research-based and technologically instrumented means, transferring control over education from teachers to mangers of the bureaucracy.Therefore, engineering education is considered as a system that can be studied, controlled,optimized, and assessed. A classic example in this regard is the “pipeline” metaphor [10] that hasbeen widely used in the United States to understand and tackle the systematic challenges withretaining engineering students and recruiting underrepresented populations. Nearly allengineering education graduate programs in the United States are in engineering colleges,schools, or departments. Arizona State University (ASU)’s Engineering Education PhD Programis another good example
upon work supported by the National Science Foundation under Grant No.1741611 Encouraging Civil Engineering Retention through Community and Self-EfficacyBuilding. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of the National ScienceFoundation.References[1] "Infrastructure Report Card." American Society of Civil Engineers. (accessed 2 Feb., 2019): https://www.infrastructurereportcard.org/.[2] S. Hatch, Diversity by Design: Guide to Fostering Diversity in the Civil Engineering Workplace. Reston, VA: American Society of Civil Engineers, 2008.[3] "Criteria for accrediting engineering programs 2019-2020." ABET. (accessed 2
Life History in a Social Science Perspective. Gainesville, FL: University of Florida Press, 1989.[20] D. J. Clandinin and F. M. Connelly, Narrative Inquiry: Experience and Story in Qualitative Research. San Francisco: Jossey-Bass, 2000.[21] F. M. Connelly and D. J. Clandinin, Teachers as Curriculum Planners: Narratives of Experience. New York: Teachers College Press, Columbia University, 1988.[22] J. W. Creswell, Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, 2nd ed. Upper Saddle River, NJ: Pearson, 2005.[23] P. B. Myers and K. D. Myers, Myers-Briggs Type Indicators. Palo Alto, CA: Consulting Psychologists Press, Inc, 1998.[24] A. S. Denzer and K. E. Hedges, “From CAD to
positive academic experiences and retention in engineering,” Proc. Natl. Acad. Sci., vol. 114, no. 23, pp. 5964–5969, Jun. 2017, doi: 10.1073/pnas.1613117114.[7] G. Crisp and I. Cruz, “Mentoring College Students: A Critical Review of the Literature Between 1990 and 2007,” Res. High. Educ., vol. 50, no. 6, pp. 525–545, Sep. 2009, doi: 10.1007/s11162-009-9130-2.[8] S. L. Fletcher, D. C. Newell, L. D. Newton, and M. R. Anderson-Rowland, “The wise summer bridge program: Assessing student attrition, retention, and program effectiveness,” in ASEE Annual Conference Proceedings, 2001, pp. 10605–10611.[9] S. C. de Janasz and S. E. Sullivan, “Multiple mentoring in academe: Developing the professorial network,” J. Vocat. Behav., vol
. 141-147, 2010.[3] D. Xu, & S.S. Jaggars, “The impact of online learning on students' course outcomes: Evidence from a large community and technical college system,” Economics of Education Review, vol. 37, no. 46-57, 2013.[4] L.L. Long, & J. A. Mejia, “Conversations about diversity: Institutional barriers for underrepresented engineering students,” Journal of Engineering Education, vol. 105, no. 2, pp. 211-218, 2016.[5] D. Lawton, N. Vye, J. Bransford, E. Sanders, M. Richey, D. French, & R. Stephens, “Online learning based on essential concepts and formative assessments,” Journal of Engineering Education, vol. 101, no. 2, pp. 244-287, 2012.[6] S. Badjou, & R. Dahmani, “Current status of online science and
. 13Litzinger, T., Van Meter, P., Firetto, C., Passmore, L., Masters, C., Turns, S., Gray, G., Costanzo, F., & Zappe, S. (2010). A Cognitive Study of Problem Solving in Statics. Journal of Engineering Education, 99(4), 337–337.Lutz, B. D., Ironside, A. J., Hunsu, N., Groen, C. J., Brown, S. A., Adesope, O., & Simmons, D. R. (2018). Measuring Engineering Students’ In-Class Cognitive Engagement: Survey Development Informed by Contemporary Educational Theories. ASEE Annual Conference & Exposition, Salt Lake City, UT. https://peer.asee.org/30795McCord, R. E., & Matusovich, H. M. (2019). Naturalistic observations of metacognition in engineering: Using observational methods to study metacognitive
talent at the crossroads,” National Academy of Sciences, National Academy of Engineering, and Institute of Medicine, Washington DC, 20115. W.B. Harvey, “American council on education (ace), minorities in higher education twenty- first annual status report (2003-2004),” American Council on Education, 1-100, 20056. P. Gurin, E.L. Dey, E.L. Hurtado, Gurin, P., “Diversity and higher education: Theory and impact on educational outcomes,” Harvard Educational Review, 72, 330-366, 20027. P. Gurin, B.R.A. Nagda, G.E., “The benefits of diversity in education for democratic citizenship,” Journal of Social Issues, 60(1), 17-34, 20048. A.L. Antonio, M.J. Chang, K. Hakuta, D.A. Kenny, S. Levin, & J.F. Milem, J. F. “Effects of racial
