production workers, such as food batchmakers who have excellent technical skills”[4]. Within the past 20 years, the United States has experienced numerous nationwide food safetyrecalls, reflecting the weaknesses in the current system. In 2010 alone, there have been 23 nationwiderecalls [7]. The urgency for a food and foodstuff ET Pathways driven by improved standards wasunderscored September 22, 2010, during the Congressional testimony of Austin J. DeCoster, owner ofDeCoster Egg Farms, whose operations were linked to the United States’ deadliest outbreak of salmonellainfected eggs that occurred in 1987, as well as this year’s recall of half a billion eggs that sickenedthousands of people.[8] Mr. DeCoster told the House Energy and
successful in, engineering studies incollege.AcknowledgementsPartial support for this work was provided by the National Science Foundation's Science,Technology, Engineering, and Mathematics Talent Expansion Program (STEP) underAward No. 0757055. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect the viewsof the National Science Foundation. The authors would also like to thank Leah Rineck,Shuwen Tang, Cindy Walker, Todd Johnson, Tina Current, Sharon Kaempfer, and JennieKlumpp (all at UWM) for their assistance with this project.Bibliography1. National Science Board. 2003. The Science and Engineering Workforce: Realizing America’sPotential. Publication NSB 03-69
: Students were put into two-person teams (dyads). Each team was given another design problem58 that neither student was exposed to previously. They were asked to design together, but record (sketch and document the details) separately.• Ideation with Design Heuristics: Students were given the same set of ten (of 77) Design Heuristics cards and asked to apply the cards while solving a given design problem.• Reflection Surveys: At the end of each intervention, students were asked to complete a short survey focused on their perceptions of that intervention. Typical questions included: “How did the structure of the problem statement affect your idea generation?” or “Which of the ideation cards appealed to you most/least?” The aim
motivation to keep building and sharing.AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.1129342. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References 1. Bureau of Labor Statistics, US Department of Labor. (2006). Occupational Outlook Handbook, 2010-11 Edition, Bulletin 2800. Washington DC: U.S. Government Printing Office. Page 24.746.13 2. National Science Foundation. (2006). Science and Engineering Degrees: 1966–2004
the National Science Foundationunder Grant No. DUE-1141076. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect the views of theNational Science Foundation.References1. Pagliaro, C.M. (2010). Mathematics instruction and learning of deaf and hard-of-hearing students: what do we know? Where do we go? In Marschark and Spencer (Eds), Oxford handbook of deaf studies, language, and education, volume 1 (pp. 156-171). New York: Oxford University Press.2. Marschark, M. and Everhart, V.S. (1999). Problem-solving by deaf and hearing students: Twenty questions. Deafness and Education International, 1(2), 65-82.3. Luckner, J.L. and McNeill, J.H
adaptive ideation behavior, and the other to encourage more innovative ideation behaviors57. Students were randomly assigned to one of the two problem frames, with half of each experimental group assigned to each framing.• Ideation in Teams: Students were put into two-person teams (dyads). Each team was given another design problem58 that neither student was exposed to previously. They were asked to design together, but record (sketch and document the details) separately.• Ideation with Design Heuristics: Students were given the same set of ten (of 77) Design Heuristics cards and asked to apply the cards while solving a given design problem.• Reflection Surveys: At the end of each intervention, students were asked to
programs, the size of the population studied will expand and the influence of one ortwo students on the overall average performance will be reduced. Furthermore, study of theoverall program goal of increasing retention and graduation rates from CEAS will need to bepostponed until sufficient time has passed for students to graduate.AcknowledgmentsPartial support for this work was provided by the National Science Foundation's Science,Technology, Engineering, and Mathematics Talent Expansion Program (STEP) under Award No.0757055. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation. The authors would also like to thank
thesurvey and we will have a better chance to examine the relations between engineers and students.AcknowledgementThis material is supported by the National Science Foundation under EEC Grant Numbers 1129403and 1129411. Any opinions, findings, conclusions, or recommendations presented are those of theauthors and do not necessarily reflect the views of the National Science Foundation.References[1] Field, D.A., (2004). Education and training for cad in the auto industry. Computer-Aided Design, 36 (14), 1431-1437.[2] Hamade, R.F., Artail, H.A. & Jaber, M.Y., (2007). Evaluating the learning process of mechanical cad students. Computers & Education, 49 (3), 640-661.[3] Ye, Z., Peng, W., Chen, Z. & Cai, Y.-Y., (2004
share insights from the family narrative (synthesisof all the data generated from the family’s participation) and results of how the family enactedspecific engineering practices. Also, the authors will share a preliminary reflection on how thesepractices might serve as a vehicle to positively impact the sense of belonging of Blackengineering students.1 IntroductionThe academic success of Black students is linked to the familial cultural capital. The familymodel has been employed as a means of helping students adjust to the rigors of higher education[1]. Positive effects on academic accomplishment are produced when a child's academicendeavors are supported by their family [2]. Familial capital shows up in the form of motivatingthe student to
