Project ELEVATEleadership team must incorporate training related to team science, including team formation.They must also develop transition and conflict management plans and clarify roles andresponsibilities for each committee and its members. 8. Acknowledgments This material is based upon work supported by the National Science Foundation underGrant No.#2149995, #2149798 #2149899 from the Division of Equity for Excellence in STEMin the Directorate for STEM Education. Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author(s) and do not necessarilyreflect the views of the National Science Foundation.[1] D. L. Gillian-Daniel, W. G. Troxel, and S. Bridgen, “Promoting an Equity Mindset
Physical Science (S) 2 (8.33%) Social Science (S) 3a (12.5%) Technology (T) 0 (0%) Engineering (E) 15 (62.5%) Mathematics (M) 4a (16.67%) Prior Research Experience No 12 (50%) Yes 12 (50%) a one student had dual majors of Mathematics and Social ScienceThere was a total of five themes that
discipline (i.e., civil, nuclear), 3) total Ph.D.'s granted between 2014 and 2017 forthe program, and 4) engineering college. Graduate programs were also sorted by size based onthe number of doctoral degrees granted: small (1-6 doctorates), medium (7-19 doctorates), orlarge (19-225 doctorates). To overcome limitations with existing datasets, the research teamconducted purposive sampling with minority-serving institutions and institutions whose highestdegree awarded is a master’s to capture the broad range of GPD experiences. Overall, 45Graduate Program Directors or Chairs were contacted over multiple rounds with a focus onensuring diverse participation across the criteria outlined previously. In the end, 9 elected toparticipate in the semi
student success," 2013.[5] D. Henriksen, M. Henderson, E. Creely, A. A. Carvalho, M. Cernochova, D. Dash, T. Davisand P. Mishra, "Creativity and risk-taking in teaching and learning settings: Insights from sixinternational narratives," International Journal of Educational Research Open, vol. 2, no. 2, pp.1-11, 2021.[6] N.R. Kuncel, S. Hezlett, and D. Ones, "Academic performance, career potential, creativity,and job performance: Can one construct predict them all?," J. Educ. Psychol., vol. 102, no. 3, pp.599-616, Aug. 2010.[7] P. C. Wankat, R. M. Felder, K. A. Smith and F. S. Oreovicz, "The scholarship of teaching andlearning in engineering," in Disciplinary Styles in the Scholarship of Teaching and Learning:Exploring Common Ground, vol. 1
acareer. The effectiveness study on the student-facing presentation is currently being repeated in aYear 3 (2023) study. Future work includes analyzing data between control and treatment groupsand across years. We also aim to grow the network of faculty who share these resources topromote more students to join the teaching profession and inspire young minds.AcknowledgementsThe authors thank Dr. Jared Breakall, the Mines chemistry course instructors, GFO presentationfacilitators, and research participants. This material is based upon work supported by theNational Science Foundation under DUE-Grant Nos. 1821710 & 1821462. Any opinions,findings, and conclusions or recommendations expressed in this material are those of theauthor(s) and do not
with multidisciplinary teams allowingstudents one-on-one interaction while working on real projects enables them to negotiate theirparticipation with peers, resulting in a deeper integration of the involved disciplines. Boundary objects play a critical role in how interdisciplinary collaboration occurs, and the coursemust offer and promote concrete boundary objects (e.g., software, procedures, knowledge) from eachdiscipline. Although some software may be predominantly used in the new CoP environment, instructorscan highlight alternative boundary objects that enable students to accomplish the tasks required in thecourse. ReferencesAlmeida, L. M. de S., Becker, K. H., & Villanueva, I