Paper ID #22785Citizen Scientists Engagement in Air Quality MeasurementsProf. Anthony Butterfield, University of Utah Anthony Butterfield is an Assistant Professor (Lecturing) in the Chemical Engineering Department of the University of Utah. He received his B. S. and Ph. D. from the University of Utah and a M. S. from the University of California, San Diego. His teaching responsibilities include the senior unit operations laboratory and freshman design laboratory. His research interests focus on undergraduate education, targeted drug delivery, photobioreactor design, and instrumentation.Katrina My Quyen Le, AMES High School
Pervasive,” J. Sci. Pract. Comput., vol. 1, no. 2, pp. 67–69, 2007.[2] Q. Bui, “Will Your Job Be Done By A Machine?,” Planet Money - The Economy Explained, 2015. [Online]. Available: http://www.npr.org/sections/money/2015/05/21/408234543/will-your-job-be-done-by-a- machine. [Accessed: 25-May-2015].[3] M. Weisser, “The Computer for the Twenty-First Century,” Sci. Am., vol. 3, no. 265, pp. 94–104, 1991.[4] S. Hambrusch, C. Hoffmann, J. T. Korb, M. Haugan, and A. L. Hosking, “A Multidisciplinary Approach Towards Computational Thinking for Science Majors,” ACM SIGCSE Bull., vol. 41, no. 1, p. 183, Mar. 2009.[5] P. B. Henderson, “Ubiquitous computational thinking,” Computer (Long. Beach. Calif)., vol. 42
Higher Education, TIAA Institute, April 2016. https://www.tiaainstitute.org/public/pdf/taking_the_measure_of_faculty_diversity.pdf. Accessed Feb. 11, 2017.[5] M. J. Finkelstein, V. M. Conley, J. H. Schuster. (2016). The Faculty Factor: Reassessing the American Academy in a Turbulent Era, Johns Hopkins University Press.[6] M. A. Mason, N. H. Wolfinger and M. Goulden. (2013). Do Babies Matter?: Gender and Family in the Ivory Tower. Rutgers University Press.[7] E. A. Cech and M. Blair-Loy. (2014) Consequences of flexibility stigma among academic scientists and engineers. Work Occupations 41(1):86–110.[8] S. Damaske, E. H. Ecklund, A. E. Lincoln & V. J. White. (2014). Male scientists’ competing devotions to work and family
supported coursewith an A or a B and is recommended for the position by their instructor. Many of these coursesare freshman-level mathematics and chemistry courses, as well as some sophomore-levelengineering courses. PAL leaders attend class for the section(s) they support so they are aware ofthe current material being discussed. This also allows them to build rapport with the instructor aswell as the students enrolled in the section(s) they support. Leaders then hold two 80 minutesessions each week. During sessions, leaders facilitate collaborative activities and studentdiscussions related to course topics as well as provide a safe place to ask questions and makemistakes along the way. We intentionally hire undergraduate students, rather than
deployed about a dozen more improved LynchBots to Iraq. His team also assisted in thedeployment of 84 TACMAV systems in 2005. Around that time he volunteered as a science advisor andworked at the Rapid Equipping Force during the summer of 2005 where he was exposed to a number ofunmanned systems technologies. His initial group composed of about 6 S&T grew to nearly 30 between2003 and 2010 as he transitioned from a Branch head to an acting Division Chief. In 2010-2012 he againwas selected to teach Mathematics at the United States Military Academy West Point. Upon returningto ARL’s Vehicle Technology Directorate from West Point he has continued his research on unmannedsystems under ARL’s Campaign for Maneuver as the Associate Director of Special
: Transforming undergraduate education for future research biologists”. Washington, DC: The National Academies Press, 2003.[2] F.A. Banakhr, M.J. Iqbal and N. Shaukat, "Active project based learning pedagogies: Learning hardware, software design and wireless sensor instrumentation," in 2018 IEEE Global Engineering Education Conference (EDUCON), Tenerife, Spain, April 17-20, 2018, pp. 1870-1874.[3] D. Perkins, “Beyond Understanding,” in Threshold Concepts Within the Disciplines, R. Land, J.H.F. Meyer, and J. Smith, Eds. Rotterdam: Sense Publishers, 2008, pp. 3-19.[4] D. Reeping, L. McNair, M. Wisnioski, A. Patrick, T. Martin, L. Lester, B. Knapp, and S. Harrison, “Using Threshold Concepts to Restructure an Electrical and Computer
; 1 SB Mentor Tenets: Community & Legacy 3 SB Growth ID Empathy Activity SS Basic Mentoring Skills D A D a S Hardware Introduction a PS Mental Health Skills for Mentors y SS Hacker Card Game y SS Afternoon Social 2 PS Art of Listening 4 SS Afternoon SocialThe training delivered by the Bulls-EYE PRIDE PI. Each day of the training program and itsusefulness to culturally responsiveness is described as followsDay 1: The first day of the training begins with introductions. Mentors mention what major theyhave declared and do brief
. Shekar, "Project-based Learning in Engineering Design Education: Sharing Best Practices", https://peer.asee.org/22949, 2014. [Online]. Available: https://peer.asee.org/project-based-learning-in-engineering-design-education-sharing-best-pr actices. [Accessed: 01- Feb- 2019]. [3] . Haag, N. Hubele, A. Garcia, and K. McBeath, “Engineering undergraduate attrition and S contributing factors,” Social and Personality Psychology Compass, 01-Jan-1970. [Online]. Available: https://asu.pure.elsevier.com/en/publications/engineering-undergraduate-attrition-and-contri buting-factors. [Accessed: 01-Feb-2019]. [4] P. Howard and P. Wolfs, Balancing project based and lecture centric education in a restructured
