based on average test scoreswith partial credit. In my implementation, the course topics were grouped into categoriescorresponding to grade levels D, C, B, and A. Each category has 3-5 topics [D1-D5, C1-C4, B1-B3, and A1-A3], and the corresponding grade is earned if a short test for each topic (or in somecases, a pair of topics) in the category is “Approved”. A grade of Approved is earned fordemonstrating A-quality work (only minor errors permitted). A grade of “ConditionallyApproved” is earned for demonstrating B-quality work, with full approval being earned throughwriting corrections. A grade of “Not Yet Approved” is assigned for demonstrating C or lowerquality work, with full approval requiring another test of the same topic to be taken. In
latter group was found to have higher finalexam grades implying greater improvement.Juhler et al. [7] examined the test and retest scores for 1,314 students who completed anintermediate algebra course. For each of seven chapter tests, if the student achieved less than a Bgrade, they could opt to take a retake. The score on the retake replaced the original test score,regardless of whether it was an improvement, but was limited to a B grade. On average, studentswere eligible to take 5.30 retakes and opted to take 2.31 retakes. The majority (88-95%) ofstudents who took the retake improved their score. However, there was no significant correlationbetween the number of retakes and the final exam score.Abraham [8] offered 150 students in intuitive
original lab on your own before attempting this quiz.You are allowed to run the Wireshark while completing this lab.The following questions are similar to Network+ type of questions and are relate to trace named:http-ethereal-trace-1.1. If you set the http filter, how many packets you will see: a. 3 b. 4 c. 5 d. 62. If you set the SNMP filter, how many packets you will see: a. 3 b. 4 c. 5 d. 63. For HTTP packet number 10 (Frame 10), ), the total size of the packet is: a. 555 b. 439 c. 541 d. 13954. For HTTP packet number 10 (Frame 10), the requesting user agent is: a. User-Agent: Mozilla/5.0 b. User-Agent: Firefox/5.0 c. User-Agent: Chrome/5.0 d. User
resubmission of work and flexible deadlines,” in 2003 GSW, 2021. [3] M. L. Amyx, K. B. Hastings, E. J. Reynolds, J. A. Weakley, S. Dinkel, and B. Patzel, “Management and treatment of attention-deficit/hyperactivity disorder on college campuses,” Journal of Psychosocial Nursing and Mental Health Services, vol. 53, no. 11, pp. 46–51, 2015. [4] C. Kuimelis, “The deadline dilemma: when it comes to course assignments, how much flexibility is too much?” Nov 2022. [Online]. Available: https://www.chronicle.com/article/the-deadline-dilemma [5] D. Thierauf, “Feeling better: A year without deadlines,” Nineteenth-Century Gender Studies, vol. 17, no. 1, 2021. [6] M. Schroeder, E. Makarenko, and K. Warren, “Introducing a late bank in online
’ family members, particularly when it comes to college expenditures.However, many of these students are about to experience a significant transformation in their lives, thismeans that they are close to completing an academic degree and obtaining their first professional position,and their financial responsibilities will change considerably. a) Personal Expenditures b) College Expenditures 100% 100% 80% 80% 65
via an Arduino microprocessor connected to a laptop. After several rounds ofcharacterization and design, four experimental modules were completed which allowed studentto perform the following experiments: a) fluid flow (FLU), b) pump and valve characterization,(CUR) c) heat exchangers (HEX), and d) fixed bed columns (BED). Similar kits have beendesigned by other institutions for experiments on momentum and heat transfer, chemicalkinetics, crystallization, and particle science, either for UOLs or as practical modules for lectureclasses[5]–[8]. Using synchronous video-conferencing instruction, multiple sections of the classwere offered in Fall 2020 (100% online) and Spring 2021 (online + in-person; not in our UOL).In both semesters, students
Age Mean = 19.96 SD = 1.48 Sex 24% Male 76% Female Grade Level 37% Freshman 13% Sophomore 20% Junior 30% Senior Expected Grade 46% A 52% B 2% C 0% Below CPhase I ResultsA Kaiser-Meyer Olkin (KMO) factor adequacy was run, and a cut-off below .55 was usedto identify and eliminate underperforming items. This KMO adequacy was rerun with thetop items until item removal did not improve the overall Measurement System Analysis(MSA). The purpose of MSA computation was to assure that a selected
robot (consisting of a mobile base and itsmicro-controller, the Cortex). The Cortex controls the robot’s basic motion, while the RaspberryPI handles image processing and high-level decision making. Commands such as “turning to theleft”, “going straight”, and “turning to the right” were sent from the PI to the Cortes for execution. This paper describes vision-based control of a PI-controlled VEX robot. This new version doesnot include the Cortex. The VEX mobile base was controlled directly/solely by the Raspberry PI.In other words, there is only one “brain”, i.e., the Raspberry PI [Fig. 1 (b)]. While in the previousversion, there are two “brains”: the PI and the Cortex [Fig. 1 (a)]. This development addressed onedirection of further