, “The equivalence of theorem proving and the interconnection problem,” SIGDA Newsl., vol. 5, p. 31–36, sep 1975. [6] E. Beyne, “The 3-d interconnect technology landscape,” IEEE Design & Test, vol. 33, no. 3, pp. 8–20, 2016. [7] D. Sylvester and K. Keutzer, “Rethinking deep-submicron circuit design,” Computer, vol. 32, pp. 25–33, 1999. [8] M. Zhu, J. Lee, and K. Choi, “An adaptive routing algorithm for 3d mesh noc with limited vertical bandwidth,” in 2012 IEEE/IFIP 20th International Conference on VLSI and System- on-Chip (VLSI-SoC), pp. 18–23, 2012. [9] S. Das and D. K. Das, “Steiner tree construction for graphene nanoribbon based circuits in presence of obstacles,” in 2018 International Symposium on Devices
Paper ID #41979Decolonizing Stakeholder Analysis for Engineered SystemsDr. Shamsnaz Virani Bhada, Worcester Polytechnic Institute Shamsnaz S. Virani, Assistant Professor of Systems Engineering at the Worcester Polytechnic Institute, earned her Ph.D.in industrial and systems engineering from the University of Alabama, Huntsville. She also holds a M.S. in human factors engineering from Wright State University, Dayton.Sarah E. Stanlick, Worcester Polytechnic Institute ©American Society for Engineering Education, 2024 Decolonizing stakeholder analysis for engineered systemsAbstractIn systems
. 2021, doi: 10.1016/j.ijdrr.2020.102011.[8] C. Kenny, “Why Do People Die in Earthquakes? The Costs, Benefits and Institutions of Disaster Risk Reduction in Developing Countries,” The Costs, Benefits and Institutions of Disaster Risk Reduction in Developing Countries (January 1, 2009). World Bank Policy Research Working Paper, no. 4823, pp. 1–42, 2009, doi: https://doi.org/10.1596/1813- 9450-4823.[9] E. Hausler, “Building earthquake-resistant houses in Haiti: The homeowner-driven model,” Innovations: Technology, Governance, Globalization, vol. 5, no. 4, pp. 91–115, 2010, doi: https://doi.org/ 10.1162/INOV_A_00047.[10] D. Félix, A. Feio, J. M. Branco, and J. S. Machado, “The role of spontaneous
. Bailey, and D. Fitch, ‘Evaluating Learning Outcomesand Assessing Social Work Skill Development: Comparing Online vs. In-Person Education,’ J.Technology in Human Services, vol. 40, no. 1, pp. 47-57 2022, doi:10.1080/15228835.2021.1997693.[9] M. Cooper, E. Cardenas-Vasquez and A. Frye, “Why Would Some Students Prefer toWatch Lecture Recordings Rather than Attend Class? A Follow-Up Study” oral presentation,AIChE National Meeting, Orlando, FL (2023).[10] M. Andrasik, S. Frey, and M. Meheret Endeshaw, ‘Qualitative Methods: Coding & DataAnalysis CFAR SPRC Qualitative Methods Workshop Series’.https://depts.washington.edu/cfar/sites/default/files/uploads/core-program/user70/QualitativeMethods Workshop_Coding_05-2014.pdf (accessed Feb. 23, 2023
expressedare those of the authors and do not necessarily reflect the views of the NSF.References[1] M. D. Koretsky et al., "For Systematic Development of Conceptests for Active Learning," in EDULEARN19 Proceedings, 2019: IATED, pp. 8882-8892.[2] B. P. Self et al., "Understanding Context: Propagation and Effectiveness of the Concept Warehouse in Mechanical Engineering at Five Diverse Institutions and Beyond–Results from Year 1," in 2020 ASEE Virtual Annual Conference, 2020.[3] M. D. Koretsky, S. B. Nolen, J. Galisky, H. Auby, and L. G. Grundy, "Progression from the Mean: Cultivating Instructors’ Unique Trajectories of Practice using Educational Technology," Journal of Engineering Education, no. 113, 2024, doi
withoutcompromising integrity or equity.References[1] J. Qadir, "Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education," 2023 IEEE Global Engineering Education Conference (EDUCON), Kuwait, Kuwait, 2023, pp. 1-9, doi: 10.1109/EDUCON54358.2023.10125121[2] P. P. Ray, “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet of Things and Cyber-Physical Systems, vol. 3, pp. 121–154, Jan. 2023, doi: 10.1016/j.iotcps.2023.04.003.[3] S. Nikolic et al., “ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial
, including an Engineering Economy course in thespring 2024 semester. These games will be studied to determine in which courses they have themost impact, how best to incorporate learning outcomes into the games, and which aspectsstudents find the most and least engaging. References[1] P. Backlund and M. Hendrix, “Educational Games – Are They Worth the Effort? A literature survey of the effectiveness of serious games,” in 2013 5th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES), Poole, UK, 11-13 September, 2013, pp. 1-8.[2] C. Sousa, S. Rye, M. Sousa, P. J. Torres, C. Perim, S. Mansuklal, and F. Ennami, “Playing at the school table: Systematic
