:10.1002/job.430[4] C.-P, Lin, & Y. -F. Chen. Modeling Team Performance. Journal of Leadership & Organizational Studies. 2015; 23(1), 96–107.doi:10.1177/1548051815616252[5] L. Melita Prati, C. Douglas, G. R. Ferris, A. P. Ammeter, M. R. & Buckley. Emotional Intelligence, Leadership, Effectiveness, and Team outcomes. The International Journal of Organizational Analysis, 2003; 11(1), 21–40.doi:10.1108/eb028961.[6] J. Fransen, P. A. Kirschner, & G. Erkens. Mediating team effectiveness in the context of collaborative learning: The importance of team and task awareness. Computers in Human Behavior, 2011;27(3), 1103–1113.doi:10.1016/j.chb.2010.05.017[7] S. Mohammed, B. C. Dumville. Team mental models in a team knowledge
provided helpful criticism that makesus more effective.This work is supported by the National Science Foundation’s Revolutionizing Engineering andComputer Science Departments (RED) program through Award #1519453.References[1] S. M. Lord, J. A. Meija, G. Hoople, D. Chen, O. Dalrymple, E. Reedy, B. Przestrzelski, andA. Choi-Fitzpatrick, “Creative Curricula for Changemaking Engineers”, Proceedings of theWEEF-GEDC 2018 Conference, Albuquerque, New Mexico, November, 2018.[2] S. M. Lord, B. Przestrzelski, and E. Reddy, “Teaching Social Responsibility: ConflictMinerals Module for a Circuits Class”, Proceedings of the WEEF-GEDC 2018 Conference,Albuquerque, New Mexico, November, 2018.[3] S. M. Lord, B. Przestrzelski, and E. Reedy “Teaching social
Medicine, How People Learn II: Learners, Contexts, and Cultures, Washington, DC: National Academies Press, 2018.[11] K. D. Tanner, "Promoting Student Metacognition," CBE-Life Sciences Education, vol. 11, pp. 113-120, 2012.[12] G. Schraw, K. Crippen and K. Hartley, "Promoting self-regulation in science education: metacognition as part of a broader perspective on learning," Research in Science Education, vol. 36, pp. 111-139, 2006.[13] T. D. Baird, D. J. Kniola, A. L. Lewis and S. B. Fowler, "Pink Time: Evidence of self-regulated learning and academic motivation among undergrduate students," Journal of Geography, vol. 114, no. 4, pp. 146-157, 2015.[14] A. Bandura, "Self-efficacy: toward a unifying theory of behavioral change
, HassanBadkoobehi, Laith Al Any, Jay Dey, and many others for their suggestions, encouragements,cooperation, and/or help during the preparation of this paper.REFERENCES[1] Ainsworth, S., (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183-198.[2] Bailey, T., Jenkins, D., & Leinbach, T. (2005). Community college low-income and minority student completion study: Descriptive statistics from the 1992 high school cohort. New York: Columbia University, Teachers College, Community College Research Center.[3] Braude, E., Bernstein, M. Software Engineering: Modern Approaches, (2nd Edition), John Wiley & Sons, 2011.[4] Brenner, M.E., Brar, T., Duran
,Washington, DC, 2010, p. F4H–1.[8] Morozov, D. Kilgore, and C. Atman, “Breadth in design problem scoping: Using insightsfrom experts to investigate student processes,” in ASEE Annual Conference and Exposition,Honolulu, HI, 2007.[9] S. Ingram and A. Parker, “The influence of gender on collaborative projects in an engineeringclassroom,” IEEE Trans. Prof. Commun., vol. 45, no. 1, pp. 7–20, 2002.[10] T. C. Brown and G. P. Latham, “The effects of behavioural outcome goals, learning goals,and urging people to do their best on an individual’s teamwork behaviour in a group problem-solving task.,” Can. J. Behav. Sci. Can. Sci. Comport., vol. 34, no. 4, p. 276, 2002.[11] C. O. L. H. Porter, “Goal orientation: Effects on backing up behavior, performance
rights reserved 11TRC Internship ProgramSchedule WEEK 1 – ONBOARDING AND INTRODUCTION WEEK 2 – CADD, PROJECTWISE & PROJECT MANAGEMENT (PM) & Project Support Time WEEKS 3 & 4 – SUBSTATION (S/S) & Project Support Time WEEKS 5 & 6 – PROTECTION AND CONTROLS (P&C) & Project Support Time WEEKS 7 & 8 – SYSTEM PROTECTION (SP) AND AUTOMATION & INTEGRATION (A&I) & ProjectSupport Time WEEK 9 – TESTING AND COMMISSIONING (T&C) WEEK 10 – FLOAT WEEK, CATCH UP & Project Support Time WEEK 11 – OVERVIEW OF OTHER GROUPS & Project Support Time WEEK 12 – FINAL PRESENTATION & Project Support Time
