Cimino, New Jersey Institute of Technology Dr. Richard T. Cimino is a Senior Lecturer in the Otto H. York Department of Chemical and Materials Engineering at New Jersey Institute of Technology. His research interests include the intersection of engineering ethics and process safety, and broadening inclusion in engineering, with a focus on the LGBTQ+ community. ©American Society for Engineering Education, 2024 Initial validity evidence for a survey of skill and attitude development on engineering teamsAbstractThis research paper discusses an emerging project that 1) seeks to gather validity evidence for asurvey of engineering student teaming attitudes and skill
Paper ID #42415Latina Engineering Student Graduate Study Decision Processes—Developmentand Initial Results of a Mixed-Methods InvestigationDr. Bruce Frederick Carroll, University of Florida Dr. Carroll is an Associate Professor of Mechanical and Aerospace Engineering at the University of Florida. He holds an affiliate appointment in Engineering Education. His research interests include engineering identity, self-efficacy, and matriculation of Latin/a/o students to graduate school. He works with survey methods and overlaps with machine learning using quantitative methods and sequential mixed methods approaches.Dr. Janice
and Use Committee (IACUC), and grant writing and proposalpreparation. Additionally, a session on technology transfer is provided.A mid-term report is due after 4 weeks, and at the end of the program a presentation poster is tobe presented to the public, faculty, and fellow students, with a demonstration of the projectdesigned.The program has been successfully conducted in the summers of 2022 and 2023, with intentionsto proceed into summer 2024. The current year's participation data is encouraging, featuring 31undergraduate students, which constitutes 10% of the school's undergraduate body. Thedistribution across academic years includes 3 seniors, 8 juniors, 14 sophomores, and 6 freshmen.Fairfield University's summer research initiative
carewithin their state. One participant, B, is a developmental psychologist with a PhD in psychologyand experience as a preschool teacher. She dedicates 50% of her working time to this project and50% of her time to other projects in the College of Social Work. The second participant, J, is aneducational psychologist finishing a PhD in Educational psychology. He also has experience as aK-12 special education teacher. He works on this project full-time. There is an additional full-time member of the research team who did not participate in this project. While these twoparticipants are education researchers working outside engineering education, they representdisciplines which may be involved on engineering education research teams. Initially, I
outcomes, practices, and experiences of those engaged in engineering research.From my initial surveying, engineering research culture seems to be an understudied space. Thislack of inquiry is likely due to the wide array of potential directions that can be taken whenconsidering culture, and the different names studies of culture often fall under. This ambiguitycreates difficulty in “visuali[zing] the range of materials that might be available” that would berequired for engaging in a systematic review [61, p. 21], [62]. For this reason, I intend to employtwo strategies to help identify related information in this space. I plan to (1) create explicitboundaries around what aspects of research culture I am interested in understanding (which thispaper
experience andengineering identity among engineering students, particularly from diverse backgrounds, fundedby the National Science Foundation's Research Initiation in Engineering Formation (RIEF)program. This project seeks to uncover how teamwork—especially team disagreements—connects to engineering identity, employing a mixed-method approach [9]. Conducted at SanFrancisco State University, a Hispanic Serving Institution in the Western U.S., our studycollected quantitative data through surveys and qualitative data through interviews with studentsfrom over 20 engineering classes spanning three semesters. We developed a semi-structuredinterview protocol and conducted 28 one-hour interviews with students.The qualitative analysis by our team underwent
educationalinstitutions [1], [12]. For example, in Adams and colleagues’ workshops, storytelling providedemerging engineering educators with the access to a community of practice, knowledge, andopportunities to co-construct community and faculty practices [1]. Unfortunately, this resourceremains largely untapped. Although change initiatives emphasize the significance of engagingvarious stakeholders, most approaches to change are conducted for and then imposed uponstudents, rather than supporting co-creation and student-led leadership [13]. This WIP papertakes a proactive approach to amplify the role of students in telling their own stories as part ofthe research on an institutional change project that is focusing on elevating student voices. Assuch, we use
learning. Additionally, both companies have previously participated inresearch efforts, including attending various ASEE conferences as both presenters and sponsors.The Interview Process Initial interviewees in both companies were identified as people who had previouslysuccessfully or unsuccessfully tried to conduct engineering education research. We plan toidentify other interviewees by asking participants to identify other members involved directly inthe research, or who had any decision making influence in the research. Questions asked areguided along by understanding the company’s interests and motivations for conductingengineering education research, describing their research, and understanding external andinternal barriers to
