occurred in spring andfall of 2022. During these conversations, administrators were asked to reflect on theimplementation of the e4usa program at their school, their personal experiences with thisprocess, and barriers or suggestions in expanding this program both locally and more broadly.The transcripts of these interviews and focus groups were analyzed using descriptive coding [1]by two researchers. During this process the codes were categorized and then emergent themeswere identified. The findings indicate that administrators have a range of personal experiencewith implementing this engineering program, and that often these experiences were reported as abenefit to the entire school. For instance, administrators often referred to connections made
growth in adaptiveness as students progress through their degree program.The first two results of this study [18] are somewhat consistent with those of the previous study [17]. Thediscrepancies stated above may be attributed to the smaller sample size in the second study and will beinvestigated further in subsequent work. It should also be noted that an interview protocol was developedand interviews conducted with low-income students as part of [18]. Preliminary analysis of theseinterviews revealed that different majors at Stevens provide different metacognitive opportunities forstudents within that particular program. Particular reference was made to programming and designactivities that inherently required self-reflection at various points in
can learnfrom that” [Student 23] and another, ”Really nice intro course to data science, made taking theBusiness Intelligence class alongside it more manageable.” [Student 9]. This indicates that thequality of the support for hands-on exercises impacts student learning and interest in DataScience.AcknowledgementThis material is based upon work supported by the National Science Foundation under AwardIUSE 2021287. 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 thank Dr. Kimberly Fluet for her contribution in designing the surveyquestions and collecting/analyzing the survey data. The authors also
cohorts of graduate students who study in the U.S. institutionsof higher education. The section below outlines typical communicational difficulties occurredbetween culturally diverse student cohorts on the U.S. campuses.3.2. Communication Challenges Between American and International Students while atSchoolMatsuda & Silva [6] pointed that International students had faced anxiety and challenges whilebeing at an American Institutions. Often, their unwillingness to communicate with domesticEnglish-native speaker peers indicates various fears. For instance, “One of the students who theyhave wrote about is Park, a student from Korea. Park in his reflective commentary had writtenabout how depressed he was about getting a good grade and how he
game, including1) Network Strength (measured by number of improvements), 2) Inequity of Improvements(measured by the maximum difference in improvements for different neighborhoods), 3)Inequity of Restoration (measured by the maximum difference in the number of non-operationalcomponents for neighborhoods), 4) System Functionality (measured by the total number ofoperational components), and 5) Community Resilience (measured by the area under recoverycurve). Teams consider all five of these objectives as they make infrastructure decisions whichare considered in final game scoring. At the end of the game each team community’sperformance is compared among the other teams based on the scoring system reflecting the fiveobjectives. The exact scoring
survey were operationalized so that respondents had a shared understanding of what wasbeing asked. The online survey and Institutional Review Board-approved protocols for issuingthe survey were designed to preserve anonymity so that respondents could answer morecandidly. While termed a “Lightning Poll” to reflect a practicable survey design for busy deansand department chairs, the survey was more robust than that title indicates. The survey,conducted in Qualtrics XM (Qualtrics, Seattle, Washington), was issued in September 2022 andconcluded in October 2022. The survey and a de-identified data sample are available uponrequest to the corresponding author.Survey ResultsResponse DemographicsOf the deans and chairs that responded to the survey, 73 of
long-term goals are. Students then re-assessed whether the job they envisioned alignswith what they learned from their informational interview. The final piece of the assignment wasfor students to reflect: Who might be best served by working in this job? What is the futureprognosis of this job, especially in light of climate change? And would this be a job that youwould actually want? The assignment culminated with short (less than 10 minute), in classpresentations where other groups were able to ask questions. I assessed the assignment by quantifying students’ perception of it in four categories. Ialso collected qualitative data by asking students open-ended questions about their experience. In this report, I share students
chemical engineer before, and mentorvideos and interactions helped them meeting with professional chemical engineers and seeingtheir future in them.Future WorkWe had collected both qualitative and quantitative data during three semesters ofimplementation. All data was cleaned, organized, coded individually and as a group. This data iscurrently being analyzed.AcknowledgmentsThis work was supported through the National Science Foundation’s funding under a PFE: RIEFGrant No. (2024960). Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the National ScienceFoundation’s views. We wish to thank survey and interview participants for their participation inthe