= 0.05 ± 0.01)compared with “Control” (average = 0.23 ± 0.08).No statistical difference was observed between the two methods for the other categories ofmistakes individually. p-values for categories 1, 2, 3, 4, and 6 were found to be 0.8, 0.23, 0.25,0.13, and 0.43, respectively. Figure 1: Comparison of mistakes per student for the eight classesOther observations. Mistake type 2 shows a significant reduction with time for instructor 1.However, this is attributed not to AR, but to the collaborative problem-solving that was part ofInstructor 1’s teaching method. This effect relates to fundamental conceptual learning achievedfrom peer teaching and has been studied in a separate work of the authors. [23]Mistake type 6 was generally
, Purdue University, West Lafayette Robin S. Adams is an Associate Professor in the School of Engineering Education at Purdue University and holds a PhD in Education, an MS in Materials Science and Engineering, and a BS in Mechanical Engineering. She researches cross-disciplinarity ways of thinking, acting and being; design learning; and engineering education transformation. c American Society for Engineering Education, 2019 Work-In-Progress: “I’m Not Your Standard Student”: Examining the Rationales for Pursuing an Interdisciplinary Engineering EducationAbstractThis Work-in-Progress paper in the Multidisciplinary Engineering Division begins to explore howundergraduate students use program
National Science Foundation for their support through a Graduate ResearchFellowship (DGE-1333468). Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect the views of theNational Science Foundation.References[1] C. E. Foor, S. E. Walden, and D. A. Trytten, ““I wish that I belonged more in this whole engineering group:" Achieving individual diversity,” J. Eng. Educ., vol. 96, no. 2, pp. 103–115, 2007.[2] J. M. Smith and J. C. Lucena, “‘How do I show them I’m more than a person who can lift heavy things?’ the funds of knowledge of low income, first generation engineering students,” J. Women Minor. Sci. Eng., vol. 22, no. 3, pp. 199–221, 2016.[3
innovation in engineering education necessitates research on ways of thinking. Wesought to gain this understanding based on four specific ways of thinking including futures,values, systems, and strategic thinking. The study builds on the existing body of knowledgeregarding these ways of thinking, while initiating a first step toward an ‘EER ways of thinking’model. We believe the resulting model could serve as an organizing and motivating structure toframe decisions throughout all engineering education endeavors.ReferencesBrown, T. A. (2015). Confirmatory factor analysis for applied research, 2nd edition. New York, NY: Guilford PublicationsCrawford, A. V., Green, S. B., Levy, R., Lo, W. J., Scott, L., Svetina, D., & Thompson, M. S. (2010
-direction calculated using images from the mobile phone and high-speed camera and v is the velocity in the y-direction calculated from images using the mobilephone and high-speed camera. Overall, the difference between the mobile phone and the high-speed camera setup is low (max error less than 0.2 m/s) (Figure 7). Figure 8. Difference between mobile PIV and industrial PIV using the absolute difference (Eq. 1). Overall, calculated velocity was similar between the two setups.ConclusionsWe completed a preliminary proof of concept of a mI-PIV device that will be refined forimplementation in classrooms in both high school and undergraduate levels. Our PIV tool isinexpensive, designed using open access image analysis code, and fully mobile. We
of 3-D printed block that took 8 hours to print. (B) Assembled 3-Dprinted robot puppet prior to adding primer, paint, and weathering effects. (C) Finished robot on day of shooting in front of a green screen. References[1] J. W. Bequette and M. B. Bequette, “A place for art and design education in the STEMconversation,” Art Education, vol. 65, no. 2, pp. 40-47, Mar. 2012doi:10.1080/00043125.2012.11519167[2] S. Fischer, D. Oget, and D. Cavallucci, “The evaluation of creativity from the perspective ofsubject matter and training in higher education: Issues, constraints and limitations,” ThinkingSkills and Creativity, vol. 19, pp. 123-135, Mar. 2016. doi:10.1016/j.tsc