Paper ID #36928Synthesizing Indicators of Quality across Traditions of NarrativeResearch MethodsMr. Kanembe Shanachilubwa, Pennsylvania State University Fourth-year doctoral student at Pennsylvania State University in the mechanical engineering department. Member of the Engineering Cognitive Research Laboratory (ECRL). Current research topics include grad- uate school attrition and persistence.Catherine G. P. Berdanier, Pennsylvania State University Catherine G.P. Berdanier is an Assistant Professor in the Department of Mechanical Engineering at Penn- sylvania State University. She earned her B.S. in Chemistry from The
work was presented atthe American Society of Engineering Education, Zone 1 Conference at Penn State College ofEngineering on March 30-April 1, 2023.References[1] Martínez, A. and Gayfield, A. (February 2019). The Intersectionality of Sex, Race, andHispanic Origin in the STEM Workforce. SEHSD Working Paper Number 2018-27: Social,Economic, and Housing Statistics Division, US Census Bureau. Retrieved from:https://www.census.gov/content/dam/Census/library/working-papers/2019/demo/sehsd-wp2018-27.pdf.[2] The White House Initiative on Educational Excellence for Hispanics (2010). Hispanics andSTEM Education. Retrieved from: https://www2.ed.gov/about/inits/list/hispanic-initiative/stem-factsheet.pdf[3] Hispanic Heritage Foundation, Student Research
observations of a single research team’s (Team Y) weekly meetings. More specifically, wefocus on a single critical interaction in which the team is making a research decision. Thisinteraction occurred across two sequential team meetings, referred to as Day 1 and Day 2.ParticipantsTeam Y is working on a multi-year, nationally funded research project. The team members arelocated at multiple institutions across the United States. The team includes four faculty (Dr.Peters, Dr. Wilson, Dr. Johnson, and Dr. Roberts) three undergraduate student researchers(Riley, Avery, and Alex), and one graduate student researcher (Eliana). The faculty are the‘permanent’ group members and wrote the initial grant that funds their current work. The threeundergraduate
to enquire if Ihad interest in an undergraduate research experience. He had recently obtained a grant toperform work in Additive Manufacturing and was looking for two undergraduate researchassistants. I was informed about the scope of the study and the research questions it sought tofind an answer to, and that it will commence at the start of the Fall 2023 semester. I learned thatthe study would involve looking into how different carbon fiber (CF) parameters such aschopped length, concentration, and geometry when mixed with a certain base polymer resin canbe customized to optimize the strength properties of the 3D prints.After being heavily involved in the initial work, I feel that this is the first time I have everundertaken research to
comfortable with engineering(98%). Furthermore, those that initially thought engineering could not improve everyday thingsfor people (44%) felt engineering could afterward (87%). While those that believed (pre) fixingthings was not something they were good at (38%) later felt it was something they could achieve(92%).IntroductionEngineering education has gained prominence in STEM education, with the integration ofengineering practices in the Next Generation Science Standards for K-12 education signifyingthe importance of engineering in pre-college education. Research suggests that integration ofengineering in STEM can improve students’ learning in science, mathematics, and technologicalliteracy as well as stimulate students’ interest in pursuing
reminisce about them with the aid of instructor facilitation.Students also have the opportunity to explore multiple perspectives on various kinds of lifeevents by listening to other students’ stories, which is a form of joint reminiscence (Wang et al.,2017) among students and between students and instructors.Study Background Our research is part of a larger initiative that promotes story-driven learning (i.e., usingpersonal stories to drive student learning) as a novel pedagogical approach in biomedicalengineering education. This initiative seeks to improve undergraduate students’ entrepreneurialmindset. Through this research, we aimed to answer the following three research questions: (a)what pedagogical practices are identified when
inquiryattends to the ways in which storytelling “commonplaces” of temporality, sociality, and placecontextualize and make sense of lived experiences [32], [33]. In other words, narrative is the unitof analysis, and it is itself an analysis. The initial stories we invite students to share function as away of creating a shared understanding, or epistemic space [35], between the researcher andparticipant and as a way of contextualizing our adaptation of the appreciative interview prompt.After learning about students’ journeys into engineering and noting with them strengths andsuccesses along the way (in accordance with APPI’s positivity principle), the researcher thenmoves to the first part of the appreciative interview heuristic, inviting the participant
