as a tool for student-centeredlearning,” The Journal of General Education, vol. 50(1), pp.56-74, 2001.[9] A. S. Aldosary, “The correlation between final grade score, attendance and homework in theperformance of CED students,” European Journal of Engineering Education, vol. 20(4). pp. 481– 486, 1995.[10] D. C. Appleby, “How to improve your teaching with the course syllabus,” APS Observer,1994.[11] P. Hinchey, “Why kids say they don’t do homework,” The Clearing House vol.69, No. 4,pp. 242-245, Taylor and Francis, Ltd., Mar.-April. 1996.[12] J. Parkes, M. B. Harris, “The Purposes of a Syllabus,” College Teaching, vol. 50:2, pp.55-61, 2002.[13] M. B. Eberly, S.E. Newton, R.A. Wiggins, “The syllabus as a tool for student-centeredlearning,” The
STEM teacher preparation and professional development.Prof. Chelsey Simmons, University of Florida Chelsey S. Simmons, Ph.D., is an Associate Professor in the Department of Mechanical and Aerospace Engineering. She joined UF in Fall 2013 following a visiting research position at the Swiss Federal Insti- tute of Technology (ETH) Zurich. Her research lab investigates the relationship between cell biology and tissue mechanics, and their projects are funded by the National Science Foundation, National Institutes of Health, and American Heart Association. She has received numerous fellowships and awards, including NIH’s Maximizing Investigators’ Research Award for Early Stage Investigators (2018), BMES-CMBE’s Rising Star
- Engineering Statics 0.238 0.303 Engineering 0.301 0.437 DynamicsFor both classes that have prerequisites, the addition of prerequisite grade(s) to the model withNCA factors and traditional measures as predictors is a statistically significant improvement(partial F-test p-value < 0.001).Discussion and ConclusionLooking at the models using NCA factors as predictors of engineering grades, we can see thatthere are clear patterns in how the factors influence success. Many of the NCA factors aremalleable, so understanding these patterns is a crucial step towards introducing initiatives in theclassroom to help students reach their full potential. The
Higher Education, 2017, 11-17. https://doi.org/10.1002/he.20257 [2] S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, M. P. Wenderoth. Active learning boosts performance in STEM courses. Proceedings of the National Academy of Sciences, Jun 2014, 111 (23) 8410-8415; DOI: 10.1073/pnas.1319030111. [3] M. E. Weimer, Learner-centered teaching: Five key changes to practice. San Francisco, Jossey- Bass, 2002. ISBN 0-7879-5646-5.[4] G. D. Kuh, J. Kinzie, J. H. Schuh, E. J. Whitt, Student success in college: Creating conditions that matter, San Francisco: Jossey-Bass, 2010, ISBN: 978-0-470-59909-9.[5] S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett, & M. K. Norman, “How Learning Works
Paper ID #34731Paper: Overcoming Comfort Zones to Better the Self-Efficacy ofUndergraduate Engineering Students (Tricks of the Trade) (WIP)Pasquale Sanfelice, Pasquale Sanfelice completed Associates in Engineering Science (AES) at the City Colleges of Chicago- Wilbur Wright College as the class of 2021’s salutatorian. Pasquale was admitted to Wright as an Engi- neering Pathway student in Fall 2019 and will pursue his bachelor’s degree in Biomedical Engineering at Northwestern University in Fall 2021. Pasquale was the American Chemical Society’s Student Chapter president AY 2020-2021, a volunteer engineering tutor, and a
PhysicsTeacher, 30, 141–158 https://aapt.scitation.org/doi/pdf/10.1119/1.2343497Lindell, R. S., Pea, E., & Foster T.M. (2007). Are They All Created Equal? A Comparison ofDifferent Concept Inventory Development Methodologies, American Institute of PhysicsConference Proceedings, 883(14), 14-17. https://doi.org/10.1063/1.2508680Loch, B., & Lamborn, J. (2016). How to make mathematics relevant to first-year engineeringstudents: Perceptions of students on student-produced resources. International Journal ofMathematical Education in Science and Technology, 47(1), 29–44.https://doi.org/10.1080/0020739X.2015.1044043Magana, A. J., Falk, M. L., Vieira, C., & Reese, M. J. (2016). A case study of undergraduateengineering students' computational literacy