, or work presented herein was funded in part by the U.S. Department ofEnergy’s Industrial Assessment Centers, under Award Number DE-EE0009734. The views andopinions of authors expressed herein do not necessarily state or reflect those of the United StatesGovernment or any agency thereof.References[1] S. Truitt, J. Elsworth, J. Williams, D. Keyser, A. Moe, J. Sullivan and K. Wu, "State-Level Employment Projections for Four Clean Energy Technologies in 2025 and 2030," 2022.[2] DOE’s IAC, “Industrial Assessment Centers”. Available: https://iac.university/#overview [Accessed Feb. 12, 2023].[3] C. Kurnik and C. Woodley, "NREL job task analysis: Energy auditor," 2011.[4] M. M. Mohamad, A. R. Jamali, M. I. Mukhtar, L. C. Sern and A
groupdiscussion to reflect on the visit. Before the visit, the group was largely unaware of the high-techSTEM careers that existed “behind the scenes” of the heavy manufacturing setting, andmentioned looking forward to sharing the experience with their students.Figure 3. Teachers concluded the summer by presenting their research outcomes, lesson plans,and discussing plans for implementing their research experiences into their classrooms during theacademic school year.Teachers concluded the 6-week summer research experience with a final presentation of theirresearch results, reviewing the lesson plans they had developed, and discussing follow-up plansfor the academic year (Figure 3).Future WorkAt time of writing, the second cohort of teachers are
, technology policy, and law through the eyes of policymakers.Students work on public-facing projects in interdisciplinary teams applying strategic technologypolicy, regulatory concepts, and systems thinking to realworld policy issues to assist relevantpolicymakers in their policy decision-making process.Through the application of engineering systems principles (Figure 1), the use of systems design,and an understanding of sociotechnical systems, students in the MELP program will acquire theknowledge necessary for the understanding of policy and law as a system and how law, policy,and technology converge. Students will also develop skills for the analysis of complex systemsproblems, characterized by multi-stakeholder engagements reflecting the
to the number oflesson plans (out of nine) that reflected the specific data analytics topic. It can be seen that datacollection, data visualization, and spreadsheet modeling are the common topics chosen, and theyare well-connected to industrial engineering curriculum. It is not a surprise that the relatedcollege courses are mainly in statistics, basic modeling and computing classes, and advancedclasses related to data analytics.Conclusion and Future WorkIn this paper, we presented a descriptive statistics analysis of the learning modules created by theparticipating teachers through the AR-DATA program. We summarized the standards theteachers have used for their lesson plans as well as the common ideas and topics of the learningmodules. It
IPA focused on the individual experience, which analyzed the discursive process ofpositioning (e.g., conversations and storylines) to understand participants’ multiple roles (seeFigure 3) [22]. Semi-structured interviews were carried out independently in person or viaonline meetings, which ranged in length from 60 to 120 minutes. We began the interview byproviding ample time for developing rapport with each participant and then gradually shiftedthe conversation to asking questions about their background and identity meaning-making.Identity meaning-making referred to questions that ask participants to be reflective abouttheir multiple roles (e.g., teacher, researcher, and family role) around their rights and duties,which was fundamental to the
all.AcknowledgementsThis material is based upon work supported by the National Science Foundation under GrantNumbers 1726306, 1725423, 1725659, 1726047, and 1725785. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily reflect the views of the National Science Foundation. We would also like toacknowledge the collaborating faculty and students on the project, Dr. Julie Linsey, Dr. TracyHammond, Matthew Runyon, Dr. Vimal Viswanathan, and Dr. Ben Caldwell, for their assistancewith data collection and the development of the software.References[1] E. Odekirk-Hash and J. L. Zachary, “Automated Feedback on Programs Means Students Need Less Help From Teachers,” in ACM SIGCSE
, the purpose of this poster paper is to identify the obstacles that have shaped,at times tacitly, our MCC-UMKC engineering transfer partnership. As Black and Gregersen(2002) noted, the first step toward implementing organizational change is to be able to see a needfor change. When we initiated our KCURE program in 2020, we didn’t see a need for change.This study provided us time to pause and reflect on what we did not earlier see. In Figure 1, wedetail the MCC-UMKC engineering transfer pathway obstacles that indicate the need for change.Figure 1: MCC-UMKC Engineering Transfer Pathway Obstacles Finances MCC Transfer UMKC Uncertainty
