Paper ID #35175Work in Progress: Spatial Visualization Assessment and Training in theGrainger College of Engineering at the University of IllinoisDr. Brian S. Woodard, University of Illinois at Urbana - Champaign Dr. Woodard received his Ph.D. in Aerospace Engineering from the University of Illinois at Urbana- Champaign in 2011. His Aerospace research interests currently focus on the effects of icing on the aero- dynamics of swept-wing aircraft. In engineering education, he is interested in project-based learning and spatial visualization. He teaches courses at the University of Illinois where he serves as the Director of
IEEE Signal Proc. Society, Speech & Language Proc. Tech. Comm.(SLTC), and Technical Advisor to U.S. Delegate for NATO (IST/TG-01). He currently serves as President of ISCA (Inter. Speech Comm. Assoc.). He has supervised 92 PhD/MS thesis candidates, was recipient of 2020 UT-Dallas Provost’s Award for Grad. Research Mentoring, 2005 Univ. Colorado Teacher Recognition Award, and author/co-author of +750 journal/conference papers in the field of speech/language/hearing processing & technology.Dr. Dwight Irvin, Juniper Gardens Children’s ProjectDr. Beth S Rous, University of Kentucky Dr. Beth Rous is a professor and researcher who works with students and organizations to apply research to generate new knowledge
Joanneum University of Applied Sciences. Prior to his studies, he attended a HTL, a technical secondary school that specializes on Mecha- tronics and Automatisation.Mr. Christian J. Steinmann, HM&S IT Consulting Christian Steinmann is manager of HM&S IT-Consulting and provides services for Automotive SPiCE. Currently, his main occupation is process improvement for embedded software development for an auto- mobile manufacturer. On Fridays, he is teaching computer science and programming courses at Joanneum University of Applied Sciences in Graz, Austria.Mr. Alexander Tretton Alexander Tretton is currently a student at the Joanneum University of Applied Science and started his studies in automotive engeneering
for several years. She holds B.S. in Computer Engineering and M.S. in Industrial Engineering. She received her Ph.D. in Industrial and Systems Engineering from Binghamton University (SUNY). Her background and research interests are in quality and productivity improvement using statistical tools, lean methods and use of information technology in operations management. Her work is primarily in manufacturing and healthcare delivery operations.Dr. Ronald S. Harichandran, University of New Haven Ron Harichandran is Dean of the Tagliatela College of Engineering and is the PI of the grant entitled Developing Entrepreneurial Thinking in Engineering Students by Utilizing Integrated Online Modules and Experiential
Paper ID #33595It’s All About Engagement: Infusing the Mobile Studio ApproachThroughout the Electrical Engineering CurriculumDr. Steven S. Holland, Milwaukee School of Engineering Dr. Steven S. Holland is an Associate Professor in the Electrical Engineering and Computer Science Department at the Milwaukee School of Engineering (MSOE). He earned his BSEE degree from MSOE in 2006, and his MSECE and Ph.D. from the University of Massachusetts Amherst in 2008 and 2011, respectively. Prior to joining MSOE in 2013, he was a Senior Sensors engineer at the MITRE Corporation. He primarily teaches courses in analog electronics
, SRI International Carol Tate is an Education Researcher at SRI International’s Center for Education Research and Innova- tion. She leads the external evaluation for the Promoting Inclusivity in Computing (PINC) program at SFUSD.Dr. Jennifer Nelson, San Francisco State UniversityDr. Nina Narayan Hosmane, San Francisco State UniversityProf. Nicole Adelstein, San Francisco State UniversityDr. Pleuni S. Pennings, San Francisco State University Pleuni Pennings is an associate professor in Biology at San Francisco State University. She received her PhD from the University of Munich in Germany. Her interests are population genetics, drug resistance, computational biology and improving access to computer science skills.Mr
Texas at ArlingtonDr. Anne Nordberg,Prof. Wei Wayne LI, Texas Southern UniversityProf. Hanadi S. Rifai P.E., University of Houston American c Society for Engineering Education, 2021 Paper ID #31234An Exploratory Study of Intentionality Toward Diversity in STEM FacultyHiringMs. Samara Rose Boyle, Rice University Samara is an undergraduate studying neuroscience at Rice University in Houston, TX. She works as a research assistant for Dr. Yvette E. Pearson in the George R. Brown School of Engineering. Her primary research focus is the advancement of diversity, equity, and
