vs. private institution [8]. We are able to investigate the demographics of our surveyparticipants to find out whether this is actually the case.References[1] D. A. Smalls and R. McCord, “Wanna take a survey? Exploring tools to increase undergraduate student response rates to real-time experience surveys,” in Proceedings of the 2014 American Society for Engineering Education Annual Conference, Indianapolis, IN, 2014.[2] E. Isaacs, A. Konrad, A. Walendowski, T. Lennig, V. Hollis, and S. Whittaker, “Echoes from the past: how technology mediated reflection improves well-being,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris France: ACM, Apr. 2013, pp. 1071– 1080. doi: 10.1145/2470654.2466137
, doi: 10.1007/BF02300500[7] C. Wood, “The development of creative problem solving in chemistry,” Chem. Educ. Res.Pract., vol. 7, no. 2, pp. 96–113, Apr. 2006, doi: 10.1039/B6RP90003H[8] D. Jonassen, J. Strobel, and C. B. Lee, “Everyday problem solving in engineering: Lessonsfor engineering educators,” J. Eng. Educ., vol. 95, no. 2, pp. 139–151, Jan. 2013, doi:10.1002/j.2168-9830.2006.tb00885.x[9] S. R. Bartholomew, and G. J. Strimel, “Factors influencing student success on open-endeddesign problems,” Int. J. Technol. Des. Educ., vol. 28, pp. 753–770, Jun. 2018, doi:10.1007/s10798-017-9415-2[10] R. M. Ryan and E. L. Deci, “Self-Determination Theory,” in Encyclopedia of Quality of Lifeand Well-Being Research, F. Maggino, Ed.. Cham: Springer
Paper ID #40603WIP: Varying the Design Experience in First-Year EngineeringDr. Kathleen A Harper, Case Western Reserve University Kathleen A. Harper is an associate professor and the assistant director of the Roger E. Susi First-year Engineering Experience at Case Western Reserve University. She received her M. S. in physics and B. S. in electrical engineering and applied physics from CWRU and her Ph. D. in physics, specializing in physics education research, from The Ohio State University. 14th Annual First-Year Engineering Experience (FYEE) Conference: University of Tennessee in Knoxville
, ahigh Particle Density increases the synthetic image generator runtime drastically. To resolve thisissue, we constructed clean() functions in our Form objects to set a boundary condition for allour variables to within general limits of PIV data collection and analysis (e.g., region sizes mustbe between 8 and 128 pixels). The Django form and field validation evaluates users’ inputs andinserts these inputs into the synthetic image generator to produce a list of graphs and values.The synthetic image generator creates a Python class object from a utils.py file that contains alist of resulting graphs (the correlation plane and images) and PIV analysis values (r, s, signal tonoise ratio, and error values). A Django Model (a single, definitive source
felt comfortable engaging with. But I think that {University Affiliate 4}'s a very ... She has the principles of kind of community development and knowing that it has to sort of stem from within them. (University Affiliate 6) Another partner identified how important it was to have genuine relationships developedwithin the context of the partnership and how having everyone on the same page helps withdecision making and trust among partners: And I think that it speaks to {University Affiliate 4} but it also just speaks to that we've developed genuine relationships with some of the teachers over time. And how it's amazing to me how important that is. (University Affiliate 3) In the context of this
Computational Model. This dependence ontheir code for Graphical/Virtual Model was incorporated because many students discussedcomputer programs along with CAD programs. Also, students’ responses that included “simulat”for simulation/s, simulating, simulate/d, etc. were coded as Computational Models. Students’responses that included both input and output were coded as Computational Models. Along witha core feature of simulations, the keyword “interactive” was also considered, but this alone wasnot coded as Computational Models because it led to too many false positives in the coding. Thelast keyword that led to a response being coded as a Computational Model was computational.Financial/Business ModelsIn the Fall 2019 and Spring 2020 analyzed data, there
Interest in Scientific Communication 5 Strongly Agree, Agree, Disagree, Strongly Disagree Expectations from experience 7 Very Likely, Likely, Unlikely, Very Unlikely Future career/education plans 11 Yes, No Reason(s) for participation 2 1-7 (Highly Dissatisfied to Highly Satisfied) Overall SatisfactionAnalysisOf the original 9 participants, 1 student completed only the pre- and mid-project surveys,disengaging from the project in the middle of the Spring term. Two (2) students completed thepre-, mid-, and Year-1 surveys and six (6) completed all 4 iterations of the survey. Graphs weregenerated to