-timeFinally, students were queried on their experience and reflections on working within a team toadvance a grand challenge and how the construction of the team affected their experience on theproject. Relevant responses along with percentages are summarized below: 1. Do you think you learned/understood more about the project by working within such a team vs. working alone? Yes, learned/understood more by working within a team (87.5%) No (0%) Maybe (12.5%) 2. How did the multi-disciplinary (4 engineering department) construction of your team affect the research project performance? Positively (87.5%) Negatively (0%) Neutral (12.5%) 3. How did the multi-level (sophomore to senior
these participants. We will also enhanceour recruiting strategies and assess what prevents students from volunteering. We will continueto expand our data size and we will continue to collaborate with more local community partnersand student organizations within Wright College to organize volunteering opportunities. Withmore activities and larger data size, we will compare the impact of all terms on the STEMidentity and STEM efficacy of volunteers.V. ACKNOWLEDGEMENTSThis material is based upon work supported by the National Science Foundation under Grant No.DUE-1832553. Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience
their ongoing support of the projectand work in conducting the interviews that provided the data for this paper.This material is based upon work supported by the National Science Foundation under grantnumbers DUE #1834425, 1834417 and 2022412. Any opinions, findings, and conclusions orrecommendations expressed are those of the authors and do not necessarily reflect the views ofthe NSF.References[1] E. Davishahl, T. Haskell and L. Singleton, "Engaging STEM Learners with Hands-on Models to Build Representational Competence," in 127th ASEE Annual Conference and Exposition, Virtual Online, 2020.[2] L. Singleton, E. Davishahl and T. Haskell, "Getting Your Hands Dirty in Integral Calculus," in 127th ASEE Annual Conference and Exposition
fieldof SciTS, including the five domains of team science competencies [4]: 1) building genuinerelationships, 2) team communication, 3) managing team research, 4) collaborative problem-solving and creativity, and 5) leadership.Some of the key topics covered across the workshops included: a) expanding our ability toparticipate in a shared vision, b) understanding the importance of diversity and practicing usingtools for inclusive teamwork, c) enhancing our awareness of developing shared language, d)exploring and practicing collaborative writing, e) drafting team charters, and f) developingguidelines for decision making.We gathered several key takeaways from our workshop reflections: • Being mindful of the value of team members when they are
moreeffective than PSpice at analyzing circuit behavior and understanding circuit operation. Forquestion 2, 80% of students believed that Simulink/Simscape was easier to use than PSpice (60%recorded it as much easier to use.) Results for question 3 show that all students believed thatSimulink/Simscape was more easily accessible that PSpice (80% believed it was much easier toaccess.) Across all three categories, at least 80% of students indicated that Simulink/Simscapewas better or much better than PSpice in terms of effectiveness in analyzing and understandingcircuits, ease of use, and ease of access. Written student comments reflect the same sentiments;a few of the student comments are listed below. • Simscape was much more user friendly. The
localarea during the pandemic. Past reflections on the designs from year 1 and year 2 noted the largesize of each final design. As the goal was to make a hand washing station that was portable, theteam was required to modify previous designs so they could fit in the towing trailer used by theTranSCEnD team. Figure 3: TranSCEnD Cohort 3Year 4For the year 4 bridge project, TranSCEnD students were presented with the problem ofdeveloping a way for members of a remote village in Panama to pump water from the middle ofthe river that serves the village. Members of the cohort modified the design of a current seniordesign team in our Civil and Environmental Engineering Department to build a floating dockoutfitted with a pump
. Two design-build projects, one individual and one team-based, allow multiple “trips” through the process, with chances to reflect on and discuss thepresented design process. Process content is supported by skills development in spatialvisualisation, CAD and technical drawing, and basic analysis techniques.Background of staffing, space allocation, material costs, and students are described to providecontext; the course aims and methods are described; student feedback is summarized; and plansfor evaluation and further development are outlined.IntroductionIn their review of project-based learning (PBL) in engineering education literature, Chen et. al.highlight the challenge of increased time and effort required by students and teachers to
of Science has been asked to complete this for their faculty. Therefore, their responseswill only reflect the faculty from that College. The study team have deliberately not asked forgreater refinement than College/School level data so as to avoid de-anonymizing data via samplesize. This instrument was socialized with the relevant College-level staff and administrative facultyprior to finalization, as described in the next section.Figure 1: IRB-approved data acquisition instrument for assessing startup equity.Stakeholder EngagementAs described in the prior section, success of this effort relies heavily upon numerous staff membersand administrative faculty providing detailed data. To that end, the assessment instrument wassocialized amongst