, and identifying new ways to empirically understand how engineering students and educators learn. He currently serves as the Graduate Program Chair for the Engineering Education Systems and Design Ph.D. program. He is also the immediate past chair of the Research in Engineering Education Network (REEN) and an associate editor for the Journal of Engineering Education (JEE). Prior to joining ASU he was a graduate student research assistant at the Tufts’ Center for Engineering Education and Outreach.Dr. Jean S. Larson, Arizona State University Jean Larson, Ph.D., is the Educational Director for the NSF-funded Engineering Research Center for Bio- mediated and Bio-inspired Geotechnics (CBBG), and Assistant Research Professor
Paper ID #34752Engineers Without Borders at a Community College: Lessons LearnedCallie CharletonMiral Desai, California Polytechnic State University, San Luis ObispoMs. Carissa Elaine NoriegaCeleste Yi ming Soon RamseyerMs. Elise GoodingMichael S. ReynaDr. Lizabeth L. Thompson, California Polytechnic State University, San Luis Obispo Dr. Lizabeth Thompson is a professor in Industrial and Manufacturing Engineering. She has been at Cal Poly for nearly 30 years and has held various positions on campus including Co-Director of LAES, Director of Women’s Engineering Programs, and CENG Associate Dean. Her research is in Engineering
Paper ID #32200Lessons from Diverse Women in STEM: Acknowledging InstitutionalChallenges and Empowering Agency Towards STEM persistenceSophie Schuyler, University of Massachusetts BostonJonathan S Briseno Jonathan Brise˜no is a doctoral student of Counseling Psychology at the University of Massachusetts Boston. He is currently a Clinical Fellow at Massachusetts General Hospital/Harvard Medical School. He provides services to a diverse population in English, Spanish, and Brazilian Portuguese. His research and clinical interests include underserved and marginalized populations, LGBTQ+ and Latinx immigrants, with a focus on
from Virginia Tech.Dr. Jeremi S London, Virginia Tech Dr. Jeremi London is an Assistant Professor in the Engineering Education Department at Virginia Poly- technic Institute and State University. London is a mixed methods researcher with interests in research impact, cyberlearning, and instructional change in STEM Education. Prior to being a faculty member, London worked at the National Science Foundation, GE Healthcare, and Anheuser-Busch. She earned B.S. and M.S. degrees in Industrial Engineering, and a Ph.D. in Engineering Education from Purdue University.Dr. David B Knight, Virginia Polytechnic Institute and State University David B. Knight is an Associate Professor and Assistant Department Head of Graduate
explore a new non-comparison sort. The linear transform sort uses alinear transformation to generate new keys between 0 and n and uses those keys to sort the datausing a recursive bucket sort. The idea of using new computed keys is adapted from theSchwartzian Transform [4], where a sorting key is extracted from other extraneous data all atonce instead of repeatedly during runtime. This sort is a proof of concept that transformation can adapt the input of non-comparisonsorts to increase speed of sorting. We will outline the linear transform sort algorithm, inductivelyprove its functionality, outline best and worst cases, present test data, and propose improvementsfor further research.Algorithmlinear transform sort(S)Input: a list of
currently based on the saturated liquid properties at the given temperatures only.For example, it is a common practice to approximate specific volume, v(T, p), by saturatedliquid specific volume, vf(T), the specific internal energy, u(T, p), by saturated liquid specificinternal energy, uf(T), the specific entropy, s(T, p), by saturated liquid specific entropy, sf(T),and the specific enthalpy, h(T, p), by hf(T) + vf(T)[p-psat(T)]. Errors resulting from theseapproximations will be analyzed in this paper. This paper will show that these approximationsare not very accurate at all ranges of temperatures and pressures. The paper will establish limitson the range of pressures and temperatures that these approximations could be used withreasonable
. Figure 2: simplified suspension system modelThe force in a spring is its constant multiplied by its displacement and the force in a hydraulicsystem is the damping of the hydraulic system multiplied by velocity. Based on these facts, theequation of motion for the system of figure 2 is as shown in equation (1). m(d2xo/dt) + c(dxo/dt – dxi/dt) + k(xo – xi)= 0 (1)Rearranging equation (1) and applying the Laplace transform formulas put equation (1) in theform shown in equation (2). [2] (mS2 + cS + k) Xo(S) = (cS + k)Xi(S) (2)The transfer function of a control system is defined as the output of the system divided by theinput of the system in
University of Missouri System and earned a Faculty Achievement Award for teaching. American c Society for Engineering Education, 2021 Curriculum Element: Economic Analysis Group Project Utilizing VoiceThreadObjectives:The curriculum element discussed may be implemented in an undergraduate or graduate levelengineering economics course. With sufficient instructor and/or TA support, the project may beimplemented with any class size. The primary objective of this project is to provide students theopportunity to 1) evaluate project(s) using a systematic economic analysis technique, 2) supporttheir recommended alternative with data, and 3
