AC 2010-816: S-STEM: ENG^2 SCHOLARS FOR SUCCESS ENGINEERINGENGAGEMENTSarah Jones, Louisiana State University Sarah Cooley Jones is the College Programs Coordinator for the Office for Diversity Programs, College of Engineering at Louisiana State University. Ms. Jones develops and manages programs for underrepresented undergraduate and graduate engineering students. These programs include scholarships, seminar series and activities that develop the student academically and professionally. She joined LSU in 1992 as a College of Engineering research associate in the area of environmental analyses and worked on numerous projects including utilization of industrial by-products, water
AC 2010-259: YES: A NSF S-STEM SCHOLARSHIP PROGRAM EXPERIENCE ATTHE UNIVERSITY OF CENTRAL FLORIDALisa Massi, University of Central Florida Lisa Massi is the Director of Operations Analysis for the UCF College of Engineering & Computer Science. She serves as the primary educational analyst for the College and is a Co-PI of the NSF-funded S-STEM program at UCF entitled the "Young Entrepreneur and Scholar(YES) Scholarship Program." Dr. Massi's research interests include program evaluation, predictors of success in persistence to graduation and academic performance, entrepreneurial programs, and use of technology to improve operational efficiencies.Michael Georgiopoulos, University of Central
highfrequencies.Here we compute complex propagation constant in a biological material which has aconductivity of 1.6 S/m (muscle) and relative dielectric constant of 51 at the frequency of 915MHz. Intrinsic impedance is also calculated. 6 f := 915 ⋅10 σ := 1.6 εr := 51 − 12 ε0 := 8.854 ⋅10 ε := εr ⋅ε0 −7 ω := 2 ⋅π ⋅f µ0 := 4 ⋅π ⋅10 2 α ⎛ ε⎞ := ω ⋅ ⎜ µ0 ⋅ ⋅ 1
AC 2010-280: SPAIN'S MASTER OF LEADERSHIP IN CIVIL ENGINEERING:CASE STUDYStuart Walesh, S. G. Walesh Consulting S. Walesh is an independent consultant in the U.S. with previous experience in the private, government, and academic sectors.Javier Conde, National University of Distance Education J. Conde is Professor of Management at the National University of Distance Education in Madrid, Spain.Jose M. de Urena, University of Castilla-La Mancha J. M. de Urena is Professor of Urban & Regional Planning at the University of Castilla-La Mancha in Ciudad Real, SpainJose Turmo, University of Castilla-La Mancha J. Turmo is Professor of Structural Design at the University of Castilla-La Mancha
principal benefitsattributable to service-learning (S-L) dispersed through core required courses through surveys,interviews, and focus groups. As S-L continues to become a significant part of the community-engagement movement in higher education, and as more university professors are encouraged toincorporate S-L activities in their course requirements, it is essential that educators build anunderstanding of what students gain with S-L and that they give students a voice in their owneducational process and in the community. The service-learning (S-L) program SLICE (Service-Learning Integrated throughout a College of Engineering), based within the Francis College ofEngineering at the University of Massachusetts Lowell, began as a curricular reform
mechanical systems. Then, students will solve them by using a directmethod in the real domain and by applying the transform methods either in the frequency domain(Phasor Transform) or in the complex s-domain (Laplace Transform). Since the application oftransform techniques is much quicker and more efficient, especially when a real system carriesthe initial condition(s) or boundary conditions, we will focus on the Phasor Transform todetermine the steady-state response for an AC circuit and the Laplace Transform to derive thecomplete system solution, which includes transient and steady-state responses for both electricaland mechanical models. By offering a broad coverage of topics and case studies, this coursecould possibly be beneficial to the
Outcomes Requires Student Learning Outcomes courses for curriculum 1 2 3 4 5 6 CMG 101 M L CMG 105 L L M CMG 110 S S TGC 217 S TGC 218 S L CMG 250 L M CMG 300 M L CMG 301 M S CMG
grounded in SDT has found differences in factorsidentified as important to students in comparison to researcher assessed methods.17-19MethodsTo guide our research, we used case study methods20, 21 in combination with the self-determination theoretical framework. In our work, each participant represents a case and weanalyze within and across cases.22 The cases include eleven engineering students at apredominantly technical school (TPub) in the western mountain region of the United States.TPub is a public research university devoted to engineering and applied science. Approximately2,500, or 75 percent, of the 3,300 students are undergraduates, and about 80 percent ofbachelor‟s degrees earned annually are in engineering. Data for this study include