community and the economy”.In the context of this research, the success of a person’s employment outcome is dependent onabout 5 factors: 1. Whether they were able to gain computing employment: Getting employed in a computing job is seen as a successful employment outcome whereas getting employed in a non-computing position is seen as an unsuccessful outcome, in the context of this research13-14. 2. How soon they were able to gain their computing employment: After college, it takes the average graduate three to six months to get a job15. Within this research, a person who gets employed within a year of the completion of their educational program(s) is seen as successful whereas a person who gains their computing
engineering isnot important or that they are not intelligent enough, a better understanding of engineering’simportance and increased confidence in one’s engineering skills and knowledge couldeventually encourage students to consider engineering as a potential career path.Additionally, integrating engineering education into the K-12 curriculum would allow for amore seamless transition between studying engineering at the K-12 level and at theundergraduate level. Furthermore, Yeter et al.’s study (2022) reveals that engineering indicesare already present across the pre-college physics curriculum in Singapore. The study alsoidentifies entry points to introduce engineering education into the curriculum and integrateengineering practices into existing
meta-analysis of training studies,” Psychol. Bull., vol. 139, no. 2, pp. 352–402, 2013, doi: 10.1037/a0028446.[7] C. Dawson, “Tackling limited spatial ability: Lowering one barrier into STEM?,” Eur. J. Sci. Math. Educ., vol. 7, no. 1, pp. 14–31, Jan. 2019, doi: 10.30935/scimath/9531.[8] S. Hsi, M. C. Linn, and J. E. Bell, “The role of spatial reasoning in engineering and the design of spatial instruction,” J. Eng. Educ., vol. 86, no. 2, pp. 151–158, Apr. 1997, doi: 10.1002/j.2168-9830.1997.tb00278.x.[9] J. Wai, D. Lubinski, and C. P. Benbow, “Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance,” J. Educ. Psychol., vol. 101, no. 4, pp. 817–835, Nov. 2009, doi
will be informed and developed with anincreased student sample size.References[1] Lee, W.C. and Matusovich, H.M. (2016), A Model of Co-Curricular Support for Undergraduate Engineering Students. J. Eng. Educ., 105: 406- 430. https://doi.org/10.1002/jee.20123[2] Drane, D., Smith, H.D., Light, G., Pinto L., Swarat, S., 2005. The Gateway Science Workshop Program: Enhancing Student Performance and Retention in the Sciences Through Peer-Facilitated Discussion. J Sci Educ Technol 14, 337–352. https://doi.org/10.1007/s10956-005-7199-8.[3] X. Lin and L. Gao, “Students’ sense of community and perspectives of taking synchronous and asynchronous online courses”, AsianJDE, vol. 15, no. 1, pp. 169-179, Jun. 2020.[4
arithmetic operations in analytically-defined functions as operations on functions wouldseem to prepare them for a deeper understanding of this aspect of the calculus. At the same time,a conception of operations in expressions as operating on numbers and not on functions wouldseem to be an obstacle to understanding the derivative and integral as linear operators. These areempirically testable hypothesis; I would welcome research on them...In this paper the obstacles faced by the participants while solving two Riemann integral relatedquestion calculations are described and analyzed by using the schema development idea in [2].By relying on Piaget`s study of functions in 1977 [4], the Action-Process-Object idea inmathematics education for the
Institute.References[1] National Society of Professional Engineers, “Code of Ethics for Engineers,” no. July. 2019.[2] N. J. Moore, “Analysis of Student Perception in Thermodynamics,” 2020.[3] D. Dunning, The dunning-kruger effect. On being ignorant of one’s own ignorance, 1st ed., vol. 44. Elsevier Inc., 2011.[4] S. Pazicni and C. F. Bauer, “Characterizing illusions of competence in introductory chemistry students,” Chem. Educ. Res. Pract., vol. 15, no. 1, pp. 24–34, 2014.[5] D. Caputo and D. Dunning, “What you don’t know: The role played by errors of omission in imperfect self-assessments,” J. Exp. Soc. Psychol., vol. 41, no. 5, pp. 488–505, 2005.[6] S. Pavel, M. Robertson, and B. Harrison, “The Dunning
our department, Dr. Hanna Song, the former Senior Director for Inclusion &Diversity at Caltech, for her invaluable guidance in developing the program and for providingfacilitator training, Miles Chan, Narravula (Harsha) Reddy, & Peter Renn, graduate students inAerospace Engineering, for facilitating discussions, and all our wonderful speakers forcontributing their time, energy, and wisdom to the course.References[1] S. Chen, “Researchers around the world prepare to #ShutDownSTEM and ‘Strike For Black Lives’.” https://www.science.org/content/article/researchers-around-world-prepare-shutdownstem-and-strike -black-lives (accessed Feb. 03, 2022).[2] “#ShutDownAcademia #ShutDownSTEM,” #ShutDownAcademia #ShutDownSTEM