througha simple measurement using a smartphone. The concept of using the accelerometer sensor in mobilephones for physics experiments has become a well-known option for STEM teachers [10]. Sincethese devices are readily available to most students and teachers, experiments can be set up at lowcost while generating interest and motivation for learning.The smartphone app called Phyphox™ is used to record the accelerometer readings and report theearth's acceleration of 9.81 m/s² while the phone is resting (which is what we call "Accelerationwith g"). In contrast, the physical acceleration is zero when the phone is resting (or moving at aconstant speed), so there is a virtual sensor that subtracts the constant acceleration (usually by takinginto
equations. The same control system can bestable or unstable depending on the input parameters into the system. Stability or lack of stabilityof a control system can theoretically be determined by solving the control system differentialequation(s). The differential equation(s) can be solved numerically. A numerical solution of adifferential equation produces numbers that can be plotted but not an expression. The differentialequations can also be solved by classical differential equation techniques. The classicaldifferential equation solution techniques can be supplemented by using Laplace Transform andusing the MATLAB software to expedite the Laplace Transform formulations. Damping level(s)in a vibrating system greatly influence the stability level
. Figure 3: A one dimensional deformed barSince the bar element is developed by using the same deflection technique as a spring,assemblage of a number of finite elements that are based on bar formulation is done by the sametechnique that is used for assembling a number of spring elements.Bar elements are used for modeling truss assemblies. In a truss, various truss elements can forman angle with the global coordinates as shown in figure 4. Figure 4: A bar element making an angle θ with X axis of global coordinate systemThe global stiffness matrix relating global forces to global displacements for the element shownin figure 5 is given in equation (13). [3] f1x C ∗ C C∗S
environment.The objective of the study is to answer the questions: (1) Which factors affect the systemperformance measures and to what extent? and (2) can optimal settings be identified for thesystem to perform consistently over the range of the extraneous noise variable? To do this,Taguchi experiments will be utilized, along with Signal to Noise (S/N) ratios and factorial plots,to analyze the results. The aim of this paper is to introduce the application of quality controlmethods in performance optimization for an automated electrohydraulic position control system.The system setup, hardware, software, and programming will be introduced. The researchdesign, measurements, and experimental runs will be demonstrated and explained. The impact onstudents
descriptors (Dominance D, Influencing I, Steadiness S and Compliance C)described in table A1 of the Appendix, are probably the most revealing as far as creating anarrative of the emerging typologies associated with the three clusters. Figure 3 shows theranking of the DISC parameters for the three clusters. Figure 3 – The mean DISC rankings for the three distinct clusters.The DISC ranking has associated word descriptors that further illustrate the associated behaviors.These word descriptors are given for the DISC variables and for the three clusters in Table 2. Table 2 – Word descriptors of the four DISC traits for the three clusters Dominance Influencing Steadiness Compliance
one sability to contribute to the level of their talent is an ethical and professional responsibility to thefield.This paper shares some early results from our broader NSF-funded project, titled Identif ingMarginalization and Allying Tendencies to Transform Engineering Relationships, or I-MATTER. The project s research questions are: 1. What does marginalization look like within engineering classrooms where teamwork is a primary feature? 2. How is marginalization legible (or not) to instructors at the classroom level? 3. What are the different ways that instructors respond to incidents of peer-to-peer marginalization? 4. How might the lessons of this work be implemented to systematically alert instructors when
students in pursuing their undergraduate studies.AcknowledgementsPartial support for this work was provided by the National Science Foundation Scholarships inScience, Technology, Engineering, and Mathematics (S STEM) program under Award No.2130428. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.ReferencesApriceno, M., Levy, S. R., & London, B. (2020). Mentorship during college transition predicts academic self-efficacy and sense of belonging among STEM students. Journal of College Student Development, 61(5), 643-648. https://doi.org/10.1353/csd.2020.0061Bagès, C., & Martinot, D. (2011