, agriculture, materials, career planning, and other topics. b) This is Engineering, taken in the second semester, will be a freshmen design style class, with hands-on problem-based learning, with sustainability embedded in all projects. c) A seminar on Justice, Equity, Diversity, and Inclusion will be developed for students to explore issues such as implicit bias and paternalism and reinforce the idea that co-design with communities will reduce discrimination and lead to better solutions. d) New courses, Wellbeing and Sustainability Economics will be developed to introduce students to essential ideas of natural capital, circular economies, and measures of well-being and prosperity. e) Other new courses include Products, Services, and
). While military training andexperience are valued they, does not always translate to a clear and straightforward career incivilian life after retirement or when servicemen (i.e., military personnel, soldiers, and officers)separate from the military; every year, about 2000,000 veterans leave the military. Over the nextfive to ten years, an increasing number of those 2000,000 people will become engaged in datascience and machine learning, driven by their interests, skills, backgrounds, and changing businessneeds[26]. The reason for this is (a) Data science will drive every type of business, and (b) TheArmy on a continuous basis, will need skillful personnel ( data engineers, analysts and scientists )to embrace its growth in emerging analytic
onmentoring. For example, Elliott et al. found that mentoring proved to be vital for women andunderrepresented minorities in STEM fields and Engineering coursework that had a focus onentrepreneurship [20] Additionally, Blaique et al. found that mentoring was a key predictor ofwomen and underrepresented groups in STEM fields going into and staying in the STEMworkforce [21]. b. financial support through scholarshipsScholarships helped retain the students by providing them with financial resources to continuetheir studies and reduce their financial burden and need to work extended hours. Scholarshipsprovided students with motivation and recognition for their achievements, which encouragedthem to stay in school and continue their studies. Additionally
Paper ID #38817A comparison of shared mental model measurement techniques used inundergraduate engineering contexts: A systematic reviewMr. Gregory Litster, University of Toronto Greg Litster is a PhD student in Engineering Education at the University of Toronto in the Institute for Studies in Transdisciplinary Engineering Education and Practice. He received his MASc degree in Man- agement Sciences (2022) and a Bachelor of Knowledge Integration degree (2020), both from the Univer- sity of Waterloo. His research interests are focused on mental models for engineering design teams, group dynamics and how collaboration
Annual Conference & Exposition, Jun. 2011, p. 22.867.1-22.867.31. Accessed: Feb. 26, 2023. [Online]. Available: https://peer.asee.org/industry-university-partnership-in-senior-capstone-design-course[4] G. Crain and M. Tull, “A Capstone Course Targeting Industry Transition,” presented at the 2004 Annual Conference, Jun. 2004, p. 9.11.1-9.11.9. Accessed: Feb. 26, 2023. [Online]. Available: https://peer.asee.org/a-capstone-course-targeting-industry-transition[5] M. McGinnis and R. Welch, “Capstones With An Industry Model,” presented at the 2010 Annual Conference & Exposition, Jun. 2010, p. 15.260.1-15.260.13. Accessed: Feb. 26, 2023. [Online]. Available: https://peer.asee.org/capstones-with-an-industry-model[6] J. B
vertical shaft that bears an impeller at itsend. Impeller was immersed in a liquid, and when the machine was in motion, the impelleragitated the liquid. The bill of materials is given in Table I. (a) (b)Figure 2. The troubleshooting setup used in the study (a). A commercially available gamma-type Stirling engine is modified for this study’s purpose. In this setup, an electric heaterprovides the heat energy to run the impeller. The load on the impeller is determined by theviscosity of the liquid that it stirs. Close view of the heating coils on the displacer cylinder (b). Table I. The Bill of Materials for a Stirling
. The group bubbled pure CO2 in samplesof 0.5M and 1M NaOH. A diagram of the experimental setup is depicted below in Figure 1b.Figure 1: a) PFD of original flooding point experiment and b) Initial experimental set-up forDAC using NaOHRotation 2The first group in rotation two built a small semi-batch reactor using a Vernier carbon dioxidemonitor, parts available in the laboratory, and supplemental parts from the local hardware store.They were able to measure and present the first set of carbon dioxide removal data to the classand passed on a prototype reactor as well as suggested improvements to the next group. Theyalso ordered additional parts for the next group, including a second gas sensor. The group in thesecond section also created a small