specific impact of theactivities in promoting wellness, as well the use of wellness techniques and campus resourceslongitudinally after participating in the course.AcknowledgmentsThis project was supported by the University of Illinois Faculty Retreat Grant and theDepartment of Bioengineering. The authors thank the students for sharing their perspectives. References [1] X. Wang, S. Hegde, C. Son, B. Keller, A. Smith, and F. Sasangohar, “Investigating Mental Health of US College Students During the COVID-19 Pandemic: Cross-Sectional Survey Study,” J. Med. Internet Res., vol. 22, no. 9, p. e22817, Sep. 2020, doi: 10.2196/22817. [2] A. Kecojevic, C. H. Basch, M. Sullivan, and N. K. Davi, “The impact of the COVID-19 epidemic on mental
as lifestyle and a meritocracy of difficulty: Two pervasive beliefs among engineering students and their possible effects," presented at the ASEE Annual Conference & Exposition, Honolulu, HI, 2007.[3] C. E. Foor, S. E. Walden, and D. A. Trytten, "“I wish that I belonged more in this whole engineering group:” Achieving individual diversity," Journal of Engineering Education, vol. 96, pp. 103-115, 2007.[4] E. Godfrey, A. Johri, and B. Olds, "Understanding disciplinary cultures: The first step to cultural change," Cambridge handbook of engineering education research, pp. 437-455, 2014.[5] D. Eisenberg and S. K. Lipson, "The Healthy Minds Study 2018-2019 Data Report," 2019.[6] A. Danowitz and K
computations. The work completed in this project could also be adaptedto be used as a mini project or laboratory activity for an undergraduate wireless communicationscourse.AcknowledgmentsThe author gratefully acknowledges the work conducted by Josiah Morales, a York College ofPennsylvania Electrical Engineering alumni, on this project.References[1] D. Lopatto, ”Undergraduate research Experiences Support Science Career Decisions and Active Learning”, CBE – Life Sciences Education, vol, 6, no. 4, pp. 297-306, 2007.[2] S. Russell, M. Hancock and J. McCullough, ”Benefits of Undergraduate Research Experiences”, Science, vol. 316, no. 5824, pp. 548-549, 2007.[3] S. Kaul, C. Ferguson, P. Yanik and Y. Yan, ”Importance of Undergraduate Research
Paper ID #33655Assessing the Academic and Social Growth of STEM Transfer StudentsProf. Thomas Woodson, Stony Brook University Thomas S. Woodson is an associate professor in the Department of Technology and Society at Stony Brook University. He investigates the effects of technology on inequality throughout the world and the causes/consequences of inclusive innovation. For the past several years he has studied the effectiveness of scientific funding to have broader impact, and ways to improve diversity in STEM fields. He is currently the director of the $4 million State University of New York Louis Stokes Alliance for
9 22 41 12 3.64The frequency of communication by instructors 0 4 12 42 27 4.08Rate instructor(s) overall teaching effectiveness? 2 2 5 41 38 4.26The results from the post-survey are listed in Table 2. The pattern and trend remain the same,however, there is an overall positive shift in satisfaction across all categories. Specifically, theaverage score for each individual element increased in the post surveys. In addition, thestatistical modes increased for two of the categories, namely Frequency of Communication byyour Instructors and Rate Instructor(s) overall teaching
believethat our modules had a greater impact on those students who were newer to computationalthinking, over those who had prior experience and were enrolled in upper-level computationalcourses.1 IntroductionAccording to Wing, Computational Thinking (CT) is the thought processes involved informulating a problem and expressing its solution(s) in such a way that an information processor– human or machine – can effectively carry out that solution [1]. The educational philosophybehind Computational Thinking is that problems in every discipline can be solved by the tools ofcomputation such as algorithmic thinking, decomposition, abstraction, pattern recognition. Forinstance, one of the pillars of computational thinking is algorithmic thinking