control the car from video. When the RC car went over anRFID sensor/tag it disabled the controls for one second. This project was funded by NSF S-STEM Scholarship program at UVU.Sample Project 2: Snake Game: A Verilog ImplementationIn this project, a team of two computer engineering students worked together to design a SnakeGame video game on a FPGA (Field Programmable Gate Array) using Verilog language. Thegame was uploaded to a tinyFPGA-BX board. The users controlled the game using simple pushbuttons that are wired to the board. A Video Graphics Array (VGA) display was used so that thegame could be transported and played on modern displays. Figure 5: Top Level View [16]Specialized hardware was built to run this
recently and successfully completed thecourse(s) for which they are being recruited. They must have a minimum of a B average with aclear preference for an A average. Grades are not the only criteria. The research team asks theinstructors of the targeted engineering courses who they would recommend as potential peerleaders. The recruitment of the peer leaders is repeated approximately six to eight weeks prior tothe start of the term to allow for hiring, onboarding, and completing the peer leader trainingcourse.During Year 1 (AY22-23), eight peer leaders were trained. Three of those eight peer leaders(37.5%) were military students (i.e., a veteran or currently serving). These eight peer leadersserved in four engineering sections (one section in the
for a C/P faculty member typically looks like?” A: “Not really. They are all so different that there really is no typical.” Q: “What about unsuccessful cases? What gets talked about as the reason(s) someone might not get promoted?” A: “Of the last set, they all went through, and overall, the success rate is typically very high.”This might be where we lose some folks. How is any of that a bad thing? It isn’t, at least notwhen taken at face value. The problem is that it did little to illuminate any parts of the promotionprocess, that to us still seemed hidden, uncertain, and in some ways, inequitable. The “very highsuccess rate” should have been reassuring, but instead seemed to activate various
offers immersive experiences that can further enhancestudents' comprehension and retention of complex material science concepts.In summary, the development of animated visual aids represents a step towards addressing thevisualization challenges in materials science education. By leveraging emerging technologies andcontinuous assessment, we aim to foster a deeper understanding of dislocations and their role inmaterial behavior, ultimately enriching the learning experience for students.References[1] R. A. Streveler, T. A. Litzinger, R. L. Miller, and P. S. Steif, “Learning Conceptual Knowledge in the Engineering Sciences: Overview and Future Research Directions,” Journal of Engineering Education, vol. 97, no. 3, pp. 279–294, Jul
. Lee, and D. Pino, “First-Year Community College Students’ Perceptions of and Attitudes Toward Intrusive Academic Advising,” NACADA Journal, vol. 36, no. 1, pp. 30–42, Jun. 2016, doi: 10.12930/NACADA-15-012.[2] S. Kraft-Terry and C. Kau, “Direct Measure Assessment of Learning Outcome–Driven Proactive Advising for Academically At-Risk Students,” NACADA Journal, vol. 39, no. 1, pp. 60–76, Jul. 2019, doi: 10.12930/NACADA-18-005.[3] J. A. Kitchen, D. Cole, G. Rivera, and R. Hallett, “The Impact of a College Transition Program Proactive Advising Intervention on Self-Efficacy,” J. Stud. Aff. Res. Pract., vol. 58, no. 1, pp. 29–43, Jan. 2021, doi: 10.1080/19496591.2020.1717963.[4] R. D. Robnett, M. M