DevelopmentAs demonstrated by accounts such as Thomas Friedman‟s The World is Flat1 and the Engineer of2020 investigations by the National Academy of Engineering,2 engineering educators are onceagain focusing on necessary changes to our national engineering workforce. If there ever were anopportunity draw useful lessons from history, it would surely be on this topic. Concerns about an“engineering manpower” crisis persisted throughout the Cold War years in American history,fueled by massive federal expenditures and the emphasis placed on science and its application tothe nation‟s arsenal and economic wealth. Even as we proceed to transform, if not dismantle, theinstitutional apparatus developed to meet the exigencies of the Cold War period, it may well
undergraduate women in engineering approaches the issue interms of persistence or retention, examining factors influencing women‟s choices of major andcareer. Originally this work was driven by alarming data suggesting that women leaveengineering at higher rates than men.1,2 More recent studies suggest that women and men leaveengineering at equal rates during the college years.3,4Factors influencing persistence and attrition are often similar for men and women, but there aresome important differences. For example, Atman5 reported data from the Academic PathwaysStudy in which seniors identified motivating factors in their decisions to study engineering.Intrinsic psychological factors (liking engineering as a subject or field) and intrinsic
consists of course-level grade-book entries, Term GPAat MSOE and FHL, and Cumulative GPA at MSOE. The data then is compared to the data ofnon-exchange students at MSOE and FHL, respectively. This allows a one-to-one comparisonbetween exchange and non-exchange students without the need of additional academicperformance assessment measures and procedures. Since the curriculum of the exchangeprogram underwent a major change in 2001, only cumulative data from 2003 onward isdemonstrated here. This corresponds to data of 58 MSOE and 31 FHL exchange students.The Term GPA and Cumulative GPA of all MSOE exchange students is shown in Figure 5. Thehorizontal axis gives either the corresponding academic quarter at MSOE (F - Fall, W - Winter, S- Spring) for
Into the Practice of Civil Engineering at the Professional Level, Reston, VA, September. (http://www.asce.org/raisethebar) 12 4. Bloom. B. S., Englehart, M. D., Furst. E. J., Hill, W. H., and Krathwohl, D. 1956. Taxonomy of Educational Objectives, the Classification of Educational Goals, Handbook I: Cognitive Domain. David McKay, New York, NY. 5. Fridley, K.J., et al., 2009. Educating the Future Civil Engineering for the New Civil Engineering Body of Knowledge,” Proceedings of the 2009 ASEE Annual Conference, June 2009, Austin, TX
withoutgraduate degrees. In multiple instances, employers and/or graduate school representatives haveexpressed how impressive and important the undergraduate research experience was, not only inthe initial hiring and financial support decisions, but also in the rate and quality with which the Page 15.939.2new hires performed their responsibilities. The success of these students has been a majorcomponent of the author‟s positive reputation in this research arena.The author has made a strong effort to integrate undergraduate research in semiconductor andthin film materials with instruction. For example, he developed two lecture/laboratory coursepairs in
response rates and N were as follows: Fellow N=8, Response Rate=100% (8/8); ResearchAdvisors N-7, Response Rate=78% (7/9); Participating Teachers N=8, Response Rate=100%(8/8). One of the fellows has two advisors. Surveys were sent as an attachment to an email letterrequesting participation. Quantitative responses were indicated by the responder underlining or Page 15.667.4making bold their choice. Tabulation and data analysis were carried out by the evaluator withinput from the PI.Quantitative FindingsThe responses to fellows, advisors and teachers to the target themes in the surveys are shownbelow. Key: GX= to a great extent; S= somewhat; N= not at
Computational Introduction to STEM StudiesAbstractWe report on the content and early evaluation of a new introductory programming course “Media PropelledComputational Thinking,” (abbreviated MPCT and pronounced iMPaCT). MPCT is integrated into afreshman-level entering students program that aims at retaining students by responding to the academicrecruitment and attrition challenges of computer science and other STEM disciplines.This course is intendedto provide meaningful experiences of relevance to students choosing majors that also fortifies theirqualitative understandings of foundational math and physics concepts. MPCT‟s activities are designed to provide analytical challenges typical of STEM professions and tomotivate additional inquiry