) have emerged as a pivotal component in highereducation, significantly benefiting students, faculty, and universities alike [1], [2], [3]. Theseprograms offer a unique combination of hands-on research experience and mentorship,enhancing students' learning and career trajectories [4]. A notable initiative in this regard is theResearch Experiences for Undergraduates (REU) funded by the National Science Foundation(NSF) [5] which highlights the importance of such programs in science, engineering, andmathematics [6]. These REUs consist of ten or so undergraduates who work in the researchprograms of the REU host universities across the United States [7].One of the primary benefits of URPs is their influence on students' decisions to pursue
of participating in an NSF-funded ERC for all who engage in the center. The instrument was designed using multiplerounds of design iterations and pilot tests.MethodsMERCII SurveyThe MERCII survey instrument is a web-based survey created by TEEC. Zhen et. al. [3]described the process of designing the instrument and initial validity steps taken. The MERCIIsurvey instrument aims to investigate categories set forth by NSF guidelines to evaluate theeffectiveness of a center [3]. The constructs used in the analysis were drawn from categorieshighlighted by the NSF Guidelines. The survey consists of eight sections: 1) research centeraffiliation (2 items), 2) understanding of the research center (5 items), 3) impact on skills (24items), 4) culture of
asa model to design new prototypes, which reflects the pertinence of DT in education.Nonetheless, in the Asian context, there is little research concerning applying DT inengineering education.The EDIPT modelSome popular DT models are Simon's three-stage linear, IDEO, and EDIPT models. Initially,DT courses in engineering and design were based on Simon's three-stage linear model,including analysis, synthesis, and evaluation [9]. IDEO expanded the DT model through aniterative Discover, Interpretation, Ideation, Experimentation, and Evaluation cycle toinnovate design work [41]. Stanford University's Educational Design Lab further integratedDT into curriculum and teaching practices by proposing the EDIPT model, currently the mostwidely adopted in
. The lab manual would includecomponents like grading policy, teamwork expectations, communication methods, and policy,emphasis on initiative, professionalism, quality work, and positive attitude, domain knowledgerequirements, technology needs and tutorials, the relationship between graduate andundergraduate researchers, and expectations around progress check-ins. Furthermore, Weldonand Reyna [5] recommended assigning concrete laboratory tasks to students – the more specific,the better. Examples of concrete educational tasks include running specific data analyses (e.g.,run a correlation between sensation-seeking score and proportion of risky choices) or finding acertain number of recent articles on a certain topic (e.g., create a spreadsheet
literature to better represent the magnitude of eachvariable. In both the Python and NetLogo models, we utilized the “random” function to maximizethe variability of each student-agent’s experience. This means that students’ interactions with eachvariable was randomly assigned instead of us as researchers assigning experiences to each agent.Randomizing the variables allows researchers to easily customize the number of agents and createsmore opportunities for a variety of experiences to be represented. The model initiates with each student-agent beginning their first academic year with arandomly prescribed motivation value between 10 and 25 that indicates their desire to go tograduate school. This is used to represent observed varying levels of
Table 4. Inthe first example, the authors structured their analysis around the theoretical framework, ensuringalignment with the study's conceptual foundations. Due to the multiple participants in the study,the authors run into a substantial volume of interview transcripts. To navigate this data, theyemployed qualitative analysis using holistic coding techniques. Their approach was meticulous,documenting analysis and interpretation procedures, including the development of memos and astatement addressing researcher bias. Specifically, their focus on cross-case analysis allowedthem to derive conclusions that transcended individual cases.In the second example, the authors initially conducted within-case analysis, thoroughly exploringindividual
University (ASU). She is a qualitative researcher who primarily uses narrative research methods and is interested more broadly in inDr. Samantha Ruth Brunhaver, Arizona State University Samantha Brunhaver, Ph.D., is an Assistant Professor within The Polytechnic School of the Ira A. Fulton Schools of Engineering at Arizona State University. Her primary areas of research include engineering ca- reer pathways and decision-making, undergraduate student persistence, professional engineering practice, and faculty mentorship. Brunhaver graduated with her B.S. in mechanical engineering from Northeastern University and her M.S. and Ph.D. in mechanical engineering from Stanford University. ©American Society