helped them in their undergraduateeducation to succeed. The goal of using this analysis, as consistent with founders of themethodology [12], is to develop a theory during textural analysis without preconceived ideas onwhat the solution, or theory, could be. GMT was developed in the 1960’s to give sociologists atool that allowed them to generate new theories. It has begun to be adopted by the designdisciplines to help navigate the fuzzy front end of design by coding observations in transcripts,for example. The idea is that stories can emerge, and connections can be made betweenunrelated ideas and help form potential hypotheses [13]. In this initial coding exercise, word-by-word, and line-by-line coding strategies were employed, as described by
the effectiveness of the workshop relativeto the benefits to SVS gained from participation in the class only.References1. S. Sorby, “Educational Research in Developing 3-D Spatial Skills for Engineering Students,” International Journal of Science Education, vol. 31, no. 3, pp. 459-480, 2009.2. J. Wai, D. Lubinski, and C. P. Benbow, “Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance,” Journal of Educational Psychology, vol. 101, no. 4, pp. 817-835, 2009.3. M. B. Casey, E. Pezaris, E., and R. L. Nuttall, “Spatial ability as a predictor of math achievement: the importance of sex and handedness patterns,” Neuropsychologia, vol. 30, pp. 35-40, 1992.4. D. Halpern, D
expectationsfrom engineering and technology graduates. To stay competitive, engineering andtechnology students need to learn the latest software used in their associated fields as wellas to understand relevant modeling and simulation frameworks. To provide students abetter learning experience discrete-event modeling software based hands-on learningexamples are developed and implemented for the junior level Facilities Planning course.This paper shares examples of the hands-on learning activities that are incorporated intothe Facilities Planning course.IntroductionAccording to the International Facility Management Association (IFMA)’s Profiles 2011Salary and Demographics Research Report, the average facility manager is “personallyresponsible for the entire
given survey was paper and pencil format. The end of course survey consisted oftwo parts: Likert scale items and three open-ended questions. The Likert scale items askedstudents “to what extent do you agree that each of the following topics improved your ability toeffectively interact with your partner(s) in the problem-solving studio?” Eleven topics oninterpersonal skills were given including i.e. constructive feedback, selective attention, effectivelistening. Each topic was given with a 6 point Likert scale ranging from 0 – I don’t recall thistopic, 1 – disagree strongly, to 6 – agree strongly. Student mean scores ranged from 0 – 6. Eachtopic was scored for overall mean therefore, if a student answered zero on the Likert scale thezero was
minutes. Most students correctly solvedthe seventh level on the first try, suggesting they had learned the objective. We took a look atsubmissions by students who made many attempts. One such student needed 4 tries to completelevel 1, 2 tries for level 2, 1 try for level 3, 4 tries for level 4, 1 try for level 5, 10 tries for level 6,and 1 try for level 7. The student spent about 5 minutes in total. Two weeks later, the samestudent worked through the activity again, perhaps preparing for an exam, and completed in justover 1 minute and making only 3 incorrect submissions across all levels. Note: The sectioncovering K-map has multiple challenge activities, and this is just 1 of them.6. Challenge activity: Enter output of an SR latch given input s
] Jackson, V. A., Palepu, A., Szalacha, L., Caswell, C., Carr, P. L., & Inui, T. (2003). “Having the right chemistry”: a qualitative study of mentoring in academic medicine. Academic Medicine, 78(3), 328-334.[8] Sorcinelli, M. D., & Yun, J. (2007). From mentor to mentoring networks: Mentoring in the new academy. Change: The Magazine of Higher Learning, 39(6), 58-61[9] van Emmerik, I. J. H. (2004). The more you can get the better: Mentoring constellations and intrinsic career success. Career Development International, 9(6/7), 578.[10] Schrodt, P., Cawyer, C. S., & Sanders, R. (2003). An examination of academic mentoring behaviors and new faculty members’ satisfaction with socialization and tenure and promotion
