(IN-MaC).References[1] A. Murgai, “Transforming digital marketing with artificial intelligence,” Int. J. Latest Technol. Eng. Manag. Appl. Sci., vol. 7, no. 4, pp. 259–262, 2018.[2] S. Lund et al., “The future of work in America. People and places today and tomorrow.,” McKinsey Global Institute, 2019. [Online]. Available: http://dln.jaipuria.ac.in:8080/jspui/bitstream/123456789/11008/1/The-Future-of-Work-in- America-Full-Report.pdf[3] M. Calzavara, D. Battini, D. Bogataj, F. Sgarbossa, and I. Zennaro, “Ageing workforce management in manufacturing systems: state of the art and future research agenda,” Int. J. Prod. Res., vol. 58, no. 3, pp. 729–747, 2020, doi: 10.1080/00207543.2019.1600759.[4] K. Song, G. Kim, H. Yun, B.-K
. Hannah Lynn, C., D.D. John, and P. Breanne, The DMVP (Detect, Measure, Valuate, Propose) Method for Evaluating Identified Needs During a Clinical and Technology Transfer Immersion Program. ASEE Conferences: Salt Lake City, Utah.4. William, H.G., et al., Work in Progress: A Clinical Immersion Program for Broad Curricular Impact. ASEE Conferences: Tampa, Florida.5. Jennifer, K., et al., Clinical Immersion and Team-Based Design: Into a Third Year. ASEE Conferences: Columbus, Ohio.6. Miiri, K., Clinical Immersion Internship Introduces Students to Needs Assessment. ASEE Conferences: New Orleans, Louisiana.7. Sood, S., Short, M., Hirsh, R. , Kadlowec, J. , Merrill T.L. , , Biodesign Through Cinical
for other instructors to adopt in a futureso that this method can have as big as in impact as possible.Bibliography[1] W. Kersting and R. Kerestes, Distribution System Modeling and Analysis with MATLAB® and WindMil®, 5th ed. CRC Press, 2022.[2] J. D. Glover, M. S. Sarma, and T. Overbye, Power system analysis & design, SI version. Cengage Learning, 2012.[3] PowerWorld, “PowerWorld Simulator,” Apr. 17, 2023.[4] F. Milano, “Experience of Unix Terminal-based Labs for Undergraduate Modules on Power System Analysis,” in EDULEARN14 Proceedings, IATED, 2014, pp. 268–277.[5] G. M. Jónsdóttir and F. Milano, “Jupyter notebooks for computer-based laboratories on electrical energy systems,” in EDULEARN19
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not a scientific study. But theanecdotal data has been encouraging enough in the author’s mind, to further investigate thiseducational approach. What is most intriguing to the author is the self-customizing ability theapproach seems to have on student learning styles. There may be much gold to mine in thisendeavor. References 1. Finelli, C.J., Killinger A. and Budney, D.D. (2001). “Strategies for Improving Classroom Environment,” Journal of Engineering Education, vol. 90, no. 4, October, pp. 491-498. 2. Palmer, S. (2000). “Management Education in Australian Engineering Undergraduate Courses,” Engineering Management Journal, vol. 12, no. 3, September, pp. 3-10. 3. Sullivan, J.F
Education Technologyand Computers, October 2020, pp. 161-164, https://doi.org/10.1145/3436756.3437039[7] Sanchez, B., Rodriguez-Paz, M. X., ”Using BIM as a collaborative platform to improve e-learning in civil engineering”, 2020 ICETC ‘20: Proceedings of the 12th InternationalConference on Education Technology and Computers, October 2020, pp. 21-26,https://doi.org/10.1145/3436756.3437015[8] Sanchez, B., Ballinas-Gonzalez, R., Rodriguez-Paz, M. X., Nolazco-Flores, J.A. (2020,June),”Usage of building information modeling for sustainable development education”,Paper presented at 2020 ASEE Virtual Annual Conference Content Access,https://https://peer.asee.org/usage-of-building-information-modeling-for-sustainable-development-education[9] Crespo, S
: Deep Learning with Python by Francois Chollet Robotics: Modelling, Planning and Control by Bruno Siciliano Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto IV. Computing and Simulation PlatformWe have used Keras and Gazebo as computing and simulation platform. Keras is an open-sourcesoftware library that provides a Python interface for artificial neural networks. Gazebo is anopen-source software platform for which anyone can develop a plug-in with model components V. Learning Outcomes and AssessmentThe learning outcomes of the course EGR 391 are as the following: 1. Understand the meaning, purpose
environment of Industry 4.0.References[1] Grieves M. W., “Digital Twin: Manufacturing excellence through virtual factoryreplication.” White paper (2015)https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication[2] Grieves, M., “Virtually intelligent product systems: Digital and physical twins,” inComplex Systems Engineering: Theory and Practice, S. Flumerfelt, et al., Editors. 2019,American Institute of Aeronautics and Astronautics. p. 175-200.[3] Grives M., Vickers J. “Digital Twin: Mitigating, unpredictable, undesirable emergentbehavior in complex systems.” in Transdiciplinary Perspectives on Complex Systems, KhlenF. J. et al., Editors. 2017, Springer International Publishing.https