preferences is shown in Table 1. The value of nshown for each cohort is the total number of students registered (summing the numbers forMBTI preference pairs in the table yields a slightly lower value since not all students reportedtheir MBTI). The distribution of MBTI by gender is shown for all years in Table 2. Page 26.813.5Table 1: Distribution of MBTI Types by Cohort. I: Introversion, E: Extraversion, S: Sensing,N:iNtuition, T: Thinking, F: Feeling, J: Judging, P: Perceiving. Gender MBTICohort N M F I E S N T F J P 2013 121 91 30 66 54 64 56 92
), non-technical constraints (C),stakeholder considerations (S), broader considerations about cultural ecosystems (BC). We thencame to consensus on how we rated each consideration.Based on our analysis, the students of the CTSS class made a distinctive shift how theyprioritized design considerations for the energy-conversion playground design, as demonstratedin Figure 1. Notably, the aggregate number of considerations that centered on socioculturalconsiderations increased from 7 (10.3% of total responses) to 29 in the second iteration (41.4%of total responses). Moreover, the aggregate frequency of technical centered responses reducedfrom 26 in the first iteration (38.2% of total responses) to 4 in the second iteration (5.1% of totalresponses
trend in the UK is similar. For example, the UKPassport Office instead of seeking a witness for the applicant of a passport from the traditionalprofessions now includes the statement “…or professionally qualified person e.g. lawyer, engineer,doctor, school teacher, police officer or person of similar standing” [5].Engineering, although long considered a profession, has not remained static in this changing space.Ever since the engineering institutions were established in the UK they have sought prestige andstatus. In the 1950’s and 1960’s engineers in the UK argued for an equivalent organization to theRoyal Society and they were rewarded by the establishment of a Royal Academy of Engineering.Several years before that, American engineers had won
assimilationist goals, ratherthan attacking and undermining the very processes by which (some) subjects become normalizedand others marginalized” (3).9 Relatedly, as McRuer summarizes, representation is not a once-and-for-all attainment: “visibility and invisibility are not after all, fixed attributes that somehowpermanently attach to any identity” (2).5 Rather, we believe that small-n studies, relying ontechniques such as qualitative analysis or narrative reporting by subjects, may shed light onindividual and collective experiences that are far more layered than conventional STEMeducational research normally admits.Most profoundly, researchers’ very definition of their “n”s as small or large reiterates theanalytic value or necessity for established and
CPLD Provides a Third Option in the Introductory Logic Circuits Course,” ASEE National Convention, 2012, session W516, AC 2012-53025 D. Hodges, H. Jackson, and R. Saleh, Analysis and Design of Digital Integrated Circuits in Deep Submicron Technology, third edition, copyright 2004 by McGraw-Hill.6 S. Kang and Y. Leblebici, CMOS Digital Integrated Circuits, third edition, copyright 2003 by McGraw-Hill.7 D. Kolb, “Chapter Two: The Process of Experiential Learning,” Experiential Learning, Experience as The Source Page 26.1252.15 of Learning and Development, copyright 1984 by Prentice-Hall.8 K. Nickels, “Pros and Cons of replacing
environment anunderstanding level of learning is expected. As students progress to performing project work, orcollecting flight test data in a student only event an application level of learning is expected. Forboth practical and written final exams a correlation level of learning is expected.Finding the proper level of student engagementMost students at USAF TPS are atypical for a university environment. Entrance to the school ishighly selective, and as such most students already possess at least one post graduate degree, haveadvanced study skills, are extremely competitive and motivated, are usually in their late 20’s orearly 30’s, and all have shown significant military career progression potential. Student pilots areconsidered experts in their
methods were utilized toanalyze the data and report on the findings. Quantitative data analysis was conducted using SPSS(Software Package for Social Sciences). This study makes use of a variety of statistical tools in order to reach its conclusion, about95% confidence intervals were produced using hypothesis testing; Wilcoxon signed rank test andMann Whitney U test for non-parametric data to determine skills satisfaction gaps between preand post participation and skills and knowledge satisfaction as self-reported by the students afterhaving such experience. Reliability was demonstrated using Cronbach`s alpha in order to determine the internalconsistencies of the used satisfaction scales, Cronbach alpha values above 0.9 indicate