mentoring of students, especially women and underrepresented minority students, and her research in the areas of recruitment and retention. A SWE Fellow and ASEE Fellow, she is a frequent speaker on career opportunities and diversity in engineering. c American Society for Engineering Education, 2016Highlights of Over a Decade of University/Community College PartnershipsAbstractIn 2002, an NSF sponsored (# 0123146) S-STEM academic scholarship program for upperdivision engineering and computer science (designated as ENGR) students materialized atArizona State University with about half of the students being transfer students. This directedattention to the need for more support for potential and actual transfer ENGR
Support Hands-on Learning in the Teaching of Control and Systems Theory,” Engineering Education, vol. 9, no. 1, pp. 62–73, Jul. 2014.[5] P. S. Shiakolas and D. Piyabongkarn, “Development of a real-time digital control system with a hardware-in- the-loop magnetic levitation device for reinforcement of controls education,” IEEE Transactions on Education, vol. 46, no. 1, pp. 79–87, Feb. 2003.[6] R. M. Reck and R. S. Sreenivas, “Developing a new affordable DC motor laboratory kit for an existing undergraduate controls course,” in American Control Conference (ACC), 2015, 2015, pp. 2801–2806.[7] S. S. Nudehi, P. E. Johnson, and G. S. Duncan, “A control systems laboratory for undergraduate mechanical engineering
Paper ID #15618Collaboration between Seniors and Freshmen on Senior Capstone ProjectsProf. 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.Kyle Joe Branch
engineering librarians in thoseservices. The study involved the engineering librarians at all United States Class 15 (Very HighResearch Activity (RU/VH)) and Class 16 (High Research Activity (RU/H)) institutions per the2010 Basic Carnegie Classification of Institutions of Higher Education. The Classifications DataFile can be obtained at http://carnegieclassifications.iu.edu/2010/resources/. IRB clearance forthe survey was obtained from both [university A] and [university B]. The authors gathered the e-mail addresses of the engineering librarian(s) by inspection of the library website of eachinstitution. The survey was meant to elicit responses from a population that include theengineering librarians at all doctoral degree granting institutions
fair was used to make families aware of the manySTEM resources in Boston as well as to pique their interest in STEM. Engaging families is apriority of the LSA in order to encourage parents to advocate for STEM offerings in schools, aswell as to encourage the parents, who are often very young, to consider STEM education andcareer pathways for themselves.Another key feature of this event was the participation of NSF S-STEM electrical engineeringscholars from Suffolk University, who are graduates of Boston Public High Schools and who arepredominantly students of color themselves. These students engaged the fair participants inhands-on experiments about energy and electricity and served as role models for the participantsand their families
upon work supported by the National Science Foundation under Grant No.1262806. 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. Graduate students Mr. Andreas Febrian, Mr. Matthew Cromwell, Mr. Moe Tajvidi,Ms. Maria Manuela, and Mr. Ben Call are acknowledged for their efforts in assisting inmentoring REU students. The project external evaluator Dr. Margaret Lubke is alsoacknowledged for her efforts in conducting independent evaluation of this program.Bibliography[1] Russell, S. H., Hancock, M. P., and McCullough, M., 2007, “The Pipeline: Benefits of Undergraduate Research Experiences,” Science, Vol
“engineering intuition.”References1 Raskin, P. Decision-Making by Intuition--Part 1: Why You Should Trust Your Intuition. Chemical Engineering 95, 100 (1988).2 Gigerenzer, G. Short cuts to better decision making. (Penguin, 2007).3 Kahneman, D. Thinking, fast and slow. (Farrar, Strauss, and Giroux, 2011).4 Elms, D. G. & Brown, C. B. Intuitive decisions and heuristics–an alternative rationality. Civil Engineering and Environmental Systems 30, 274-284 (2013).5 Dreyfus, S. E. & Dreyfus, H. L. A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition (A155480). (1980).6 Chen, J. C., Whittinghill, D. C. & Kadlowec, J. A. Classes that click: Fast, rich feedback to enhance
Science Foundation (CNS #1138469, DRL#1417835, and DUE #1504293), the Scott Hudgens Family Foundation, and the Arthur M. BlankFamily Foundation.References[1] J. M. Wing, “Computational thinking and thinking about computing,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 366, no. 1881, pp. 3717–3725, 2008.[2] M. Guzdial and E. Soloway, “Teaching the Nintendo generation to program,” Commun. ACM, vol. 45, no. 4, pp. 17–21, Apr. 2002.[3] A. Bruckman, M. Biggers, B. Ericson, T. McKlin, J. Dimond, B. DiSalvo, M. Hewner, L. Ni, and S. Yardi, “‘Georgia computes!’: improving the computing education pipeline,” in Proceedings of the 40th ACM technical symposium on Computer
