. Snowball is another effectiveactive method used in the last 10 minutes of the web session where a student shall generate threereactions to an issue presented during the web session. Then, two students join and their task isto find three things about the topic that they agree on3. The online students studied by Stanley etal. are in favor of either active experimentation or reflective exercises. They would probablyadapt to both linear and novel approaches on course topics. Online instructors may want toincorporate factual, practical applications and examples for students, and evaluate the visualcontent in their courses4.The integrated systems and software engineering degree program at Texas Tech University wasresulted from the collaboration between
Foundry1 as the core pedagogical platformcoupled with Resources, like the Rural Reimagined Grand Challenge, Science OlympiadCollegiate Scholars, and the university’s STEM Center to offer students learning opportunitiesthat would help them to acquire skills aligned with those of holistic engineers. The program alsoleveraged the KAP and KTP as respective training sessions and research developmentrespectively in the creation of a PIT that addressed societally relevant challenges. Further, theHolistic FUEL program provided the support and structure for participants to integrate severalhigh-impact practices (HIPs) inherently reflective of the Foundry.1, 13 These includedcollaborative and active learning, faculty-student engagement, real-world
team. Initial surveyson time availability and interests would have helped to pair students who would have had a betterworking chemistry. Despite these issues, significant progress was made on the project. Allstudents prepared a final presentation of their work, gaining additional presentation practice fortheir Institution-specific assessments. We believe that earlier, reflective surveys would have beenmore useful for pairing students who shared similar interests, which might have helped move theproject closer to completion.ConclusionThis project provided an experiential learning experience to two sets of students from differentdisciplines and institutions. The project idea was to incorporate cyber security for roboticsystems. The students from
societal challenge and leveraged the Foundry as a guide as noted above.The resulting PITs did not necessarily contain specific sustainability elements from the EOPframework and were evaluated by an interdisciplinary team of judges that reflected expertise inengineering, education, and/or business through a validated rubric instrument that encompassedthese major areas. In the semester of the BioFoundry Initiative implementation, judges had anopportunity to volunteer follow-up questions or comments through an anonymous survey platformregarding the student-team PITs that could be beneficial in improving the end result. Thepreliminary results herein are representative of an analysis conducted on these evaluative datawhich provided a guideline for the
, and the 5th-12th gradestudents, as illustrated with the 4-spoked assistivetechnology collaboration wheel shown in Figure 1. We Figure 1: Assistive Technologywill provide a brief overview of the mentorship and Collaboration Wheelcollaboration approach, give an overview of the fourassistive technology teams and their projects, andprovide reflections on the Make:able projects from the 2021/22 year.The Mentorship and Collaboration ApproachBy participating in the Make:able challenge, we pursue three goals: 1. Generate excitement for engineering and technology among 5th-12th grade students 2. Provide opportunities for growth and leadership to university engineering students 3. Improve the day-to-day life of someone with a
is now built or under construction and ranges from guardrail to large, complex industrialfacilities. When the work my students prepare is on par with that of practicing engineers, I considerthis the best metric possible. Yes, some do sub-par work, but that’s life in school.How else do I know? My students tell me in their reflections, and in written reviews. This feedbackcomes from the fall of 2022, which a student submitted to the UVU’s Office of Teaching andLearning.“Paul understands that lecturing the entire class is not effective. Students need to get involved in adiscussion to actually learn something. Paul led a hybrid course where he discussed/lectured for 30-45 minutes and then let us work on our homework in class. This allowed us to
Airflow Velocity Measurements: A Project-Based Learning ExperienceAbstract: This paper describes the involvement of undergraduate students in a multidisciplinary team-basedresearch project between three engineering programs. The paper focuses on the contribution of a subgroup ofmechanical engineering students working on the airflow measurements around a single fan, triple fans, and asmall-scale wind turbine. The paper outlines the process undertaken by students to design and perform theexperiments and reflects on the challenges and lessons learned. Three different experiments were conducted tomeasure the airflow around the fans and wind turbine with the aim of defining a “No Fly Zone” for drones. Thesingle fan
