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philosophical, theoretical, and methodologicalfoundations needed to ethically render trustworthy accounts of human experience. The field ofengineering education can, perhaps, benefit most from the promise of narrative research througha community approach to innovate new narrative methodologies and methods that cohere withbroader narrative research traditions while, at the same time, uniquely support inquiries ofexperience in the engineering education context.AcknowledgementsThis material is based on work supported by the National Science Foundation (NSF) under Grant2045634. All findings, opinions, conclusions, and recommendations are those of the authors anddo not necessarily reflect the views of the NSF.References[1] S. E. Chase. “Narrative inquiry
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, undergraduate research experience helpsengineering students develop communication skills.The findings further revealed that students’ reflexive positionings and identities interplayed andimpacted each other. For example, one female student recursively constructs an identity as apotential engineer when reflecting on technical work experience. That identity as a potentialengineer influenced her to position herself as an active agent who was willing to take action inorder to work in the engineering field after graduation. Just like undergraduate engineeringstudents in Schell et al.’s [12] study, the students who could identify themselves as engineerswithin internship experience influenced their future plans to consider engineering as a career.Implications
et al.’s [47], and Hess et al.’s [10]extension, scaffolded, integrated/interactive, and reflective analysis (SIRA) framework. Thisstage expects students to consider the “basic facts” of the case—establishing a grounding ofknowledge about the sociotechnical space surrounding the dilemma. In this stage, students areprompted to specify (give a rudimentary definition for) an ethical principle that they identified inthe previous stage. This formulated a more formal procedure for ethical reasoning, based onBeever and Brightman’s [48] Reflexive Principlism approach. Moreover, this procedure ofoperationalizing an ethical principle as students gather sociotechnical knowledge about a casecan set the stage for rational discourse [49].4.4. Small group
, factualconsistency, and comprehensiveness. Coherence means the capability to summarize qualitativedata input into a coherent piece of information with cohesion. Factual consistency evaluateswhether each meaning unit in the summary is backed up by the qualitative data. Importantly, wealso added whether information found in the source qualitative data is represented in thesummary. Comprehensiveness evaluates the extent to which the summary reached thecomprehensiveness of the source qualitative data [6]. We dropped “harmfulness” from Tang et al.’s evaluation scheme since the data in this project does not have the clear physiological harms inthe biomedical studies. We adopted a 5-point Likert scale with 1 being “the least satisfied” and 5being “the most
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solution Task #1 Collect data. System of differential equations that Mass balance at steady state describe the dynamics of the biological system Task #2 Plot velocity as a function of Predict what the dynamics of receptor, Energy balance at steady state stride length toxin, and antitoxin levels are over time Reflection on results from Task 2 Task #3 Plot velocity as a function of Include 1 or 2 regulatory modules for Characterizing the steady state s
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, P. Kauffmann, and M. Bosse, “Are We Missing Opportunities to Encourage Interest in STEM Fields?,” J. Technol. Educ., vol. 23, no. 1, pp. 32–46, 2011.[8] C. A. Supalo, “A Historical Perspective on the Revolution of Science Education for Students Who Are Blind or Visually Impaired in the United States,” J. Sci. Educ. Stud. Disabil., vol. 17, no. 1, pp. 53–56, 2013.[9] T. J. Ashby, W. H. Goodridge, S. E. Lopez, N. L. Shaheen, and B. J. Call, “Adaptation of the Mental Cutting Test for the Blind and Low Vision,” presented at the 2018 ASEE Zone IV Conference, Mar. 2018. Available: https://peer.asee.org/29599[10] N. L. Veurink and S. A. Sorby, “Longitudinal study of the impact of requiring training for students with