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Education Journal, 2(3), 2011, 106-112.11. N. Provotas and K. Elder, Phase-Field Methods in Materials Science and Engineering, John Wiley & Sons, 2011.12. S.B. Biner, Programming Phase-Field Modeling, Springer, 2017.13. M. Foss and Y.-C. Liu, “Creating solutions through project-based and experimental learning: A case study of the Concept Center”, International Journal of Engineering Education, 37(6), 2021, 1630-1642.14. M. Foss, Y.-C. Liu, and S. Yarahmadian, “Project-based learning in a virtual setting: A case study on materials and manufacturing process and applied statistics’, International Journal of Engineering Education, 39(5A), 2022, 1377-1388.15. M. Foss and Y.-C. Liu, “Promoting sustainable development goals through
by its inclusion in the 2017-2018 biennial report of the NSF’s Committee on Equal Opportunities in Science and Engineering(13) presented to the U.S. Congress, which states that: “[s]upported by NSF’s Division ofEngineering Education and Centers, this REU site is directed by NSF Faculty Early CareerDevelopment Program awardee […] to provide undergraduate students with Attention DeficitHyperactivity Disorder (ADHD) with interdisciplinary research experience in the cyber andphysical security aspects of critical infrastructure resilience. […] This effort is changing theeducation paradigm from deficit–based to strength–based and make engineering education moreinclusive of diverse learning and thinking styles.”The impact of the program on the
. Dorneich, W. Rogers, S. D. Whitlow, and R. DeMers, “Human performance risks andbenefits of adaptive systems on the flight deck,” International Journal of Aviation Psychology,vol. 26, pp. 15-35, 2016[2] B. Seijo-Pardo, I. Porto-Díaz, V. Bolón-Canedo, and A. Alonso-Betanzos, “Ensemble featureselection: Homogeneous and heterogeneous approaches,” Knowledge-Based Systems, vol. 118,pp. 124–139, 2017[3] X. Li, J. Zhang, and J. Han, “Trajectory planning of load transportation with multi-quadrotors based on reinforcement learning algorithm,” Aerospace Science and Technology, vol.116, 2021[4] L. Zhang, B. Whiteley, D. K. Sharma, I. Dabipi, W. L. Brown, W. Zhu, and X. Tan, “On theexploration of game-based learning in STEM education with the development and
://engineeringunleashed.com. [Accessed 23 February 2023].[2] J. B. Hylton, D. Mikesell, J-D. Yoder and H. Leblanc, "Working to Instill the Entrepreneurial Mindset Across the Curriculum," Entrepreneruship Educ. and Pedagogy, vol. 3, no. 1, pp. 86-106, 2019.[3] C. Kim, R. A. Cheville, E. & Jablonski, M. J. Prince, K. E. K. Nottis, N. P. & Siegel, M. A. Vigeant and J. Tranquillo, "Instilling an Entrepreneurial Mindset through IDEAS Studio Courses," in Proceedings of the ASEE Annual Conference & Exposition, New Orleans, LA, June 2016.[4] S. Ardakanai, "Implementing Entrepreneurial Mindset Learning (EML) in a Timber Design Course," in Paper presented at 2020 ASEE Virtual Annual Conference, Virtual On line, 2020.[5] C. Wang, "Teaching
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, R1institution. All three sections had different instructors and a class size of around 50 students. One of thethree sections implemented a technology that allowed students to connect to a teammate’s computer andshare control of keyboard/mouse, enabling real-time collaborative programming in a normallyindividual programming environment. The other two sections were control sections with no specificimplementation differences. A survey instrument grounded in Burch et al.’s conceptual framework wasdeveloped and distributed at strategic times to measure students’ engagement with their team and in-class programming tasks. Results presented include a Confirmatory Factor Analysis (CFA) that supportsthe factor structure of the student engagement survey and an
and clarify the required functions. In order to do so, functions must be identified, classified, and organized. Then, resources are allocated to functions and functions are prioritized for value improvement. 4. Creativity: Generate ideas on all the possible ways to accomplish the required functions. All ideas associated with each function are recorded for further review. Brainstorming techniques are utilized at this stage. 5. Evaluation: The large number of ideas generated in the previous stage are synthesized. Those have promise are selected for development into specific value improvements. Each feasible idea/improvement is discussed with the team. 6. Development: Here, ‘best’ alternative(s) for
coupled with online classes) or completely virtual campus. Similarly, student supportprograms had to adjust quickly.In academic and professional environments, mentors often take the role(s) of advocates, counselors,advisors, and/or friends 6,4 . Ensher et al. 2 enumerated three major support avenues provided bymentors: (1) vocational or instrumental, (2) psychological, and (3) role models. Vocational orinstrumental support is provided when a mentee’s career is enhanced by opportunities such assponsorship, visibility, or providing challenging tasks through the mentoring relationship 2 .Psychological support could be provided through a mentor-mentee friendship, counseling, ormentor’s encouragement 2 . Finally, mentors could also serve as role
. JRC120911). Adopted at the 3617th European Union Councilmeeting. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CONSIL:ST_9009_2018_INIT&from=ENJackson, D. (2014). Factors influencing job attainment in recent Bachelor graduates:Evidence from Australia. Higher Education, 68(1), 135–153.https://doi.org/10.1007/s10734-013-9696-7Kolmos, A., & Holgaard, J. E. (2019). Employability in engineering education: Areengineering students ready for work? In S. H. Christensen, B. Delahousse, C. Didier, M.Meganck, & M. Murphy (Eds.), The Engineering-Business Nexus (Vol. 32, pp. 499–520).Springer International Publishing. https://doi.org/10.1007/978-3-319-99636-3_22Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate peripheral