. 5References[1] S. D’Agostino, “Why faculty members are polarized on AI,” Inside Higher Ed, Sept. 13,2023. [Online]. Available:https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2023/09/13/why-faculty-members-are-polarized-ai[2] E. Surovell, "Faculty Members Still Aren't Sure What to Make of ChatGPT" The Chronicleof Higher Education, Mar. 16, 2023. [Online]. Available:https://www.chronicle.com/article/faculty-members-still-arent-sure-what-to-make-of-chatgpt?sra=true&cid=gen_sign_in[3] J. Moody, "The ChatGPT Commencement Address," Inside Higher Ed, June 29, 2023.Available:https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2023/06/29/college-administrators-embrace-chatgpt[4] K. Huang, “Alarmed by A.I
team has developed exercises for theintroductory Statics course that serves as most students’ first introduction to engineeringproblem solving.Currently, the U.S. engineering workforce remains 90% white and male; engineering, inparticular, has not attracted women and URMs. Baccalaureate degrees received by bothURMs and women in engineering peaked in 1999-2000 and have trended downwardsince then[1] A study conducted by Engineers Dedicated to a Better Tomorrow used theNSF WebCASPAR database to document that although about one half of earnedbaccalaureate degrees in S&E as a whole go to women, in physics, engineering,engineering technology, and computer science, these rates dropped to one in five[2].While in 2008 women earned 18.5% of
statistical concepts which are frequentlymisunderstood by students at this level16.It should certainly be pointed out to students that this approach provides a very conservativenumber because it assumes worst case addition of inaccuracies and that more sophisticatedtechniques will be introduced later. If students are familiar with basic statistical techniques wecan differentiate between random and systematic errors and show that random errors can bereduced by averaging the results of repeated measurement. In this case, for random errors, therange can be replaced with 2s / n , where s is the experimental standard deviation and n is thenumber of samples averaged. This gives a 95% coverage interval for normally distributed dataand, by Chebyshev’s
to the current phase “Expansion Development” (NSF DUE-1022750).References1. Acharya R, Wasserman R, Stevens J, and Hinojosa C: Biomedical imaging modalities: a tutorial. Computerized Medical Imaging and Graphics 19:3-25, 1995.2. Allan GL, and Zylinski J: The teaching of computer programming and digital image processing in radiography. Intl. Journal of Medical Informatics 1998; 50:139-143.3. Alon P: Bringing the Internet and multimedia revolution to the classroom. Campus-Wide Information System 17:16-22, 2000.4. Athanasiou S, Kouvaras I, Poulakis I, Kokorogiannis A, Tsanakas P, and Koziris N: TALOS: An interactive
weeks in introductory soils courses. The high-techflavor of x-ray CT can be attractive to these students. Anecdotal comments from students usingthis approach have been positive and encouraging; however, the newness of this approachprecludes the presentation of statistical assessments in this paper. A more quantitativeassessment of student learning will be assembled in future semesters based on additional studentfeedback.AcknowledgementsExperimental x-ray CT analyses conducted by former students Brent Nielsen, Josh Nichols, andBryant Robbins were useful in developing the simplified approach described in this paper. Theirvaluable contributions are acknowledged and greatly appreciated by the authors.References Cited1. Alshibli, K. A., Batiste, S
retail price of this new OMAP- Page 22.1118.2based system is $149 (USD),2 while the suggested retail price for the still-available C6713 DSK is$395 (USD). When compared to the TMS320C6713 DSK, this new experimenter kit has several Figure 1: The new LogicPD Zoom OMAP-L138 eXperimenter Kit.changes, and depending upon the intended application these changes may or may not be consid-ered improvements. The OMAP-L138 SoC includes a multi-core processor that contains both aC6748 VLIW digital signal processor and an ARM926EJ-S RISC general purpose processor, bothrunning at 300 MHz. In the experimenter kit configuration, the processor has 64