many project alternatives that exist for cornerstone courses ([2]), the authors have lookedtowards a service-learning project as a means of achieving their course‟s design learningobjectives [6,7]. Service-learning is defined as a “method under which students learn anddevelop through active participation in thoughtfully organized service” [8]. In the context ofengineering design courses, projects centered in service-learning typically feature the studentteams designing a product or process that meets the needs of a community partner in need.Service-learning activities are becoming more prevalent in engineering curricula as instructorsdiscover that their pedagogical objectives of problem solving, working in groups, andexperiential learning
Learning Engineering Survey(APPLES). Five research questions were posed in the survey design: • Do women express a loss of interest during their program? • Is there a chilly climate for women in the college? • Do women‟s self-efficacy levels change during the program? • Do academic performance levels play a role in women‟s retention in engineering? • Do women have an adequate support structure in the college?The survey generated 116 responses from 2 solicitations, with women students representedfrom every major across all four undergraduate years. An unintended outcome was that thesample largely consists of women with high grade point averages. Thus, this paper offersinsight on top performing women‟s self-efficacy and
k CA CB (2)Here, the rate constant k has units of [m3 / (moles-s)]. The rate expressions for each reactant arerelated to the intrinsic reaction rate defined by equation (2) by their respective stoichiometriccoefficients where the latter are negative for reactants and positive for products. rA rB rC r (3) - 1 -1 1i. Model GeometryThe geometric model and dimensions are the same as that of the T-micromixer. A 3-D diagramof the model is shown Figure 1. Product Reactant B
outcomes were developed initially in draft form. Performance criteria were thenidentified for each of the outcomes. Rogers defines performance criteria as the “specific,measurable statements identifying the performance(s) required to meet the outcome and areconfirmable through evidence”24. Developing the performance criteria helped the faculty tofurther refine the outcomes. The number of performance criteria per outcome was limited to fouras an accepted rule-of-thumb as they were being developed. The template in figure one was usedas the learning outcomes and performance criteria were identified.The advisory board for the program was convened and asked to provide input on all theoutcomes and performance criteria that were identified by the
≡ = = = 0.1659 (1) Tl ωm ω$ mwhere Ti is the torque drained from the motor by the leadscrew (N·m), Tl is the torque supplied tothe leadscrew by the motor (N·m), l is the leadscrew angular velocity (rad/s), and m is themotor angular velocity (rad/s). The leadscrew pitch is Ta v v$ m p≡ = = = 2.022 ×10−4 (2) f a ωl ω$ l radwhere Ta is the torque drained from the leadscrew by the linear axis (N·m), fa is the forcesupplied to linear axis by the leadscrew (N
people who motivated you to engineering fields?Question 4-9 What do you think about the most effective assessment method(s) to verify student learning during the camp?Question 4-10 What do you think about the most effective team forming method(s)?Question 4-11 Can you state a unique YESTexas camp feature different than other usual summer camps? Page 15.154.5Improvements in camp application and participant selection. The application form wasimproved to comply with the requirements of the external funding agency and to enhance theselection process. Demographic, educational and career
National Science Foundation under Grant No. 0525484. Anyopinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and donot necessarily reflect the views of the National Science Foundation.learning and designs with more than one possible correct outcome depending on designconstraints; and because students could compete against their peers using the same designconsiderations.In 2009, the West Virginia University Engineers of Tomorrow research team reviewed regionalliterature on STEM career opportunities for Appalachian students, and noted, "It has long beensaid that high tech industries with higher paying jobs would improve the lives of residents of theAppalachian region. Careers in the sciences