• Should you use =, is, or == to calculate the answer? • This predicate is only meant to be used in situations where you know the first two pa- rameters. Do not call it with variables as either of the first two parameters in the call. • You will likely need to write more than one rule named add. • You may need to test the type of one of the parameters to know whether to use a rule with + or a rule with string concat. • Do not worry about calls to add like add(3, 3.14) or add(3, “hi”) for this assignment. Autograder test cases test case fail response(s) add(3, 4, 7) Doh! Your results were: results. add(3.2, 4.6, 7.8) Doh! Your results were: results
Paper ID #38510First-year Engineering Students’ Sense of Belonging: Impact of COVID-19and Efficacy as a Predictor of GraduationBreanna Graven, University of Louisville PhD candidate in curriculum and instruction with focus on informal science education. Graduate research assistant for the First-Year Engineering department studying ways to improve student retention.Dr. Patricia A. Ralston, University of Louisville Dr. Patricia A. S. Ralston is Professor and Chair of the Department of Engineering Fundamentals at the University of Louisville. She received her B.S., MEng, and PhD degrees in chemical engineering from the
, Russian Academy of Sciences, Chernogolovka, Russia, and later at the Department of Semiconductor Physics, University of Ulm, Germany. Currently, he is Associate Professor in the Electrical and Computer Engineering Department of Virginia Commonwealth Univer- sity. His research interests encompass growth, characterization and device applications of semiconductor, nanophotonic, and plasmonic materials. He is a member of the American Physical Society, the Materials Research Society, and IEEE. ©American Society for Engineering Education, 2023 New Course Development for Internet of ThingsAbstractIoT is a fast growing technology sector that is estimated to be valued at 100’s of billions
. “Learning by Doing” is a common training process which is a well-known theory ofeducation popularized by philosopher John Dewy in the 1930’s [5].Conclusions and RecommendationsIt is important to understand that these projects are missions that for engineer leaders are vehiclesfor training in preparation for any possible deployment. Engineer leaders and craftsmen on theground focus on skills critical to successful project planning and management. LearningObjectives 8 through 12 are consistently a focus during training events. Is the military missing anopportunity to better incorporate sustainable design (LO 13), data collection long term impactanalysis (LO 6), and stakeholder history and cultural education to develop military engineers intoglobal
design, test fixtures, and dataanalysis. The existing AM standards are covering only a few of these aspects. Students will havean opportunity to develop new SOP and standards. Upon completion of this module, studentsshould be able to (a) develop SOP of specimen and fixture design and data analysis, if there is nospecific standard available; (b) identify ASTM and/or ISO standard(s) which can be referencedto the developed procedure; (c) document the developed SOP and prepare for further evaluationand improvement for evolving it to be a standard. The pilot course is MFGE 5334/5334GAdditive Manufacturing of Lightweight Structures. It will be then introduced to other graduatelevel AM courses.The new graduate-level course:A new graduate level course
still exist betweenmale and female students, especially in STEM. Given these findings, we intend to extend thisstudy by investigating why LCDLMs fostered similar forms of engagement for males andfemales. LCDLMs are inexpensive miniature prototypes of industrial equipment; participantsinteract with the LCDLMs without fear of damaging the equipment. Researchers [17]hypothesize that men are more likely to take risks than women. Although preliminary, we predictthat the female participants in our study were as comfortable using the LCDLMs as the maleparticipants because the modules are inexpensive. Future studies will carefully examine thishypothesis.References[1] E. S. Pietri et al., “Addressing Unintended Consequences of Gender Diversity
. Kazemersky and D. Alp, "An alternate learning approach for the engineering management program at the University of Tennessee at Chattanooga," in 2000 Annual Conference Proceedings, 2000.[4] S. Motavalli, H. Zong, F. Ganjeizadeh and D. Bowen, "Development of a new masters degree program in engineering management," in 2005 Annual Conference Proceedings, 2005.[5] R. Dusseau, "History of the online master of engineering management program at Rowan University," in 2014 ASEE Annual Conference & Exposition Proceedings, 2014.[6] N. Cherbaka and J. Lavelle, "Proposing an Engineering Management program at North Carolina State University," in ASEE Annual Conference and Exposition, 2009.[7] L. Stauffer, "A model for the graduation