and frequently with little interaction. This paperdiscusses the potential of BIM for improving collaborative AEC education, and proposes a wayforward for Universities, based on the outcomes of a series of surveys and interviews with arange of industry and academic stakeholders in the AEC professions, examining current andfuture practice in this important area.The need for collaboration in the AEC professionsIn the U.S., approximately eight per cent of the total workforce in 2007 was employed inconstruction and the industry contributed $611 billion, or 4.4 per cent of the gross domesticproduct (GDP) in that year1. Similarly, the construction industry represents approximately six percent of both Australia‟s and the UK‟s GDP2, 3. But despite the
history of engineering distance learningat the University of Florida, and a one year snapshot of enrollments and students. The core ofthe work goes through UF EDGE basic model used to optimize resources and time including: thecombined distance and campus classroom structure, infrastructure for online delivery, coursemanagement system and online optimization tools, curriculum for online delivery, and thedistance exam proctoring process.1. Introduction: UF EDGE History, Departments, and Students.The University of Florida began offering on-site distance learning instruction at select Floridacompanies in the 1950’s. In 1964, the UF College of Engineering launched the first livegraduate engineering courses broadcast from UF with real time two-way
course time restrictions and itwas based on puzzle questions that may not accurately identify critical thought.DiscussionThis paper focuses on the beginning portion of the study involving three cohorts and their fouracademic years at the University of Louisville. The freshman data on the CA (critical thinkingassignment) and the IFR (independent faculty rating) of the CA is being used to create thebaseline for comparison as each of thecohorts’ progress through their academic careers at J.B.Speed School of Engineering. The second year data have been collected for two cohorts, butcohort 2 has not been analyzed yet. The IFR for cohort 2’s second year will be completed in2011.Table 4 shows the freshman data for each cohort. Since the pre/post CTA was
. Dodds, A. Howard, S. Tejada, and J. Weinberg, pp. 35-41. Technical Report SS-04-01. Menlo Park, CA: AAAI Press, (2004). 2. S. Coradeschi and J. Malec “How to make a challenging AI course enjoyable using the RoboCup soccer simulation system, in RoboCup-98: Robot soccer world cup II: Lecture notes in artificial intelligence, vol. 1604, pp.120-124, ed. M. Asada and H. Kitano. Berlin: Springer, (1999). 3. M. Goldweber, et al. “The use of robots in the undergraduate curriculum: Experience reports,” Panel at 32nd SIGCSE Technical Symposium on Computer Science Education, Charlotte, North Carolina.. 4. F. Klassner, “Robotics as a Unifying Theme for Computing Curriculum 2001”, National Science Foundation
education and occupational codes.2. BackgroundIn this paper, definitions for STEM fall into one of two domains: education or occupation. Thespecific discipline categories used in the education domain are derived from the National Centerfor Education Statistics Classification of Instructional Programs 20008 and the Classification ofInstructional Programs 19909. The standard Occupational Classification (SOC) system is used inthe occupational domain.CIP and CIP CodesThe National Center for Education Statistics (NCES), of the U. S. Department of Education,developed the Classification of Instructional Programs (CIP). CIP includes all the disciplinesoffered in academic institutions and universities in the United States. For each discipline, there isa
.pdf [Accessed Dec 08, 2010]3. Chakraborty, S., Sharma, S. and Ray, S. (2007), Samsung Electronics (A&B): In India, , Page 22.1226.7 HBR Case Study, 906M34-PDF-ENG and 906M35-PDF-ENG4. Huang, M., Riggs, B.K., Lynn, B.C., Dongsheng, W. and Gaffney, P. (2006), Eliminate the Middleman? , HBR Case Study R0603X-PDF-ENG5. Kane-Sellers, L., Koerber-Walker, J. and Zoghi, B. (2004), Connecting Resources: A Primer for Electronics Distribution, Thomson Custom Publishing.6. Kaufman, S.P. (2007) Arrow Electronics-The Apollo Acquistion, HBR Case Study 607007- PDF-ENG7. Miller, M., Moran, A., Richardson, B., Waguespack, T., Carter, R. and