work supported by the National Science Foundation under GrantNo. 2225201. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. We would like to acknowledge the other members of the VOCES team whocontributed to the design and implementation of the VOCES project: Yesim Darici (PI), RocioBenabentos, Laird Kramer, Janki Bhimani, Leanne Wells, Stephen Secules, JaroslavaMiksovska, and Umut Caglar. We also acknowledge and appreciation the contributions of theundergraduates who participated in the VOCES project and the storytelling workshops.References[1] R. Adams, C. Allendoerfer, T. Smith, D. Socha, D. Williams, and
universitarios,” Revista Electr´onica de Psicolog´ıa Iztacala, vol. 20, no. 1, mar. 2017. [Online]. Available: https://www.revistas.unam.mx/index.php/repi/article/view/58921 [5] F. Sempere-Ripoll and A. Rodr´ıguez-Villalobos, “La emoci´on como clave del e´ xito para el desarrollo de competencias en la direcci´on de operaciones,” Direcci´on y Organizaci´on, p. 73–84, Jul. 2019. [Online]. Available: http://dx.doi.org/10.37610/dyo.v0i68.553 [6] B. Giangrasso, S. Casale, G. Fioravanti, G. L. Flett, and T. Nepon, “Mattering and anti-mattering in emotion regulation and life satisfaction: A mediational analysis of stress and distress during the covid-19 pandemic,” Journal of Psychoeducational Assessment, vol. 40, no. 1, p. 125–141, Dec
current thesis is on the instrumentation of an autonomous vehicle.Prof. Sean Walker, University of South Alabama Dr. Walker is an Associate Professor at the University of South Alabama in Mobile, AL and Program Coordinator of the Systems Engineering Program. They received their Ph.D. in Systems Design Engineering from the University of Waterloo, in Waterloo, Canada, in 2012. Dr. Walker has taught at the University of South Alabama since 2016 and has won multiple teaching awards from Mortar Board and Tau Beta Pi. Sean’s research interests include Engineering Education, and Sustainable Systems.Rachel Chai, University of South Alabama Rachel S. Chai was born in Ocean Springs, MS in 1996. She received his B.S. and M.S. in
Time (s)Figure 3: Cursor position (black lines) and code length (gray lines) measured in characters from the beginning ofthe source code (y-axes), vs. time in seconds (x-axes), one subplot per attempt. Solid and dashed panel bordersindicate eventual success vs. failure, respectively. Rows correspond to participants (labeled on right) and columns to arepresentative subset of problems (labeled on top; see code repository for full descriptions of each problem).DiscussionWe have presented a new web-based code editor that keylogs users. As a free and open-sourceweb-based application, it is operating-system agnostic, requires no installation by participants,can be administered remotely, and is readily available to education researchers. This makes
do not necessarily reflect the views of theNational Science Foundation.References[1] M. M. Espino, “Exploring the role of community cultural wealth in graduate school access and persistence for mexican american phds,” American Journal of Education, vol. 120, no. 4, pp. 545–574, Aug. 2014, doi: 10.1086/676911.[2] M. M. Camacho and S. M. Lord, “Quebrando fronteras: Trends among Latino and Latina undergraduate engineers.,” Journal of Hispanic Higher Education, vol. 10, no. 2, pp. 134–146, 2011.[3] J. Mejia, R. Revelo, I. Villanueva, and J. Mejia, “Critical Theoretical Frameworks in Engineering Education: An Anti-Deficit and Liberative Approach,” Education Sciences, vol. 8, no. 4, p. 158, Sep. 2018, doi
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than we expected with a variety of diverse experiences.”Author 2’s conversations with her participants were essential to creating an effectiveco-designing environment. Through feedback from research advisors, she learned the use ofstorytelling as a method for engaging the teacher co-designers in providing feedback regardingthe workshop and the designed lessons. Conversations can look different; they do not have to betraditional interviews or focus groups. Storytelling provided more information than Author 2expected, making it a method she is more likely to use in future workshops. 3. Building and Engaging with CommunitiesCo-design requires engagement with research participants and their local communities to addresscommunity needs
timeworking in a research lab had a greater impact on their long-term goals than their classes, andworking in the lab made them consider graduate school. “But like, [PROFESSOR]’s lab I think, makes me- it like, put the idea of graduate school in my head, even though that was like never in my- I never thought about it before. I was just kind of going to get my degree and then start doing something I was more interested in. But now, like, since I’m interested in research, it’s opening up different doors in my mind. I’m like, ‘I could do that, in materials science’ or get master’s in materials science and then work still in aerospace, just on the materials side, which would be like, very cool. . . But I still
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Guevara, J. C. Tudón Martínez, D. Hernández Alcántara, and R. Morales-Menendez, “Active learning in engineering education. A review of fundamentals, best practices and experiences,” Int. J. Interact. Des. Manuf. IJIDeM, vol. 13, pp. 909–922, 2019, doi: 10.1007/s12008-019-00557-8[3] S. Olson and D. G. Riordan, “Engage to excel: producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Report to the president.,” Exec. Off. Pres., 2012, https://eric.ed.gov/?id=ed541511.[4] E. ASEE, “Transforming Undergraduate Education in Engineering: Phase II: Insights from tommorow’s engineers,” Wash. DC Natl. Sci. Found., 2017.[5] A. J. James, C. K. Chin, and B. R. Williams
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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
Learning in STEM education,” Educ. Technol. Res. Dev., vol. 70, no. 4, pp. 1169– 1194, Aug. 2022, doi: 10.1007/s11423-022-10122-y.[8] S. L. Thorne, R. W. Black, and J. M. Sykes, “Second Language Use, Socialization, and Learning in Internet Interest Communities and Online Gaming,” Mod. Lang. J., vol. 93, pp. 802–821, Dec. 2009, doi: 10.1111/j.1540-4781.2009.00974.x.[9] M. Bravo and E. Garcia, “Learning to write like scientists: English language learners’ science inquiry and writing understandings in responsive learning contexts.,” presented at the American Educational Research Association, 2004.[10] B. A. Brown and K. Ryoo, “Teaching science as a language: A ‘content-first’ approach to science teaching,” J. Res. Sci. Teach
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/ 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
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
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