conveniencefor the actors, facilitators, and students. Whether or not it returns to an on-campusimplementation in future years will be decided collaboratively by those who plan this event andthe theatre personnel who implement it. Additional considerations regarding futureimplementations of the Theatre Sketch productions are related to the time, effort, and cost ofproduction and the university and department budgetary resources.AcknowledgmentThis material is based upon work supported by the National Science Foundation. Any opinions,findings, conclusions, or recommendations expressed in this material are those of the author(s)and do not necessarily reflect the views of the National Science Foundation. The authors alsothank the Partnership for Equity
whenconstructing individualized feedback for 40+ students. Lastly, and related to the benefits of RQ2,instructors’ time may be freed up if students ask the tool questions instead of the instructor,particularly for quick, verifiable questions.One primary complication of ChatGPT being used in ENES100 is the inability of an instructor todistinguish between work done solely by a student and work done by (or with the assistance of)ChatGPT. This introduces a challenge of how to assess student work. For example, when theprompts for a reflection assignment was given to ChatGPT, it produced a narrative that wasindistinguishable from a typical student-written response (RQ1 lines 937-964). This may not beproblematic for students who are responsibly using ChatGPT to
entails, arguably one reason that first-year college engineeringstudents commonly cite math as a key area of struggle. Much like Wendy’s classic “Where’s thebeef?” catchphrase in 1984 (which implored potential customers to reconsider the quantity ofbeef in other restaurants’ burgers), educators might ask a similar question today about thequantity of math in K-12 engineering activities.Initial discussions for this study began when faculty and undergraduates from Ohio NorthernUniversity’s Math Education and Engineering Education programs collaborated on classroomactivities intended to embed math content within hands-on engineering. Upon reflection of theirown experiences, the research team (one math ed. faculty, one math ed. undergraduate
] can create barriers to mentoring access for ethnic minority students as well as become a hurdle to fostering commitment to mentoring ethnic minority students11. Deal with intergroup or diversity-based anxiety and unresolved identity and cross-cultural competence issues: Faculty mentors, especially White faculty, must seek help to deal with any element of intergroup or diversity based anxiety and the truths in mentoring [54], lack of cross-cultural competence, unresolved personal racial identity and cultural insecurity as these can create dysfunctional relationships that may negatively impact the career outcomes of minority students [55]12. Be reflective of your own experience: Be willing to reflect upon your own
the AGEP-NC Alliance can befound in [15-18].One of the areas for critical reflection within the departments is the advisor-advisee relationship.In this paper, we examine faculty perceptions of the frequency with which they provide keyadvising benefits with students’ perceptions of receiving those same benefits and compare howstudents’ perceptions differ based on underrepresentation status. We present updated findingsfrom [19], focusing on baseline surveys from engineering and computer science departments atthe three AGEP-NC universities and answer the following questions: 1. What advising practices do faculty report using with doctoral students? What advising practices do dissertation-stage doctoral students report receiving? Are
, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.8. References[1] N. Baumer and J. Frueh, “What is Neurodiversity?,” Harvard Health, 2021. [Online]. Available: https://www.health.harvard.edu/blog/what-is-neurodiversity-202111232645. [Accessed: 15-Dec-2022].[2] S. Comberousse, “A begginer’s guide to neurodiversity,” Learning Disability Today, 2019. [Online]. Available: https://www.learningdabilitytoday.co.uk/abeginners-guide-o- diversity. [Accessed: 15-Dec-2022].[3] E. V. Cole and S. W. Cawthon, “Self-disclosure decisions of university students with learning disabilities,” J. Postsecond. Educ. Disabil., vol
progress on implementation and ask questions of the project team andeach other. The check-ins served to obtain implementation data and foster a learning communityamong teachers. These informal discussions were recorded and summarized within one week ofeach discussion in order to share teacher feedback related to critical components, adaptations,and challenges with the project team. At the end of the first semester of implementation,researchers conducted semi-structured, in-person interviews, lasting 45 - 60 minutes. Theseinterviews were guided by a protocol including questions and follow-up prompts aligned to eachcritical component along with questions designed to elicit reflections on factors influencingimplementation. These interviews were
semesterthat they can still be completed incrementally towards the overall project goal. One positive isthat the competition-provided training activities strongly encourage students to explain theirassumptions and engineering judgments that were made in justifying the design. Thus, studentscan still be evaluated on these elements of their reasoning about the end product that areincorporated into the competition deliverables. In the interest of minimizing duplicated or unnecessary student work, it may also beuseful to consider the various elements of the Deliverable Packages that may have some overlapwith the preliminary and intermediate design process steps. In the case of the Development Plandocument, students are asked to reflect on the
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