traced back to a single member‟sinitiative in late December 2000. From that initiative, the MULTI Division membership hasgrown to its rank as the fifth largest ASEE Division in 2010. At this ten-year mark, it seemsappropriate to document this decade of development of the ASEE MULTI Division, as it is nowknown. This paper reviews the MULTI Division‟s development over its first decade with arecord of the highlights of each year of that first decade and the steps that led from that initiativeto recognition as an ASEE “constituent committee” in 2005 and then the MultidisciplinaryEngineering Division in 2006. This initiative also contributed significantly to ASEE‟s currentrole in the ABET EAC accreditation process. MULTI is now the fifth largest
argue that using real-world projects provides students exposure to working withchallenging clients and imperfect design information 2. Students need this experience with realworld problems to become effective civil engineers 3.This paper presents a synopsis of previous studies on engineering design courses in the nextsection, particularly those including industry collaboration. Next, the case study methodology is Page 15.159.2discussed in detail, including the new course‟s integration with the Civil Engineering Body of 1Knowledge for the 21st
Figure 3: A plot showing the z-plane annotated for discussing bandpass sampling.dents that allows them to evaluate Equation (1) in a way that promotes exploration and “what if” thinking.A simple m-file that provides this capability is shown in Listing 1, with an example output given in Figure 4for the bandpass signal parameters from Figure 2(a). Listing 1: M ATLAB program to evaluate valid sampling frequencies for bandpass sampling.f u n c t i o n vFs = bp samp ( fu , B )% vFs=bp samp ( f u , B )%% C r e a t e a s e t o f min and max v a l i d s a m p l e f r e q u e n c i e s% f o r bandpass sampling .% For Q= f u / B ,% 2B (Q / n ) <= Fs <= 2B ( ( Q− 1 ) / n −1))% where n i s an i n t e g e r s u c h t h a t 0> bp_samp
always proven to be verysuccessful. Even today, a large percentage of the deaf community has reading comprehensionand writing deficits and this has not changed much over the past 30 years.3When deaf or hard of hearing students arrive at college, they have high expectations ofthemselves for completing bachelor‟s and graduate degrees.4 The research led by Cuculick andKelly has shown through statistical analysis that only about 17% of incoming deaf students atNTID, 2001 and 2002 had the requisite reading and language skills to enter a baccalaureateprogram in their first year. Also, with the same data, it indicated that at NTID it takes longer forthe deaf students to complete Associate of Occupational Studies (AOS), Associated of AppliedScience (AAS
. Students in this knowledge-deficitsituation generally benefit from direct interaction with an instructor, such as in the traditionalvisit to an instructor‟s office for private one-on-one tutoring. In this personal interaction, theinstructor will assess „in real time‟ the student‟s understanding of any number of prerequisiteskills and knowledge and will adjust the direction and pace of the meeting, and ideally respondwith sensitivity, insight, and accuracy at teach point of assessment during the tutorial session.The long-term goal of this effort is to create, using interactive software, an effective substitutefor the one-on-one, across-the-desk tutorial experience for the advanced placement engineeringor engineering technology student needing
(1)where is the time constant (s), x(t) is the axis position (mm), K is the gain ((mm/s)/V), and FC isthe Coulomb friction (mm/s), which is modeled by ⎧ FC + if v (t ) > 0 ⎪ FC = ⎨ 0 if v (t ) = 0 (2) ⎪F if v (t ) < 0 ⎩ C−where v(t) is the axis velocity (mm/s). The X, Y, and Z axis parameters are listed Table 1.Ignoring Coulomb friction and using position and velocity as the system states, the linear axisstate
on characteristic patterns in time.A quantitative technology forecast includes the study of historic data to identify one of severalcommon technology diffusion or substitution models. Patterns to be identified include constantpercentage rates of change (so-called “Moore‟s Laws”), logistic growth (“S”- curves), logisticsubstitution, performance envelopes, anthropological invariants, lead/lag (precursor)relationships, and other phenomena. These quantitative projections have proven accurate inpredicting technological and social change in thousands of diverse applications, on time scalescovering only months to spanning centuries.Invariant, or well-bounded, human individual and social behavior, and fundamental humanagency and evolutionary drives