inthe course (minimum grade of B), (2) they had to have shown mastery of the equipment theyworked on while in the course, (3) they had to be outgoing and willing to engage with thestudents in the class, and (4) they had to exhibit a willingness to teach the students in the coursewithout just giving them the answer. The author used a combination of observing the studentswhen they took the course and an informal interview with the perspective coaches to addressthese four criteria in terms of selection of the coaches. After the coaches were recruited, theywere then added to the payroll of the Department of Chemical Engineering which providedfinancial support for the students in the form of an hourly wage with a weekly workload of ~8-10hours
bystudying the current state of wind engineering tracks within civil engineering programs offeredworldwide and identifying their Strengths, Weaknesses, Opportunities, and Threats (SWOT). Toachieve these objectives, this research (a) analyzed the different civil engineering programs thatinclude wind engineering tracks offered worldwide and identified the academic institutions thathave academic expertise and equipment including atmospheric boundary layer (ABL) windtunnels, a fundamental tool for the research and study of wind events; (b) conducted a survey toall WE faculty and students doing research on these topics at Florida International University togather information on the courses offered and the intention of the course, as well as informationon
data-driven, or in other words, we allow themesto emerge from the data. Subsequently, we allow the research question to evolve through thecoding process. For thematic analysis, we draw on Braun and Clarke’ framework (2006) [16],which includes six phases (a) familiarizing/reading all data, (b) generating initial codes, (c)identifying initial themes, (d) reviewing and refining themes, (e) defining and naming thethemes, and (f) producing the report.Analysis and DiscussionThematic analysis of the data that captured transfer student experiences generated 17 initialcodes (548 coded text segments), from which four major themes emerged: universitycharacteristics, department academics, department support services, and student affectiveelements. Figure
Paper ID #37892Examining Engineering Students’ Shift in Mindsets Over the Course of aSemester: A Longitudinal StudyDr. Dina Verdin, Arizona State University, Polytechnic Campus Dina Verd´ın, PhD is an Assistant Professor of Engineering in the Ira A. Fulton Schools of Engineering at Arizona State University. She graduated from San Jos´e State University with a BS in Industrial Systems Engineering and from Purdue University with an MS in Industrial Engineering and PhD in Engineering Education. Her research interest focuses on changing the deficit base perspective of first-generation col- lege students by providing asset
. How did thisinterdisciplinary learning experience affect your ability to engage with the curriculum?Open-Ended Reflection Question B (Debrief): What went well? What didn’t go so well? Whatwill you do differently next time?3.4 Data AnalysisWe analyzed the qualitative data for repeated responses among student experiences. For this, weused a thematic analysis approach as described by [31]. Briefly, student responses were reviewedin total. The most common responses were classified into 3 main themes with subthematic areaslisted. Four to six student quotes supporting each theme have been provided enabling readers toindependently assess appropriateness of described themes [32].4. ResultsQualitative analysis of student quotes led to identification
solicitedfeedback from 13 students who had participated in similar exam review activities in the Winter2021 offering of the heat transfer course using the same survey. The survey results aresummarized below. Note that the Winter 2021 students completed the survey in the academicyear following the completion of the course, whereas, at the time of writing this paper, theWinter 2023 students have completed one survey after their first exam.Likert Scale Survey QuestionsSelect your level of agreement with the following statements:(1 - Strongly Disagree, 2 - Disagree, 3 - Neutral, 4 - Agree, 5 - Strongly Agree, DNR - Do NotRemember) A) Participation in the exam review activity improved my performance on the exam. B) The questions discussed and/or solved as
engineeringthinking and engineering design in addition to the tools to succeed in their new college environment. Inthe late 2000s, providing students just exploring the possibility of pursuing an engineering degree wasdeemed appropriate with an adjusted version of the first-year engineering seminar. This version wascomparable but differed in depth and rigor. The two versions of the seminar were for (a) acceptedengineering and (b) intended engineering students.The intended group made up approximately 35%-40% of the first-year engineering cohort. Thispercentage of the first-year cohort remained consistent since 2016; however, it was a more diverse groupin terms of race/ethnicity and first-generation status than that of the accepted students consisting of