from research studies, they also express it increative hobbies which ranged from drawing and fashion design to creative writing and fiberarts. However, despite engaging in creative hobbies, Chopper, Dwight, and Felicia initially saidthat they did not consider themselves to be creative people. Dwight and Chopper shared the sameview as Felicia, who stated that they associate creative people with the arts. “As far as when I describe people as creative, I am not like those people… it's often someone that is very into the arts… My first instinct is to link creativity with artistic, but that’s not necessarily the case all the time, but that's where my mind immediately goes to.” — FeliciaHowever, after a series of follow-up
undergraduate degree in civil engineering. IZ was in their junior year and hadextensive prior experience doing paid work as a transcriber. IZ carried out observations andfieldwork over a period of ten weeks during the summer of 2021. On average, they spent aboutten hours per week collecting data, writing up field notes, and updating the research team ontheir progress. They also did nine follow-up interviews with technical and managerial staff at thecompany during Fall 2021, after their observation period concluded. The data collection goalscommunicated to IZ were broadly concerned with studying the nature of engineering practiceand the day-to-day experiences of engineers.A second PI initially looked for opportunities to collect data in large
methodological development. ©American Society for Engineering Education, 2023 A Qualitative Methods Primer: A Resource to Assist Engineering Education Scholars in Mentoring Traditionally-Trained Engineering Faculty to Educational ResearchAbstract This research methods full paper presents a primer on qualitative analysis methodsintended to be a resource for experienced qualitative engineering education researchers tocommunicate the basics of qualitative research methods to traditionally-trained technicalengineering faculty embarking on educational research initiatives. The recognition and growth ofengineering education has drawn new
material are those of the authors and do not necessarilyreflect the views of the National Science Foundation. The authors thank the surveyparticipants for their insights and contributions to our research. The authors thank Study 1advisory board member Dr. Alison Godwin, who provided an example draft of a CIprotocol which we closely followed in developing the protocol for our initial Study 1cognitive interviews.References[1] P. C. Beatty and G. B. Willis, “Research synthesis: The practice of cognitive interviewing,” Public Opinion Quarterly, vol. 71, no. 2, pp. 287-311, 2007.[2] R. Tourangeau, L. J. Rips, and K. Rasinski, “The psychology of survey response,” Cambridge University Press, Cambridge, UK, 2000.[3] T. Li, E
all three authors, andwhen their codes were compared, an initial agreement of 97% was obtained. Minormodifications to the coding scheme were made, and following discussion, all discrepancies incoding were reconciled and 100% agreement was reached. The remaining years 2012-2021 werethen coded by one coder, with each author coding three or four years of JEE articles. The first level of coding examined the general method used; quantitative, qualitative, ormixed methods. In this review, the general method categorization used the following definitionsto code the articles. Quantitative research was defined as a set of experimental designs, methods,statistics, data analysis, and modeling that aims to represent observed outcomes
the keywords should appear in the titles or abstracts once selected[15]. Our initial queries employed search terms from "AB Abstract" in EBSCO, yielding 3,889articles. We then narrowed down these articles using four filters: scholarly (peer-reviewed)journal, full-text available, a date range of 1993 to the present, and English. Applying thesefilters resulted in 1,843 articles sorted by relevance. Two independent authors screened theliterature for cross-checking. After removing duplicate research studies, we reviewed the titlesand abstracts of these articles using the search criteria mentioned above. If the articles' relevancecould not be determined from the title and abstract, we read the full articles for furtherevaluation. Disagreements
. Thishelps to narrow the focus of the review and ensure that only relevant articles are included. Next,keywords related to the research question are used to do a full search of relevant databases likeScopus, Science Direct, PubMed, Web of Science, and Google Scholar. The resulting articles arescreened for duplicity and eligibility based on the inclusion criteria. The selected articles are thencritically evaluated for their quality and relevance to the research questions. Finally, a synthesisof the findings from the eligible articles is conducted, and the results are organized and presentedin a clear and concise manner.In this study, the initial literature review examined a selection of studies based on specificcriteria. These criteria included: (a
Paper ID #38645Death by 1000 cuts: Workshopping from Black engineering narratives frominterview to stageDr. Debalina Maitra, Arizona State University, Polytechnic Campus Debalina Maitra is a Post-doctoral Research Associate at ASU. Prior to her current role, Debalina Maitra was employed by CAFECS (Chicago Alliance for Equity in Computer Science), a NSF-funded Research Practice Partnership, for almost two years. She complDr. Brooke Charae Coley, Massachusetts Institute of Technology Brooke Coley, PhD is an Assistant Professor in Engineering at the Polytechnic School of the Ira A. Fulton Schools of Engineering at Arizona