pushed beyond the scope of criteria and constraints set up by the client and occasionally contemplated additional criteria that led to a greater diversity of outcomes. For example, Cameron’s questioning of Ben’s design led to Ben considering the size and other defining characteristics of the artifacts, criteria not mentioned by the client (see Table 2). Table 2 Cameron Questioning Ben’s DesignSpeaker Discourse Code(s)Ben No, no, no, but you have to agree that this one (pointing to his MSOL, own design) is pretty good though. You can't basically get ADS-cc through; you have to go through three times before you get to the artifacts because
journal or proceedings, (Scan) and the resulting set were manuallyscanned for acronyms in all capital letters within fields with data entered by patrons. (Examine) Entrieswith all capital letter acronyms were flagged and further assessed for the standard format type. All itemsdetermined to be standards were then reviewed to determine if the request was fulfilled by ILL staff byany means. [7]To replace the Scan step, Author 1 first developed a regular expression, using Python in JupyterNotebooks (code, documentation, and de-identified dataset can be found on Author 1’s GitHub:https://github.com/hburns2/desperately-seeking-standards). Regular expressions (or regex), used for textprocessing and querying, identify patterns within written text. A
challenging national (and even global)emergencies. Furthermore, these events also provide a platform for highlighting the positives andstrengths of HBCUs in response to COVID-19 in supporting their stakeholders. The awarenessand joint interest established during these events can lead to the development of a robust HBCUnetwork that can be sustained through a commitment to Black student success. References[1] R. I. Boothroyd, A.Y. Flint, A.M. Lapiz, S. Lyons, K.L. Jarboe, and W.A. Aldridge, “Activeinvolved community partnerships: co-creating implementation infrastructure for getting to andsustaining social impact,” Transl. Behav. Med., vol. 7, no. 3, pp. 467-477, 2017.[2] M. Pellecchia, D. S. Mandell, H.J
,” Journal of Business Ethics, vol. 8, no. 4, pp. 217-230, 1989. [4] H.W. Gehman, Jr., J.L. Barry, D.W. Deal, J.N. Hallock, K.W. Hess, G.S. Hubbard, J.M. Logsdon, D.D. Osheroff, S.K. Ride, R.E. Tetrault, S.A. Turcotte, S.B. Wallace, and S.E. Widnall, “Columbia Accident Investigation Report, Volume I,” NASA, Aug. 26 2003. Available:http://www.nasa.gov/columbia/home/CAIB_Vol1.html. [5] S. Bates, “Flint water crisis: For young engineers, a lesson on the importance of listening,” NSF.gov, 23 March 2016. [Online]. Available: http://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=138060&WT.mc_id=USNSF_1. [Accessed Apr. 11, 2019]. [6] K. Samuelson, “Companies That Built Collapsed FIU Bridge Had Been Fined for
measured correctly. Finally, students must open inExcel the ASCII file CrankSlider.DAT output by their program, and plot the displacement S,velocity dS/dt and acceleration d2S/dt2 of the slider (see Figure 3), as well as the transmission 2angle and the discriminant (labeled Delta in Figure 4) of the quadratic equation that occurs whensolving the position problem of the mechanism. (a) (b)Figure 2: The 10th frame of the CrankSlider.PAS simulation as read from the DXF file (a) output by theoriginal program, and (b) by the modified program as required in Assignment 2.Figure 3: Plot
theyparticipated in any professional development for teaching, and in particular on the use of active learning.This quantitative data were analyzed using descriptive statistics. Percentages, measures of centraltendency, such as the mean, median and mode were examined as well as demographic distributions.Qualitative data included semi-structured interviews with participants, and aimed to delve further intohow engineering instructors learned about various active learning strategies, why they decided toimplement them in their course(s), what kinds of support they received as well as any challenges theyhad, and how they overcome those challenges. This qualitative data was analyzed following a recursiveand spiral pattern to code for recurring themes and