ideas Build your Revise your Share your Try out yourproblem(s) in solutions to the considering chosen solution to make work with solution the story problem materials solution it better others Digital Lesson Library for grades PK-5 Mat erials Lis Follows the entire problem-solving Variety o t
be taught? Can they be assessed?. Journal of Engineering Education, 94(1), 41-55.6. Flanagan, J. C. (1954). The critical incident technique. Psychological Bulletin, 51(4), 327-358.7. Khan, H. N. (2017). Scaling Moore's wall: Existing institutions and the end of a technology paradigm. Doctoral dissertation. Carnegie Mellon University.8. Benham, M., Foster, T., Gambell, T., & Karunakaran, S. (2020). The resilience imperative for medtech supply chains. McKinsey & Company. Available at: https://www.mckinsey.com/business-functions/operations/our- insights/the-resilience-imperative-for-medtech-supply-chains.9. Batur, D., Bekki, J. M., & Chen, X. (2018). Quantile regression metamodeling: Toward improved
error bars was conducted.For each set of data, the following was determined and plotted: 1) the average of the 12 averagemeasurements, 2) the average of the 12 maximum measurements, 3) the average of the 12minimum measurements, 4) the maximum of the 12 maximum measurements, 5) the minimum ofthe 12 minimum measurements, 6) and ± 2 standard deviations of the average (Fig. 10). 1s 5s 10 s 25 s 1 minute 12.5 12.5 12.5 12.5 12.5 12 12 12 12 12
workplaces, which can positively affect productivity,commitment, and performance [20].Theoretical FrameworkWithin engineering education, the role of values remains relatively underexplored (perhapsbecause engineering culture often positions itself as free of values or biases), but outside ofengineering education, examining these issues is not new. Researchers in social andorganizational psychology have examined values through numerous approaches and frameworks,e.g., [46]-[49]. For this study, we turn to Schwartz et al.’s values framework [50] [51], which weleverage due to its seminal and popular nature and proven utility in understanding how valuesinfluence behaviors and priorities in a range of domains (e.g., workplaces [51] [52]). WhileSchwartz et
from an understanding that engineers need systems thinking skills to address complexengineering problems, our research is aligned with best practices in curriculum and trainingmaterial development. Once a desired result is identified, in this case the goal is to developengineers who are able to use comprehensive systems thinking knowledge and skills to addresscomplex problems, the next step is to determine how the achievement of that goal will beassessed [8]. Such assessment(s) then guide the development of learning activities andexperiences, e.g., methods for teaching systems thinking [8]. Our analysis sought to understandthe ways in which existing systems thinking assessments relevant in an engineering contextattend to various dimensions
1.210 Using VR helped provide a better overview of the content. 134 3.51 1.237 Using VR helped to identify the critical concepts from topics in the lesson(s). 134 3.52 1.225An important aspect of the VR lesson design was usability including opportunities for interactionwith the lesson. All the 10-items of this dimension registered mean responses in the direction ofagreement with the items (Table IV). The responses indicated the user interface was userfriendly. The average of the responses was highest for the ability to review the lesson andunderstand the mistakes.Table IV: VR Lessons Usability (N = number of respondents, SD = standard deviation) Overall, I am satisfied with how easy it was to understand
, experiments, and physicalmeasurements. The following transfer function model is used to design and simulate the PIcontroller, Θ(s) 1 kb N = 2 (1) E in (s) s La (N 2 J m + J L )s2 + [La (N 2bm + bL ) + Ra (N 2 J m + J L )]s + Ra (N 2b m +bL ) + k b N 2 The model accounts for armature inductance and resistance, gear ratio, the motor inertia androtational damping. To more accurately model the dynamics of the motor, an alternative modelthat incorporate the stick-slip torque, Tf, and the motor saturation voltage, Vsat, was implementedin MATLAB
ethics and social responsibility and how these views are influenced byorganizational/institutional cultures. We anticipate that our findings will also benefit engineeringstakeholders in both academia and industry, namely by generating new insights about what typesof learning environments and experiences have the biggest impacts on how engineering studentsand professionals perceive and practice ethics, social responsibility, and related concerns.AcknowledgmentsThese materials are based in part upon work supported by the National Science Foundation underGrant Nos. 1449479, 2024301, and 2130924. Any opinions, findings, and conclusions orrecommendations expressed in these materials are those of the author(s) and do not necessarilyreflect the views