, NY.[22] Highlander Research and Education Center and Gabriela Hurtado-Ramos (artist), Methodologies en Color (1), https://highlandercenter.org/our-story/mission/ (accessed Feb. 28, 2023).[23] D. Boyd, Under the Radar: Popular Education in North America, A White Paper, COMM-ORG Papers, vol. 18, 2012, https://comm-org.wisc.edu/papers2012/boyd.htm (accessed Feb. 28, 2023).[24] A. Frausto Aceves, B. Torres-Alave, and S. Tolbert, “On love, becomings, and true generosity for science education: honoring Paulo Freire,” Cultural Studies of Science Education, vol. 17, pp. 217-230, 2022, https://doi.org/10.1007/s11422-021-10098-w.[25] Medibank, “Uncle Bob Randall,” Medibank, Jul. 15, 2016, https://www.medibank.com.au
will explain the reason behindthis data range in the next section.(iv) In Fig. 5 (a), we can see different options available under the “Blocks” section. Navigate tothe Output code category, then drag out a “print to serial monitor” block and place it just beforethe serial block that is already in the program. A student can change the default text to label theSerial data, such as “Sensor Value: ”, and from the dropdown menu either choose to print with orwithout a new line. Please note, in case of Fig. 4, the default block code has been used, where anumber is printed on the serial monitor. In contrast, after the code block configuration as shownon 5 (b), the serial monitor output looks similar to Fig. 6. A student can stack similar serial
multiple-choice selection but also their explanation and response to follow-up questions—to a conceptualstatics question compare across diverse institutional contexts? To address this overall question,we ask more specifically: a. How are student correctness, confidence, and their metacognitive reflections on the question related to their institution? b. What do the student responses suggest about their epistemological frames in learning statics? MethodsQuestion AdministrationFor this study, we selected one concept question which was administered via the ConceptWarehouse [29] (ConcepTest #4606), as shown in Figure 1. The question was delivered to 241students at six
Paper ID #38993Cultivating ”global competency” in a divided world: A collaborative autoethnographyof the cross-border curriculum designYiXiang Shawn Sun, National Taiwan UniversityDr. Sharon Tsai-hsuan Ku, University of Virginia Dr. Sharon Ku has dual background in physics and STS, specializing in the sociology of scientific knowledge, standardization, and science policy in the US and China. She works closely with scientists and engineers from academia, government and industry. Dr. Ku received her PhD from History & Philosophy of Science, Cambridge University in 2010, and is currently an assistant professor at Dept. of
generates compare estimates than the Bayesian method for some modeling parameters,the Bayesian approach produces substantially improved results for the standard deviationestimates of the relationship effect (𝜎𝑟 ), the autoregressive coefficient of the relationship effect(𝛽𝑟 ), the correlation between target and perceiver effects (𝜌𝑝𝑡 ), and the correlation betweenreciprocal ratings (𝜌𝑟 ). All our qualitative conclusions from Panel A holds for Panel B as well.Nevertheless, when the overall sample size has increased, the differences between the Bayesianand SR-SEM methods become smaller, due to the impact of the prior distribution beingweakened with a larger sample.Table 3Simulation Results Panel A: 15
, vol. 94, no. 1, pp. 41-55, 2005, doi: https://doi.org/10.1002/j.2168-9830.2005.tb00828.x.[9] Å. Cajander, M. Daniels, and B. R. von Konsky, "Development of professional competencies in engineering education," in 2011 Frontiers in Education Conference (FIE), 2011: IEEE, pp. S1C-1-S1C-5.[10] D. C. Montgomery and W. H. Woodall, "An Overview of Six Sigma," International Statistical Review, vol. 76, no. 3, pp. 329-346, 2008, doi: https://doi.org/10.1111/j.1751- 5823.2008.00061.x.[11] R. Bray and S. Boon, "Towards a framework for research career development: An evaluation of the UK's Vitae Researcher Development Framework," International Journal for Researcher Development, vol. 2, no. 2, pp. 99-116, 2011, doi
Paper ID #39312A Near-Peer Mentoring Framework for a Civil and EnvironmentalEngineering CurriculumMarie Bond, University of Illinois, Urbana-ChampaignProf. Ramez Hajj, University of Illinois, Urbana-ChampaignProf. Jeffery R. Roesler, University of Illinois, Urbana-Champaign University of Illinois Urbana-Champaign Professor, Civil and Environmental Engineering Associate Head and Director of Graduate Studies and ResearchDr. Arthur R. Schmidt III, University of Illinois, Urbana-ChampaignProf. Jacob Henschen, University of Illinois, Urbana-Champaign Professor Henschen completed his B.S., M.S., and PhD. at the University of Illinois Urbana
use of the direct and indirect assessments in parallel to fullycharacterize student curiosity as it relates to an EM. Future work will focus on adapting theexisting codebook to better align with the 5DCS constructs in the context of a first-yearengineering classroom and to differentiate between overt covert social curiosity, sub-constructsdistinguished by Kashdan et al., [25] in the Revised Five-Dimensional Curiosity Scale (5DCR).References[1] D. Pusca and D. Northwood, “Curiosity, creativity and engineering education,” Global Journal of Engineering Education, vol. 20, no. 3, pp. 152–158, 2018. [2] T. B. Kashdan, P. Rose, and F. D. Fincham, “Curiosity and exploration: facilitating positive subjective experiences and personal growth