–359, 2012. [6] N. Shin, D. H. Jonassen, and S. McGee, “Predictors of well-structured and ill-structured problem solving in an astronomy simulation,” Journal of Research in Science Teaching, vol. 40, no. 1, pp. 6–33, 2003. [7] H. J. Passow, “Which ABET competencies do engineering graduates find most important in their work?” Journal of Engineering Education, vol. 101, no. 1, pp. 95–118, 2012. [8] Q. Symonds, “The global skills gap in the 21st century,” 2018. [Online]. Available: https://www.qs.com/portfolio-items/the-global-skills-gap-in-the-21st-century/ [9] C. Grant and B. Dickson, “Personal skills in chemical engineering graduates: the development of skills within degree programmes to meet the needs of employers,” Education
more interested in lean skills, while in the MEP and structural tracks, there is moreinterest in skills such as design management. Figure 3b also shows that more cutting-edge technology, suchas AR/VR technology, is much smaller and not important for some disciplines (i.e., structural). Comparison of Hired Disciplines Comparison of Desired skills Fig: 3a Fig: 3bFigure 3: Surveyed Responses of hired disciplines and their desired skills group by three discipline tracks. The next part of Survey 2’s data collected looked at characteristics that may affect success and howthe AE curriculum creates competencies. As Figure 4 shows, it is
Education: Innovations and Research, vol. 22, no. 4. Oct. 2021.[7] K.P. Goodpaster, O.A. Adedokun, and G.C. Weaver, “Teachers’ perceptions of rural STEM teaching: Implications for rural teacher retention,” The Rural Educator, vol. 33, no. 3, pp. 9-22. 2012. [Online]. Available: https://scholarsjunction.msstate.edu/ruraleducator/vol33/iss3/2/v33i3.408[8] S. L. Hartman, “Academic coach and classroom teacher: A look inside a rural school collaborative partnership,” The Rural Educator, vol. 38, no. 1, pp. 16-29. 2012. [Online]. Available: https://scholarsjunction.msstate.edu/ruraleducator/vol33/iss3/2/[9] R. S. Harris and C. B. Hodges, “STEM education in rural schools: Implications of untapped potential
statistical analyses toaddress complex and nuanced research questions important to the field. Our results are consistent with Borrego et al. (2009), highlighting the importance ofquantitative methods in engineering education research. Nevertheless, our results alsodocumented the prevalence of using all quantitative, qualitative, and mixed methods in JEE, asasserted in Borrego et al. (2009). Finally, our results echo Borrego et al. (2009)’s call for usingadvanced quantitative research methods beyond the boundary of disciplinaries, as many of theadvanced methods originated from other social sciences fields. While the excessive reliance onbasic descriptive statistics is still common, our results underscore joint efforts made byengineering
University Gregory S. Mason received the B.S.M.E. degree from Gonzaga University in 1983, the M.S.M.E. de- gree in manufacturing automation from Georgia Institute of Technology in 1984 and the Ph.D. degree in mechanical engineering, specializing in multi-rate digitalDr. Teodora Rutar Shuman, Seattle University Professor Teodora Rutar Shuman is the Chair of the Mechanical Engineering Department at Seattle Uni- versity. She is the PI on an NSF-RED grant. Her research also includes electro-mechanical systems for the sustainable processing of microalgae. Her work is published in venues including the Journal of Engineering Education, IEEE Transactions on Education, International Journal of Engineering Educa- tion
. Perkins, J. Gesun, M. Scheidt, J. Major, J. Chen, E. Berger, and A. Godwin, “Holistic wellbeing and belonging: attempting to untangle stress and wellness in their Impact on sense of community in engineering,” International Journal of Community Well-Being, vol. 4, no. 4, pp. 549-580, 2021, doi: 10.1007/s42413-021-00149-z.[4] K. J. Jensen, E. M. Johnson, J. F. Mirabelli, and S. R. Vohra, “CAREER: Characterizing Undergraduate Engineering Students’ Experiences with Mental Health in Engineering Culture” in 129th ASEE Conference, Minneapolis, MN, 2022, https://peer.asee.org/41926.[5] K. J. Jensen, S. R. Vohra, J. F. Mirabelli, A. J. Kunze, I. M. Miller, and T. E. Romanchek, “CAREER: Supporting Undergraduate
, and a published author. He is a former McNair Scholar, National Academies of Sciences, Engineering, & Medicine-Ford Foundation Fellow, Herman B. Wells Graduate Fellow, Inter- national Counseling Psychologist, former Assistant Professor at the University of Kentucky, and current Post-Doctoral Research Scholar at the University of Pittsburgh. Dr. Z.’s research program focuses on examining the impact of intersectional oppression on historically excluded groups & creating culturally relevant interventions to enhance their well-being. Within this framework, he studies academic persis- tence and mental wellness to promote holistic healing among BIPOC. He earned Bachelor’s degrees in Psychology &