paperreviews the findings from the subset of longitudinal data to add to the literature related to thisinstrument and to gather feedback related to future directions for this project.BackgroundThe Campbell University’s School of Engineering is able to offer students need-basedscholarships through an NSF S-STEM grant. As part of this program, students are expected totake part in a variety of professional development activities including faculty and peermentoring, industry tours, tutoring, and internship preparation assistance. This institution islocated in a rural area with many first-generation college students in the engineering studentpopulation. The institution also accepts many students into the engineering program who mayneed an additional
, earning the Ph.D. degreed e g a a ee a j b ffe . S , a e i e e ed i a academic i i i i ki g e ha h ee m e ea f life be d he bachel deg ee. S me field migh e i e kexperience in the profession or post-doctoral studies. If the position is obtained, tenure will beeither earned or denied. So, conceivably after about 10 years of study and work the individualmight have failed in the pursuit of an academic career and some major or minor career changecould be necessary. A negative aspect of pursuing a Ph.D. is that, if a teaching position is notavailable, having the Ph.D. degree might in some cases reduce employment opportunities. In thecase of a traditional engineering position, the candidate might be viewed
(2) where x is the expected value for x calculated from the mean value of n replicated measurements 1 x x (3) n and ux is the combined uncertainty due to random (uR) and systematic (uZ) errors in the measured values: ux uR2 uZ2 (4) 2. Assume random errors are normally distributed and use the standard error in Equation (5) for the random uncertainty in terms of the standard deviation s, and the sample size, n
into two parts: the theoretical and functional. Ideas behindthe advanced manufacturing process under discussion were introduced in the first part of themodule(s) as well as defining the relationship between engineering topics covered in thecurriculum; whereas the active online experimentations were introduced in the second part ofeach module(s) focusing on preliminary engineering concepts and techniques such design anddesign rationale, durability, and other topics needed to build the UAVs. The students wereadvised to use SoildWorks or TinkerCad to make slight adjustments to the general design of theirprojects. The students could also use MATLAB to collect, store and analyze their data dependingon the level of programing experience each student
concepts by using 5E learning cycle model. Hacettepe Üniversitesi EğitimFakültesi Dergisi, 36(36).[6] Wilson, C. D., Taylor, J. A., Kowalski, S. M., & Carlson, J. (2010). The relative effects andequity of inquiry-based and commonplace science teaching on students' knowledge, reasoning,and argumentation. Journal of research in science teaching, 47(3), 276-301.[7] Gutierrez, K. S., Ringleb, S., Kidd, J., Ayala, O., Pazos, P., & Kaipa, K. (2020) “PartneringUndergraduate Engineering Students with Preservice Teachers to Design and Teach anElementary Engineering Lesson through Ed+gineering,” 2020 ASEE Annual Conference andExposition, Montreal, Canada, June 21-24, 2020.[8] Kidd, J., Kaipa. K., Sacks, S., Ringleb, S., Pazos, P., Gutierrez, K
intervening with the groups’work to improve the quality of students’ interactions in collaborative problem solvingengineering classrooms.References[1] J. Roschelle and S. Teasley, "The construction of shared knowledge in collaborative problem solving", in Computer Supported Collaborative Learning, 1995, pp. 69-96.[2] B. Barron, “When Smart Groups Fail,” Journal of the Learning Sciences, vol. 12, no. 3, pp. 307–359, 2003.[3] C. Kaendler, M. Wiedmann, N. Rummel, and H. Spada, "Teacher Competencies for the Implementation of Collaborative Learning in the Classroom: a Framework and Research Review", Educational Psychology Review, vol. 27, no. 3, pp. 505-536, 2014. Available: 10.1007/s10648-014-9288-9.[4] R