both the regional military student support community andnationally.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.2045634. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of National Science Foundation.References[1] “2020 Demographics profile of the military community,” Department of Defense, 2020.[2] K. A. Holder, “Veterans who have served since 9/11 are more diverse,” United States Census Bureau. Accessed: Feb. 07, 2024. [Online]. Available: https://www.census.gov/library/stories/2018/04/post-9-11-veterans.html[3] “VA College Toolkit, ‘Characteristics of Student Veterans
is to determine whether or not the implementation of our new app willimprove rider experiences with the transit system. Additionally, the study would also look intoinsights on whether using SmartSAT app can increase the amount of people that took the publictransportation service.AcknowledgmentThis work is supported by the National Science Foundation under Grant No. 2131193. Any opinions,findings, conclusions, or recommendations expressed in this material are those of the author(s) anddo not necessarily reflect the views of the National Science Foundation.References[1] Transit Capacity and Quality of Service Manual-2nd Edition, http://onlinepubs.trb.org/onlinepubs/tcrp/docs/tcrp100/Part4.pdf.[2] Smartphone Applications To Influence Travel
those of the authors and do not necessarily reflect the National Science
freshman ECE students during their primaryintroduction to the discipline?AcknowledgementsThis material is based upon work supported by the National Science Foundation, specifically theDivision of Undergraduate Engineering in the Directorate for STEM Education, under Grant No.2020560. Any opinions, findings, conclusions, or recommendations expressed in this material arethose of the authors and do not necessarily reflect the views of the National Science Foundation.References[1] M. Prince, “Does active learning work? A review of the research,” Journal of Engineering Education, vol. 93, no. 3. pp. 223–231, 2004[2] S. Freeman, S.L. Eddy, M. McDonough, M.K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth, “Active learning increases
) Grant No. 1037808Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of the National Science Foundation. Page 23.1166.2AbstractPublished research has provided a robust set of documented tools and techniques fortransforming individual engineering courses in ways that use evidence-based instructionalpractices. Many engineering faculty are already aware of these practices and would like to use 2them. However, they still face significant implementation barriers. The E R2P effort
thestudents would know most of the answers before we began the assessment as thequestions are indeed very basic. The students overall performed the worst on the basicchemistry questions (only 44%), while they only did only somewhat better on thequestions reflecting on hands-on learning (55%).We also examined whether the students’ scores in these three content areas made adifference in their performance on four low stakes quizzes and the two mid-term exams.Only one minor difference was noted on the first three quizzes in that on quiz three, thestudents scoring higher in basic science knowledge, scored higher than their peers. But,on quiz 4, student outcomes were different for those students scoring higher (upper 50%)on their pre-course assessment
has been known to significantly increase success, retention, and graduationrates. We noticed the differences in the level of preparedness and its influence on the student’sperception of their journey. We also explored the influence of soft skills, outlook, scholarlyattributes, and support on the perception of the journey through the program. Although ourparticipants have reported that they did not perceive any overt sexism or racism, we present thefindings correlated with gender and race/ethnicity.Our future work will include fine-tuning the protocol to explore intersectionality and reflect uponthe situations where the students might feel minoritized. Additionally, the students in the futurestudy will be purposefully selected to examine
components wereapproved by the UW Institutional Review Board.Here we focus on institutional data and student outcomes from the first four years of the programfor computer science and engineering students or pre-majors. A more in-depth analysis of surveyand interview responses and outcomes for students who are pursuing all STEM majors in theprogram will be published elsewhere.Program OutcomesStudents in the ACCESS in STEM program generally reflect the diverse composition of thestudent body at UWT, with 74% First Gen, 31% URMs, and 11% veterans or military dependents(Table 2). Although female and non-binary students are still underrepresented compared to theoverall campus population, at 27% they show much greater representation than the
situation to do a recommendation. I think if I was just being like, "Wow, you can go do it. This is a reasonable time." I think I'd be 13 seconds.Participant 2.7’s analysis of the Cup Stacking question forced us to reflect on the task’s value asa means to measure normative behavior: While the other questions have fairly unambiguousconsequences since they affect the decision-maker individual alone, the Cup Stacking questionasks one to consider how a different person will react. Since it is not reasonable to expect that allpersons will react in the same way to an identical response, our inequality-based coding schemeis not appropriate for this question.DiscussionThis project seeks to understand how engineers reason under variability: cases