). The Fourth Industrial Revolution. Currency; Illustrated edition. ISBN-10 : 9781524758868. 192 pages. 2. Accreditation Board for Engineering and Technology (ABET). (2021). Criteria for Accrediting Engineering Programs, 2022 –2023. 51 pages. https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineeri ng-programs-2022-2023/ 3. F. Z. Moser. 2007. "Faculty Adoption of Educational Technology". Educause Quarterly. Nov. 1, 2007. 66-69. 4. N. P. Wingo, N. V. Ivankova, and J. A. Moss. 2017. "Faculty Perceptions about Teaching Online: Exploring the Literature Using the Technology Acceptance Model as an Organizing Framework." Online Learning. 21(1), 15-35. S. Y. Yousafzai, G. R
. Borneman, J. Littenberg-Tobias, and J. Reich, “Developing Digital Clinical Simulations for Large-Scale Settings on Diversity, Equity, and Inclusion: Design Considerations for Effective Implementation at Scale,” in Proceedings of the Seventh ACM Conference on Learning @ Scale, Virtual Event USA: ACM, Aug. 2020, pp. 373–376. doi: 10.1145/3386527.3405947.[4] S. Kaka et al., “Digital Simulations as Approximations of Practice: Preparing Preservice Teachers to Facilitate Whole-Group Discussions of Controversial Issues,” no. Journal of Technology and Teacher Education, Mar. 2021, doi: 10.35542/osf.io/95gyd.[5] M. Thompson, K. Robinson, Y. Kim, J. Reich, and K. Owho-Ovuakporie, “Teacher Moments: an online platform for
interpersonalconflicts.Acknowledgements: This work is supported by the National Social Science Fund of China(AIA220013).References Brown P R, Matusovich H M. Career Goals, Self-Efficacy and Persistence in Engineering Students. IEEEFrontiers in Education Conference (FIE), 2016. Chenchen Zhu and Luze Han. 2021. CV chatbot based on “STAR” method. In Proceedings of HumanInterface Technologies 2020/21 Winter conference (CPEN541). ACM, Vancouver, BC, Canada, 6 pages. Choi D S, Loui M C. Grit for Engineering Students. IEEE Frontiers in Education Conference (FIE), 2015. David D. Woods. Four concepts for resilience and the implications for the future of resilience engineering [J].Reliability Engineering and System Safety,2015(141):5-9. French B.F, Immekus J C, Oakes
. Furthermore, monitoring factors such astemperature, humidity, pH, and algae nutrients is vital for the maximum biomass yield.How to calculate energy yield• The number of algae that is produced: This can be calculated using the following equation: Algae biomass (g) = Light intensity (μmol/m2/s) x Photosynthetic efficiency (%) x Culture volume (L) x Culture period (days) as per Brennan et al.[1]• The energy content of the algae: The energy content of algae varies depending on the species,but it is typically around 20-40% of the dry weight.• The efficiency of the biofuel production process: This can be calculated using the followingequation: Biofuel yield (L/kg) = (Algae dry weight (kg) x Energy content (kJ/kg)) / Energy content
differentiate fromsources in my normal reference list (e.g. [A27] instead of [27]). For in-text citations, I only usethe generic term ‘authors’ when referring to the writers of a text, never the author’s or authors’last name(s). Finally, for the few articles quoted twice, I have assigned them a unique number foreach quote. While this does introduce redundancy to the reference list, it avoids drawingadditional attention to the articles in-text.Structural useThe structural use category characterizes articles based on how neurodiv* was used relative tothe article’s purpose. In other words, it describes what role neurodiv* played in the article. Thiscategory consists of four usage groups: casual, minor context, major context, and focus. Iassigned each
Percentage of participants Coping strategy Coping strategy using strategy (N=55) using strategy (N=55) Music/art/performance/ Alcohol use 11% (n = 6) 4% (n = 2) movies (not at home) Caffeine use 5% (n = 3) Pet(s) 7% (n = 4) Eating to relax 35% (n = 19) Planning or scheduling 24% (n = 13) Errands/shopping 4% (n = 2) Reading 16% (n
consider it to be the appropriate time to share said details. However, future publicationswill discuss data collection and analysis in more detail.AcknowledgementThe material is based upon work supported by the NSF 2217477. Any opinions, findings, andconclusions or recommendations expressed in this material do not necessarily reflect those of theNSF.ReferencesAmerican Association of University Professors (AAUP). 2018. https://www.aaup.org/news//data-snapshot-contingent-faculty-us-higher- ed#.YKqu9ahKg2wvAn, S. 2016. “Asia Americans in American History: An AsianCrit Perspective on Asian American Inclusion in State and U.S. History Curriculum Standards, Theory, and Research in Social Education.” Theory and Research in Social