-0.08 No SIV 66 4.30 1.05 SIV 111 4.22 1.57 A- 0.23 No SIV 120 3.99 1.60 SIV 22 3.64 2.01 B+ -0.32 No SIV 28 3.96 1.71 SIV 25 3.36 1.63 B 0.00 1.118 0.351 No SIV 22 3.36 1.40 SIV 7 3.00 0.00 B
students who want to pursue careers in academia.References[1] J. F. Kelly Kang, “Research Doctorate recipients with definite postgraduation commitments, by major field of doctorate,” NSF, https://ncses.nsf.gov/pubs/nsf24300/data-tables (accessed Feb. 2, 2024).[2] I. Mena, H. Diefes-Dux, and B. Capobianco, “Doctoral students as course instructors: Three engineering teaching assistants’ socialization experiences,” 2011 ASEE Annual Conference & Exposition Proceedings, 2011. doi:10.18260/1-2--17790[3] C. J. Finelli and J. E. Froyd, “Improving Student Learning in Undergraduate Engineering Education by Improving Teaching and Assessment,” Advances in Engineering Education, 2019.[4] R. R. Baiduc, R. A. Linsenmeier
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several categories, which were subsequently analyzed and labeled as thematic learning processes [25] (see Tables 1 and 2 in the Appendices).• Developing memos: We created memos to further analyze and describe the themes and begin answering the research questions of what the participants learned and how they learned. These memos were the basis for the presentation of the findings in the next section.Findings 1: Student Learning Experiences in the Educational EcosystemFrom the analysis of the students’ interviews, we identified five main themes characterizing thelearning processes in their university engineering studies: (a) Learning the science andapplication of engineering; (b) Learning beyond engineering; (c) Learning the
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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
sentiments of the tweets related toEE (n=3,605) with the most frequent topics. The data in Figure 2a) represents the racialdistribution in tweets, and Figure 2b) represents the tweets' originating location map. Figure2a) demonstrates that White individuals accounted for the majority (88.3%) of these tweets.Tweets from different racial categories were as follows: Asian (5.9%), Hispanic (5.3%),Black (0.3%), and American Indian (0.2%). It portrays that tweets in social media discussingEE have a very high racial disproportion.Figure 2: a) percentage of tweets by race and b) tweet location map with average sentiment (n=3,605)*Please note in the tweet map the data points show the tweet originating location and if therewere many tweets from same location
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, 2022.[4] H. S. Alim, D. Paris, and C. P. Wong, "Culturally sustaining pedagogy: A critical framework for centering communities," in Handbook of the Cultural Foundations of Learning, Routledge, 2020, pp. 261-276.[5] L. M. Vaughn and F. Jacquez, "Participatory Research Methods – Choice Points in the Research Process," Journal of Participatory Research Methods, vol. 1, no. 1, 2020. https://doi.org/10.35844/001c.13244.[6] D. N. Champion, E. Tucker-Raymond, A. Millner, B. Gravel, C. G. Wright, R. Likely, and T. M. Dandridge, "(Designing for) learning computational STEM and arts integration in culturally sustaining learning ecologies," Information and Learning Sciences, vol. 121, no. 9/10, pp. 785-804, 2020.[7] V. T. Lac and M
offered instructionally in Statics often results inan their inability to master the concepts in preparation for the subsequent Mechanics of Materialscourse. For example, a student quantifies external forces acting on an engineering object (Figure1.a) and uses these values to infer internal forces acting within an engineering object.Decomposed into principal components, the forces acting along the x, y, and z dimensions areformally represented using symbols, diagrams (e.g., free-body; Figure 1.b), and equations(Figure 1.c). While these formalized ways of quantifying and equating forces offer valuable toolsfor modeling and analysis, they also distance students from embodying their understandings.Progressive formalization advocates that physical
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with engineers or computer scientists over that same timeperiod; and (b) worked for US companies or subsidiaries of non-US companies operating in theUS. Individuals from academia and non-academia and from a broad range of engineeringworkplace settings (both corporate and government) completed the survey; 210 total responseswere collected for the tool development phase.InstrumentsSurvey items used in this study were either (a) taken directly from existing workplace scales; or(b) adapted from scales used in higher education. Those taken directly from existing workplacescales included satisfaction and frustration of relatedness, competence, and autonomy needs inthe SDT literature [28][29]; an occupational belongingness scale [30]; and an
M. D. Koretsky, “Toward professional practice: student learning opportunities through participation in engineering clubs,” European Journal of Engineering Education, vol. 44, no. 6, pp. 906–922, Nov. 2019, doi: 10.1080/03043797.2018.1477119.[19] E. Michor and M. Koretsky, “Students’ Approaches to Studying through a Situative Lens,” Studies in Engineering Education, vol. 1, no. 1, p. 38, Nov. 2020, doi: 10.21061/see.3.[20] S. B. Nolen and M. D. Koretsky, “WIP: An Ecosystems Metaphor for Propagation,” in ASEE Annual Conference Proceedings, Montreal, 2020.[21] L. S. Vygotsky, Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press, 1978.[22] A. N. Leontiev, Problems of
thecountry. A focus group occurred during Spring of 2023 at University A and University B and a secondfocus group at University A occurred during Fall 2023. Participants were selected from those whoresponded to an email that was sent to all public email addresses associated with project teams at the twouniversities. Students interested in participating signed consent forms that collected their contactinformation, project team name, and their role on their team. Only one student from each project teamwas invited to participate in each focus group. In the event that two students from the same project teamcompleted the form, the first student to sign up was contacted first to participate in the focus group. Atotal of 15 members of project teams
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, teaching assistants). First, we asked students about their study tendencies and whetherthey study for the class (a) alone, (b) with classmates in the course or in another section of thecourse, (c) with members of the instructional team such as the instructor of record or TAs, or (d)with other peers in the collegiate community who had taken the course in the past. For this study,the outcome of interest was dichotomous, where 1 represented those students who reported studyrelationships with other students either enrolled in the course, another section of the course, orwho had enrolled in the course in the past, while 0 represented those students who reported thatthey studied alone.Demographic Characteristics. Additionally, given existing research
critique of flexible online learning and its “anytime anyplace” claims. British Journal of Educational Technology, 50(3), 1005-1018.[10] Kuo, Y. C., Walker, A. E., Belland, B. R., Schroder, K. E., & Kuo, Y. T. (2014). A case study of integrating Interwise: Interaction, internet self-efficacy, and satisfaction in synchronous online learning environments. International Review of Research in Open and Distributed Learning, 15(1), 161-181.[11] Humiston, J. P., Marshall, S. M., Hacker, N. L., & Cantu, L. M. (2020). Intentionally creating an inclusive and welcoming climate in the online learning classroom. In Handbook of Research on Creating Meaningful Experiences in Online Courses (pp. 173-186). IGI Global.[12] Martin, F
experience. These evaluations tend toinclude both numerical (Likert scale) and open-ended written feedback, although thoroughanalyses of written feedback are rare due to the lack of methods to rigorously analyze the largeamount of content with a teaching-specific lens. In this paper, we create a comprehensive lexiconto measure eight teaching qualities from the written feedback using a combination of naturallanguage processing (NLP) and manual filtering. We refer to this lexicon as “Lexicon forEvaluation of Education Quality” (LEEQ). We then validate LEEQ by analyzing how thefrequency of words in each dimension is correlated with (a) numerical ratings and (b) otherdimensions. Finally, we compare it with other sentiment analysis tools that are less