-organizing metadiscourse: tracking changes in rhetorical persuasion,” Journal of Historical Pragmatics, vol. 21, no. 1, pp. 137–164, 2020. [3] J. A. Hardy, “Undergraduate writing,” in The Routledge Handbook of Corpus Approaches to Discourse Analysis. Routledge, 2020, pp. 235–251. [4] R. S. Campbell and J. W. Pennebaker, “The secret life of pronouns: Flexibility in writing style and physical health,” Psychological science, vol. 14, no. 1, pp. 60–65, 2003. [5] Y. R. Tausczik and J. W. Pennebaker, “The psychological meaning of words: Liwc and computerized text analysis methods,” Journal of language and social psychology, vol. 29, no. 1, pp. 24–54, 2010. [6] J. Barell, Teaching for Thoughtfulness: Classroom Strategies To Enhance
/ SEP760 course. We have reimagined a student learning experience and would like to get your honest opinions. FACILITATORS PRESENT THE PROTOTYPE(S) AND OBSERVE INITIAL RESPONSE/REACTION. • Is there anything that surprises you? If yes, what? • Is there anything you expected to find that is not there? • What is unnecessary if anything? • If you had a magic wand, what would you change about this experience?Reflect, Iterate, and ImplementThe researchers had an opportunity to reflect individually and debrief as a group following eachfocus group interview and discussed what was learned. The following questions helped guideresearchers’ reflections on understanding learning from the student perspective: • What did I learn
[3], researchers found the ten-year completion rate for engineeringPh.D.’s is only around 62%. Studies have indicated many factors within engineering graduateschool culture that lead to attrition from graduate school, especially relating to students’expectations, goals, and quality of work and life [4]-[6]. Specifically, Zerbe et al. [6] identifiedthat mismatched expectations and preconceptions for graduate school directly led students toquestion or depart from their programs. Recognizing the challenges related to pursuing anengineering graduate degree, undergraduate students motivated to pursue graduate degrees wouldgreatly benefit from additional preparation for the culture and expectations for graduate students. Socialization
’ perceptions on the use of ChatGPT in engineering. Further studies can be conducted todetermine the factors influencing the undergraduate and graduate students’ perceptions on the useof ChatGPT in engineering considering different demographic parameters such as gender identity,race/ethnicity, class standing, engineering major, etc.AcknowledgementThe authors would like to thank the content experts and potential participants for providingfeedback on the survey instrument. Thank you to all the respondents. This project was supportedby the Provost’s Summer Undergraduate Research and Creative Activities (UReCA) Fellowship.Its contents, including findings, conclusions, opinions, and recommendations, are solely attributedto the author(s) and do not
. Higher Education, 35(3), 299–316. https://doi.org/10.1023/A:1003145613005Fink, L. D. (2013). Creating Significant Learning Experiences: An Integrated Approach to Designing College Courses. John Wiley & Sons.Ford, J. K., Smith, E. M., Weissbein, D. A., Gully, S. M., & Salas, E. (1998). Relationships of goal orientation, metacognitive activity, and practice strategies with learning outcomes and transfer. Journal of Applied Psychology, 83(2), 218–233. https://doi.org/10.1037/0021-9010.83.2.218Khachikian, C. S., Guillaume, D. W., & Pham, T. K. (2011). Changes in student effort and grade expectation in the course of a term. European Journal of Engineering Education, 36(6), 595–605. https://doi.org
; Research methods qualitative; mixed methods; multi methods; not applicable (synthesis, conceptual/theory); unspecified; other (please specify) A multi-select list of: class; department/school; discipline; SB reference group institution; online contexts (e.g., classroom); peer/other small groups; research lab; unspecified; other (please specify) Geographic location of the Area(s) where study took place, if applicable study Results and DiscussionsThis
tracing, and imitation learning [2-3], [8], [12], [16]. There are a varietyof other algorithms, however, these are the ones that are mainly incorporated in the reviewedarticles. Through the literature search, it was evident that reinforcement learning (RL) is the mostwidely used algorithm, consistently picked due to its high versatility and adaptability compared toother algorithms.RL is often preferred as it has a unique ability, allowing the AI agent to ‘communicate’ with itsenvironment, opening more gateways for development in programs, especially in gamedevelopment [11], [15], [18]. In this method, there are two main components, the agent and theenvironment. The environment reveals itself and its current data in the form of the state, S; and