parameters for industrial engineering education in South Africa. South African Journal of Industrial Engineering, Vol 28, Iss 1, Pp 114-124 (2017). 2017;(1):114. doi:10.7166/28-1-1584.[6] Palma M, Ríos I de los, Guerrero D. Higher Education in Industrial Engineering in Peru: Towards a New Model Based on Skills. Procedia - Social and Behavioral Sciences. 2012;46:1570-1580. doi:10.1016/j.sbspro.2012.05.342.[7] Ferraras, A., Crumpton-Young, L., Rabelo, L., Williams, K., and Furterer, S., (2006) “Work in Progress: Developing a Curriculum that Teaches Engineering Leadership & Management Principles to High Performing Students,” Proceedings of the 2006 Frontiers in Education Conference, San Diego, CA.[8
, 2012.[2] National Academy of Engineering, “Educating the engineer of 2020: Adapting engineering education to the new century.” Washington, DC: The National Academies Press, 2005. Available: https://doi.org/10.17226/11338.[3] M. Besterfield-Sacre, M. Moreno, L. J. Shuman, and C. J., “Gender and ethnicity differences in freshmen engineering student attitudes: A cross-institutional study.” Journal of engineering Education, vol. 90, no. 4, pp. 477-489, 2001.[4] S. Kumar and J. K. Hsiao, “Engineers learn ‘soft skills the hard way’: Planting a seed of leadership in engineering classes.” Leadership and Management in Engineering, vol. 7, no. 1, pp. 18-23, 2007.[5] D. C. Davis, S. W. Beyerlein, and I. T. Davis
faculty mentor working with the student(s) receives a stipendranging from $1,000-$1,500.The SURE Program strives to improve student skills integral to performing research. Studentsand their research mentors are expected to work together for eight hours per week for one-on-one instruction and research skill development. In addition to conducting research with facultymentors, mentees are required to attend four lunch meetings throughout the summer experience.These meetings focus on professional development, mentoring, and providing an opportunity forstudents to discuss research progress with peers. In the first meeting, staff from the campusMultimedia Services Office conduct a poster preparation workshop in which they teach thebasics of designing a
. Sestito, A. Harel, J. Nador, and J. Flach, "Investigating Neural Sensorimotor Mechanisms Underlying Flight Expertise in Pilots: Preliminary Data From an EEG Study," Frontiers in Human Neuroscience, Report 2018.[3] S. Puma, N. Matton, P.-V. Paubel, É. Raufaste, and R. El-Yagoubi, "Using theta and alpha band power to assess cognitive workload in multitasking environments," International Journal of Psychophysiology, Article vol. 123, pp. 111-120, 1/1/January 2018 2018.[4] G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Review: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness," Neuroscience and
early 2000’s aimed at guiding educators in the development of “Engineers for 2020.” Thereports addressed many global factors and encouraged universities to integrate curricula withexperiences that would lead to graduates who are prepared to enter a much moreinternationalized workforce by 2020. The need for these experiences has been widely embraced,and the vehicles for achieving that goal have taken many forms [1].No single program, or even one type of program, will achieve these goals alone. A multi-pronged approach, with many different aspects is necessary to reach students [2], [3]. TheUniversity of Dayton has a well-resourced and effective Center for International Programs.There are myriad opportunities to study abroad, take courses in