, vol. 26, no. 2, pp. 46-73, 2015.[9] M. Friebroon Yesharim and M. Ben-Ari, "Teaching Computer Science Concepts ThroughRobotics to Elementary School Children", International Journal of Computer ScienceEducation in Schools, vol. 2, no. 3, 2018. Available: 10.21585/ijcses.v2i3.30.[10] S. Papert, Mindstorms: children, computers, and powerful ideas. Brighton: Basic Books,Inc., 1980.[11] E. M Silk, R. Higashi and C. D Schunn, "Resources for Robot Competition Success:Assessing Math Use in Grade-School-Level Engineering Design", in American Society forEngineering Education, Vancouver, BC, Canada, 2011.[12] N. Arís and L. Orcos, "Educational Robotics in the Stage of Secondary Education:Empirical Study on Motivation and STEM Skills", Education Sciences
University,” Sci. Sci. & Manag. S&T, vol. ED-9, pp. 145-149, Sep. 2006.[3] C. Zheng, “Example of Entrepreneurial Universities: Experience of Technical University of Munich,” Jiangxi. Educ., vol. ED-10, pp. 57-61, Mar. 2016.[4] D. Sang, J. Zhu, “Taken Innovation and Entrepreneurship College as Carrier to Promote Effective Development of Entrepreneurship Education in Colleges and Universities,” J. Ideo. & Theor. Edu., vol. ED-11, pp. 72-76, Jun. 2011.[5] M. Jin, J. Zeng, M. Do, “Current Situation of Innovation and Entrepreneurship of Engineering Students,” J. Nat. Sci., vol. ED-9, pp. 293, Sep. 2017.[6] D. Dai, “An Empirical Research of Innovation and Entrepreneurship Competition on Improving College Students
different age groups and disciplines to facilitatecollaborative problem solving activities.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant No. 1628976. Anyopinions, findings, conclusions or recommendations expressed in this material are those of the authors and do notnecessarily reflect the views of the National Science Foundation. References[1] J. Roschelle and S. Teasley, "The construction of shared knowledge in collaborative problem solving", in Computer Supported Collaborative Learning, 1995, pp. 69-96.[2] R. Gillies, A. Ashman and J. Terwel, The Teacher's Role in Implementing Cooperative Learning in the Classroom. Boston, MA
References[1] B. Capobianco, H. A. Diefes-dux, I. Mena, and J. Weller, “What is an engineer? Implications of elementary school student conceptions for engineering education,” Wiley Online Libr., vol. 100, no. 2, pp. 304–328, 2011.[2] X. Chen and M. Soldner, “STEM attrition: college students’ paths into and out of STEM fields: statistical analysis report,” 2013.[3] M. M. McDonald, V. Zeigler-Hill, J. K. Vrabel, and M. Escobar, “A Single-Item Measure for Assessing STEM Identity,” Front. Educ., vol. 4, Jul. 2019.[4] S. Brown and R. Lent, Career development and counseling: Putting theory and research to work. 2004.[5] L. S. Gottfredson and S. Brown, “Applying Gottfredson’s Theory of Circumscription and
from the beginning: The definitive history of racist ideas in America. New York: Nation Books, 2016.[3] A. L. Pawley, J. A. Meija, and R. A. Revelo, “Translating Theory on Color-blind Racism to an Engineering Education Context: Illustrations from the Field of Engineering Education,” presented at the ASEE Annual Conference, Salt Lake City, UT, 2018.[4] Data USA, “Engineering | Data USA,” 2019. [Online]. Available: https://datausa.io/profile/cip/engineering#employment. [Accessed: 13-Dec-2019].[5] D. E. Chubin, G. S. May, and E. L. Babco, “Diversifying the Engineering Workforce,” J. Eng. Educ., vol. 94, no. 1, pp. 73–86, Jan. 2005, doi: 10.1002/j.2168-9830.2005.tb00830.x.[6] A. E. Slaton, Race, Rigor, and Selectivity in U. S
have already proposed algorithms, pipelines and tools to resolve the issues based onthe U.S. Health Insurance Portability and Accountability Act (HIPAA)’s requirement onprotecting protected health information [6]–[8]. However, HIPAA requires protection on lots ofunexpected information in the academic setting, such as locations, dates, telephone numbers, faxnumbers, social security numbers, etc. [9]. In the education context, Rudniy reported anautomating deidentification project using peer feedback textual data for online writing projectsvia MyR [10]. However, our peer to peer comment data is structured in groups to facilitateteamwork learning so that it is highly possible that the commenter mentions more than one groupmember, which might