implementation. Peer Review, 16(1), 1-8. Retrieved from https://www.aacu.org/peerreview/2014/winter/linking-advising-and- eportfolios-for-engagementAshikin, H. T., Ruhizan, M. Y., & Rohani, S. (2015). E-portfolio model development for the professional practice bachelor of teaching (PISMP) in Malaysia. Procedia - Social and Behavioral Sciences, 174, 1262-1269. http://dx.doi.org/10.1016/j.sbspro.2015.01.746Cheng, S.-I., Chen, S.-C., & Yen, D. C. (2015). Continuance intention of E-portfolio system: A confirmatory and multigroup invariance analysis of technology acceptance model. Computer Standards & Interfaces, 42, 17-23. http://dx.doi.org/10.1016/j.csi.2015.03.002Dunbar-Hall, P., Rowley, J
Emulation Engine (BEE) (both Ettus Research and BeeCube were part of NationalInstruments Corporation now), Rice University’s Wireless Open-Access Research Platform(WARP), Microsoft Research’s Software Radio Platform for Academic Use (SORA), andDatasoft’s Typhoon SDR Development Platform. Due to the highest versatility for lowest cost,USRP N200 kit 18 and SBX daughterboard 18 that provides 400 MHz-4400 MHz accessiblefrequency range were selected for the REU project and the educational module presented in thefollowing two sections. The main component of the USRP N200 kit is a motherboard thatconsists of a Xilinx Spartan FPGA for all the physical layer functions such as filtering,modulation/demodulation and other baseband signal processing, 100 MS/s
these identity frameworks in the broaderliterature. To be fair, in the broader literature there have only been a few claims that identity isexplicitly distinct from other constructs such as self-efficacy2 or the expectancy-value theory ofachievement motivation.3 However, in the last five years some have made this distinction. Forexample, Lent, R. W., Brown, S. D., & Hackett, G.4 expand on Bandura’s theory of self-efficacyto the extent of illuminating the importance of self-efficacy in academic persistence. While thisis not explicitly identity, self-efficacy is a theoretically relevant construct that had to be takeninto consideration in this review as it is often associated with identity measures.Table 1 Categorization of Identity Studies by
instruction. College teaching, 44(2), 43-47. 2. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. PNAS 11 (23), 8410- 8415.3. Jungst, S., Likclider, L. L., & Wiersema, J. (2003). Providing Support for Faculty Who Wish to Shift to a Learning-Centered Paradigm in Their Higher Education Classrooms. The Journal of Scholarship of Teaching and Learning 3(3), 69-81.4. Felder, R. M., & Brent, R. (1996). Navigating the bumpy road to student-centered instruction. College teaching, 44(2), 43-47.5. Prince, M. (2004). Does Active Learning Work? A Review of the Research
1 = Black/African American Louisiana Residency (State) 0 = Non-Resident 1 = Resident High School Rank (HSRank) 0.2 – 100 High School GPA (HSGPA) 1.59 – 4.0 ACT component scores Science Score (ACT S) 7 – 36 Mathematics Score (ACT M) 14 – 36 English Score (ACT E) 11 – 36 Reading Score (ACT R) 12 – 36ParticipantsThe participants involved in this study include first-time-in-college (FTIC) freshmen whoentered the university in any school year between 2006 and 2015 and declared an engineeringdiscipline as their major. Enrollment in a university seminar class that all FTIC freshmen
increases. Thus we denoteproduction cost as ci (z), where the first derivative ciz < 0. In addition, let s(β, γ)denote the collaboration cost. The mathematical model for the firm’s payoff is: ΠI = b1 z − M − s(β, γ) − ci (z) (2)where b1 is a positive constant and b1 >> 0. We assume furthermore that s(β, γ)is convex with respect to both β and γ. The collaboration cost increases as the GAME THEORY APPROACH ON A UNIVERSITY-INDUSTRY COLLABORATION MODEL 7relevance γ decreases, but at a decay rate. That is, sγ < 0 and sγγ > 0. And ci (z)is also convex with respect to z.2.5. Formulation of the University’s Model. The payoff of the university fromthe collaboration
provide further insight intostudent perceptions. The following observations are noted for data summarized in Table 7: The highest survey response (96%) was noted for perceived student understanding of professional and ethical responsibility. This outcome also has the lowest standard deviation (9%) indicating a concurrence of student perception on this professional skills outcome and providing further evidence of a strong positive response.Table 5. FE Exam Ethics and Business Practice Results, 2009-2015 (n=220) FE Exam Institution CE National Avg. Ratio of Institutional Avg. Administration Avg. % Correct % Correct % Correct / National Avg. S 2009 (2) 88