(2021) introduced the concept of person-centered approaches to the engineeringeducation community, which originated in the context of longitudinal analyses. A person-centered approach recognizes heterogeneity and attempts to identify latent groupings amongindividuals in the sample based on the relationships among variables which reflect thecharacteristics of individuals and their environment. In contrast, a variable-centered approach isfocused on prediction and relationships between variables (Laursen & Hoff, 2006). Althoughperson-centered approaches may use data-driven methods to fulfill these tasks, not all data-driven methods can be used in a person-centered fashion without more critical thought (Godwinet al., 2021). For example
internationally trained minoritized women.Our study will expand the ongoing conversation into the Canadian landscape.Theoretical PerspectivesOur study adapted Carlson and team’s [1] conceptual model of professional identity developmentwhich include: 1) Program Expectations; 2) Teaching and Supervision; 3) Research; 4)Publication; 5) Grants and Funding; 6) Service; and 7) Conferences, Networking, and ProfessionalDevelopment. We chose this model because it was suited for examining the professional identitydevelopment of doctoral programs, was extendable to include ECR and allowed specific elementsof the model to be woven into our interview questions and narratives. We choose duoethnography[18] because of its collaborative, reflective, dialogic, and
the National Science Foundation under grant EEC#1929727. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author and do not necessarily reflect the views of the National Science Foundation.References[1] W.C. Johnson and R.C. Jones, “Declining Interest in Engineering Studies at a Time ofIncreased Business Need.”http://www.worldexpertise.com/Declining_Interest_in_Engineering_Studies_at_a_Time_of_Increased_Business_Needs.htm (accessed Jan. 20, 2023).[2] U.S. Bureau of Labor Statistics, “Civil Engineers.” https://www.bls.gov/ooh/architecture-and-engineering/civil-engineers.htm (accessed Jan. 20, 2023).[3] Data USA, “Civil Engineering”. https://datausa.io/profile/cip/civil-engineering (accessed
in a small box using an Arduino and MATLABSimulink was successfully designed and implemented, showing good servo and regulatorresponse. Multiple box dimensions and LEDs were tested. As expected, increased distancesbetween lights and photoresistor, reflective or absorbing internal surfaces, or increased box spacereduce the measured brightness level and impact the dynamics of the process. Addressing resetwindup and sampling time issues, adding filters, and using different controller types could allowadditional control exercises to implement in a control course.We look forward to using this kit in the classroom and assessing student perceptions andlearning. We also hope that the current study may help spark new ideas and provide
learning to adjust their designs and recut throughout their lab time.Figure 3. 3D SolidWorks model of the fidget spinner exampleAfter creating the 2D sketch, students are then asked to 3D model their designs to become morefamiliar with independent use of the 3D features in SolidWorks. Students are encouraged not tosimply extrude their 2D design and instead incorporate different 3D features in an iterative design.In the design below, the feature manager tree shows how instead of extruding the whole bat-shaped design, the student extruded half of the design, and mirrored it in order to reduce the needto define many measurements and relations in the base sketch and so that any designadjustments to the main sketch immediately get reflected in the
for a CNC milled 2.4 GHz patch antenna.After the antenna is created, it should be measured and then evaluated on whether it is operablewithin the desired frequency band. Fig. 6 shows the S11 parameter, or the reflection coefficient,which indicates the antenna’s resonant frequency. In general, the lower this number is, the betterthe antenna will perform at that frequency. The S11 parameter can be measured usingcommercially available tools such as a NanoVNA. While gain and directionality are alsoimportant factors in antenna design, finding hobbyist or entry-level devices to properly measurethese aspects is difficult.C. Chemical Etching Figure 7. Left, a probe-fed patch design printed on vinyl backing using a laser jet printer. Right, the
-structured interview protocol with four sections:introduction and warm-up, engineering identity, teamwork, and conclusion. When timepermitted, we asked the interviewees to reflect on the stories of the practicing engineers. Thesestories were developed from publicly-available accounts of the day-to-day experiences ofpracticing engineers. The interview protocol and other applicable parts of our study design wereapproved by our institution’s human subjects review process.Two mock interviews were performed to evaluate the clarity (or ambiguity) of the questions andthe total time required to perform the interview. It also served as an opportunity for our team tofamiliarize ourselves with the interview process. Two students volunteered for the