they have the interview with the professor.The interview with the professor involves a dialogue tree that allows the participant to choosehow they wish to respond in real-time in the conversation. This ability, coupled with theparticipant having Becky’s vantage and mirrored body movements, enables participants to feelmore immersed as the actual character. Although the evolution of conversation is dependentupon the selections of the participant, there are key statements made by the professor that areindependent of the participant’s response. These statements reflect what is constant in allinteractions. Specifically, all constants in the dialogue involve at least one of the followingconcepts—(P)rejudice, (R)acism, (I)mplicit bias, (S)exism, (M
workforce and empowering those interested in STEM, regardless of their background. Dr. Huderson was a 2015-2017 American Association for the Advancement of Science, Science and Technology Policy (AAAS S&T) Fellow in the Engineering Education and Centers’ division (EEC) at the National Science Foundation, where she provided leadership on developing, coordinating, and im- plementing support for programs that foster an inclusive climate for pre-collegiate and collegiate STEM students. Currently Dr. Huderson serves as the Manager of Engineering Education at the American Soci- ety of Mechanical Engineers (ASME), where she is responsible for advancing and managing the research, development, promotion, implementation
Med Plains 12 83 VH7 Public L RU/VH Prof+AS Med Plains 11 91 B-L1 Public L RU/VH Bal Large Southwest 17 82 B-L2 Public L RU/VH Bal Large Great Lakes 15 60 B-L3 Public L RU/VH Bal Large Great Lakes 11 64 VH-B Public L RU/VH Bal Med Mid East 13 38 H-B Public RU/H Bal Med New England 9 100 H-Pf-S Public
), andsupport that members of students’ community provide to aid them in their engineering coursework.Community networks encompass four subthemes: students’ family members, networks at work,neighborhood friends, and university friends. Each of the four subthemes prompted students toindicate to what extent the following statements were true using a 7-point anchored numeric scalefrom 0- “Not at all true” to 6- “Very True.” Sample items for each subtheme of communitynetworks include, “Friend(s) from my neighborhood have given me resources that helped me inmy engineering coursework,” “Friend(s) in my current school have given me resources that helpedme in my engineering coursework,” “Family member(s) have given me resources that helped mewith my
interviews. Through thisprocess, using our theoretical framework the codebook will be expanded, refined, compacted,and sub-categorized. After the refinement, the codebook will be reflected on paying particularattention to the differences between the first-year and senior level participants. These reflectionswill be used to determine how students’ beliefs affect their learning in order to generaterecommendations on improving engineering educational practices to increase retention andstudent learning.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.#1738209. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily
design and implementation ofcollaborative ill-structured tasks using a research-based framework that outlines the necessaryelements of such tasks: an introduction to the problem that provides context, a description of theproblem itself, the specific task(s) students are expected to achieve as a group, supplementarymaterial that provides information useful for solving the task, and scaffolding tools that studentscan use to develop plans, draw diagrams, and generate solutions [6]. This paper presents amethod to evaluate the design of ill-structured tasks in relation to the interaction processes thatstudents used in their groups. The paper showcases the use of our method by evaluating thedesign of one ill-structured task, and provides suggestions
∠𝜃 phasor(Zm, th) Admittance Y polar 𝑌 = 𝑌 ∠𝜃 phasor(Ym, th) Power S polar 𝑆 = 𝑆 ∠𝜃 phasor(Sm, th)All above vectors are modeled as Objects under a phasor class. These objects interact like ordinarymathematical variables. Phasor objects can be added, multiplied and divided using same operators“+, -, *, / and left divide \ matrix solution” as are used in traditional mathematical operations involv-ing constants and variables.2.1 Algebraic Operations and VisualizationFollowing example shows three phasors V1, V2 and V3 are added, and visualized and addedgraphically in the complex plane, The two phasors V1 and V2 are specified by