: M = {X, Y, S, ta, δext , δint , λ},Where:X - set of input events;Y - set of output events;S - set of sequential states (also called the set of partial states);ta - time advance function used to determine the lifespan of a state;δext : Q × X → S - the external transition function defining how an input event changes astate of the systemδint : S → S - the internal transition function describing the way how system state changesinternally ϕ ϕλ :S →Y - is the output function where Y =Y ∪{φ} and φ ∉Y is a ”silent” or an”unobserved” event.Our model consists of the several equipment units represented as atomic models. Units statesare updated dynamically starting from the physical representation of the
activities were internalized, benefitted their development, and could possibly be improved to maximize impact on subsequent cohorts.A. Academic outcomes from the project C.1 The objectives of this project were consistent with my research interests C.2 This experiential learning project had an impact on my hands-on/laboratory skills and data collecting skills Which one(s) in particular? C.3 This project had an impact on my presentation skills Which ones(s) in particular? C.4 This project developed my technical skills C.5 This activity enhanced my content knowledge? C.6 I was able to integrate knowledge from many different sources and disciplines (example, chemistry, biology, engineering, technology, computer science, environmental sciences, etc)B
. Responses Questions Team consisting of Team consisting of two students individual student (one h/w focused and one s/w focused)Approximate time • 55 total hours (30 hours for s/win hours you • 24 hours focused student and 25 hours for h/wworked on this focused student)projectLevel of difficulty(1 5, with 1 asextremely easy, 3 as • 4.3 for s/w focused student • 4moderately difficult, • 4 for h/w focused student5 as extremelydifficult
survey examinesthese collaborative relationships only in the United States, while it is important to include foreignliterature in the historical development of these relationships.BackgroundIndustry-academia collaboration is not a new concept as we find the earliest discussion occurringat the end of the 1960’s,3 in Russia. These collaborations sponsored by the governments ofcountries4,5 interested in promoting this kind of activity, eventually became individualrelationships between companies and universities throughout the rest of the world. Currentliterature indicates that such relationships became more of the norm in the late 1990’s and in thelast decade commonplace in various forms. Recently, consideration of minorities, women, andother
is having difficulties in their process and step in to assist.Design challenges provide a safe environment for students to feel the pressure of working on achallenge problem with a tight timeline. However, the stakes are not so high that failure iscatastrophic. In addition, they see where they are failing and work to develop methods toanticipate failure conditions and avoid them. Further studies need to be performed to determineif students’ increase in skills and confidence transfer to their other design experience in theiracademic and professional careers.REFERENCES 1. ABET. (2000). ABET Engineering criteria 2000: criteria for accrediting programs in engineering in the United States. 2. Jamieson, L., Brophy, S., Houze, N
0 0 3For calculating the TE values represented in table 2, based on TE equation, joint probabilities arecalculated for emerging node degrees observed in table 1. Table 2. Transfer Entropy values calculated based on table 1 Source Node Destination Node Transfer Entropy Transfer Entropy (S) (D) (S-D) (D-S) N1 N2 0 0.2442191 N2 N3 0 0.2073259 N3 N4 0.09370405 0 N4 N5 0.150515
Engineering Education, 2016 Performance of Engineering and Engineering Technology Scholars in the Transfer Pipeline (TiPi) ProgramAbstractThis paper introduces the Transfer Pipeline (TiPi) Scholars’ program funded by the NationalScience Foundation (NSF) that focuses on students who transfer at the 3rd year level from 2-yearschools to our university. The objectives of the TiPi program are: (i) to address a nationalconcern by helping to expand the engineering/technology workforce of the future, (ii) to developlinkages and articulations with 2-year schools and their S-STEM programs, (iii) to serve as amodel for other selective universities to provide transfer students the access to the baccalaureate,(iv) to give scholars hands-on