. Participating faculty first attenda workshop to learn what elements of the classroom process they should focus on and how toprovide helpful observations to their colleagues. They meet in their groups after the classroomobservations have been made to debrief one another and then submit a reflection paper on theirexperiences to the project leadership team. The goal of the program is for faculty to be able toobserve and be observed by colleagues in a non-evaluative environment.The Peer Observation Program has been run three times (spring semesters of 2018, 2019 and2020) with the number of faculty participants at 11, 13 and 15, respectively.(c) SOS (Steering Online Success) STEMDuring spring 2020 the university, like most, transitioned to fully online
multidisciplinary use. We hope that the analysis and reflections on our initial offeringshas improved our understanding of these challenges, and how we may address them whendesigning future data science teaching modules. These are the first steps in a design-basedapproach to developing data science modules that may be offered across multiple courses.1. Introduction As technology advances, familiarity and expertise in data-driven analysis is becoming anecessity for jobs across many disciplines. Data science is an emerging field that encompasses alarge array of topics including data collection, data preprocessing, data quality, data visualization,and data analysis using statistical and machine learning methods. A recent National Academy ofSciences
and flagged to generate a listing of internally consistent, discretecategories (open coding), followed by fractured and reassembled (axial coding) of categories bymaking connections between categories and subcategories to reflect emerging themes andpatterns. Categories were integrated to form grounded theory (selective coding), to clarifyconcepts and to allow for interview interpretations, conclusions and taxonomy development.Frequency distribution of the coded and categorized data were obtained using a computerizedqualitative analytical tool, Hyperrresearch® version 3.5.2. The intent of this intensive qualitativeanalysis was to identify patterns, make comparisons, and contrast one transcript of data withanother during our taxonomy and CPPI
Workshop. Parallel tracks continuedthroughout the day. Members of the Program Committee who served as the Track Chairs alsodesignated two breakout sessions from each track so that elements of the White Paper receivedsufficient time to be emphasized. The day ended with a tour of new active learning spaceinfrastructures and facilities that could support various aspects of DMTL. Tuesday’s sessionsbegan with a keynote address followed by a track debrief by each track chair to the entireworkshop. The workshop breakout sessions commenced after a Reflection Debrief havingemphasis on trends and progress made and areas to focus the remaining time to maximize theparticipants work together. After parallel tracks concluded, there was the formation of
data. Ideas or phenomena were first identified and flagged to generate alisting of internally consistent, discrete categories (open coding), followed by fractured andreassembled (axial coding) of categories by making connections between categories andsubcategories to reflect emerging themes and patterns. Categories were integrated to formgrounded theory (selective coding), to clarify concepts and to allow for interview interpretations,conclusions and taxonomy development. Frequency distribution of the coded and categorizeddata were obtained using a computerized qualitative analytical tool, Hyperrresearch® version3.5.2. The intent of this intensive qualitative analysis was to identify patterns, make comparisons,and contrast one transcript of
reflect emerging themes and patterns. Categories wereintegrated to form grounded theory (selective coding), to clarify concepts and to allow forinterview interpretations, conclusions and taxonomy development. Frequency distribution of thecoded and categorized data were obtained using a computerized qualitative analytical tool,Hyperrresearch® version 3.5.2. The intent of this intensive qualitative analysis was to identifypatterns, make comparisons, and contrast one transcript of data with another during ourtaxonomy and CPPI refinement.First Year Study Findings and Discussion To our knowledge, there is no coherent (mutually exclusive and collectively exhaustive)taxonomy of pedagogical practices that may contribute to student success in
teachingexperience, including use of writing in courses, 3) evaluation of quality of the writing activities,4) reflections on the instructional experience, and 5) impressions of the student experience.Student Writing Assessment: To perform controlled tests on the efficacy of our exercises, wewill divide the large classes into two groups, determined according to course section, one ofwhich will receive the exercises and one that will not. The assessments will include pre- andpost- tests of student writing abilities, such as writing a paragraph to explain a graph. However,the specific assessment will clearly target the element of writing that we believe the implementedwriting exercises address (e.g. organization, paragraph composition, etc.).Student Technical
emphasis on increasing the proportion of engineering majors, theToys’n MORE project seeks to increase the number of students in STEM majors at thePennsylvania State University by as much as 10 percent. Please note that any opinions, findings,and conclusions or recommendations expressed in this material are those of the authors and donot necessarily reflect the views of the National Science Foundation.This project is being conducted by the College of Engineering at Penn State through an NSF-sponsored Science, Technology, Engineering, and Mathematics Talent Expansion Program grant(STEP grant, DUE #0756992). The project involves the College of Engineering and 13 regionalcampuses in the Penn State system. These campuses offer 2-year degrees, 4-year