- Non- STAR Non- STAR STAR STARS STAR S STARS S S S Year-to-year retention in N/A N/A 73% 62% N/A N/A Engineering Year-to-year retention at N/A N/A 77% 73% N/A N/A university Average cumulative GPA 2.26 2.34 2.64 2.80 2.74 2.35 Performance in math courses 1.96 1.68 1.85 2.04 2.68 2.35 Performance in
advancedconcepts about robotics also will be used in research for graduate students in many applicationsuch surveillance applications. The software will be composed of ten modules. The developedsoftware system allows a mobile robot attached with the robotics arm to navigate in anenvironment autonomously. The mobile robot accepts the commands from the human being(operator) using three different techniques. The mobile robot starts navigating to detect manyobjects based on color(s) and shapes, and also sends these information back to the operatorthroughout Graphical User Interface (GUI). With a camera attached to the mobile robot, thesoftware will be able to classify the objects based on color (s) and shape(s), and to determineits/their position. The
. (2010). Rising Above the Gathering Storm, Revisited: Rapidly Approaching Category ByMembers of the 2005 "Rising Above the Gathering Storm" Committee; Prepared for the Presidents of the National Academyof Sciences, National Academy of Engineering, and Institute of Medicine. Washington, DC: National Academies Press.10. National Research Council. (2012). Discipline-Based Education Research: Understanding and Improving Learning inUndergraduate Education. S. R. Singer, N. R. Nielsen, and H. A. Schweingruber, Editors. Committee on the Status,Contributions, and Future Directions of Discipline-Based Education Research, Board on Science Education, Division ofBehavioral and Social Sciences and Education. Washington, DC: National Academy Press.11
microfluidic networkof channels, conduits, chambers, filters, and flow control components [9]. Relative to traditionalmacroscale systems, ‘lab on a chip’ systems yield noteworthy advantages including more precisecontrol of reactants faster reaction time, lower consumption of reagents, convenient disposal,effective containment of infectious agents or hazardous substances, portability, and compactness.Lab-on-a-chip applications such as polymerase chain reactions (PCR) to amplify nucleic acids, aswell as cell cultures, need closely regulated heating and cooling with temperature control (often ±0.5 °C) and fast thermal response times (> 5 °C/s) [4]. For such applications, infrared thermalcameras offer non-contact measurement of temperatures and two
scenario indicates that Dice.com data would not be a wise choice forthe OPC course design and review process.The reason why the professor is looking for a particular website here is that a job may beadvertised across multiple websites at a time. Aggregating the data may exaggerate the ratings ofthe topics. For example, company X may post a job description that contains ProductionScheduling on all the five websites. Summing up the topic across all the websites would give anequivalent rating of frequency five while the topic should have received a rating equivalent tofrequency one.The professor may also need to explore more topics that s/he has not yet provided a rating for.For example, the professor needs time allocation recommendations for the
Engineering Professionals—Russia, India, AmericaAbstractIn this global world, today’s engineer is likely to have to work in global international teamswith colleagues from other nationalities. The challenge for many engineering curricula is howto include, in a realistic way, this global dimension and increase the student’s awareness ofthe issues that are encountered. However as curricula begins to be developed, it would bebeneficial to study what the differences might be between cultures.To expose the issues that may be encountered for future multidisciplinary teams made up ofstudents from USA, Russia and India, the Miville Guzman Universal Diversity Scale(MGUDS-S) survey and form which assesses cross cultural diversity
collectivistic cultures? A purposive sampling was used to recruit the qualitative participants who met minimumcriteria. Seventeen participants met the criteria (lived experiences of international assignment(s)in MENA) and provided the answers to the interview questions. The study includes a sub-question that give depth and detail in relation to the phenomenological research question. Thepurpose of the qualitative research question (RQ) and sub-research questions (SRQ) was togather participants lived experiences of American global expansion. An open-ended interviewquestions developed from the research questions. SRQ 1: How do business leaders and decision makers working outside the United Statesdescribe the experience of changes in