-scored-higher-on-a-medical-quiz-than-a-real-human-doctor (accessed Jun. 02, 2023).[3] D. C. Weiss, “Latest version of ChatGPT aces bar exam with score nearing 90th percentile,”ABA Journal, Mar. 16, 2023. https://www.abajournal.com/web/article/latest-version-of-chatgpt-aces-the-bar-exam-with-score-in-90th-percentile[4] J. Narayan, K. Hu, M. Coulter, and S. Mukherjee, “Elon Musk and others urge AI pause,citing ‘risks to society,’” Reuters, Mar. 29, 2023. Available:https://www.reuters.com/technology/musk-experts-urge-pause-training-ai-systems-that-can-outperform-gpt-4-2023-03-29/[5] F. Candelon, R. C. di Carlo, M. D. Bondt, and T. Evgeniou, “AI Regulation Is Coming,”Harvard Business Review, Sep. 01, 2021. https://hbr.org/2021/09/ai-regulation-is
measures the degreeto which a lesson integrates technology and helps students reach the learning goal(s). Theframework is based on three main components: (1) Engagement in learning goals (2)Enhancement of learning goals, and (3) Extension of learning goals. The key concept of thisframework which is relevant to this study is its emphasis on the importance of the instructionalstrategy, which goes hand-in-hand with the use of any technology for learning.In this project, the Triple E Evaluation framework will be utilized as a lens to assess whether thetechnology choices made for teaching and learning leads to student engagement in learninggoals, enhancement of learning goals, and whether technology use helps the learners extend theiracademic learning
stakeholder representatives. Thesequestions are not those included in the questionnaires but are rather the guiding questions for thebackwards design process.Table 1 SET Content Areas Content Area Essential Questions Stakeholders To what extent did the students learn the content contained in the A Faculty, Student learning objectives? B To what extent did the course meet ABET student outcomes? Faculty, Administrator Was the way(s) the course was taught effective at helping students C Faculty, Administrator, Student learn the
Attendees Challenge n (approximate s ) completed Introduction: Scavenger 12 9 Algorithms and Deaf Deaf in hunt 1 people in STEM STEM Blocks: Inputs, LEDs, Icons, String, Pause, Show Engineering Icon design, 12 8 Blocks, Loops and Design Handwashing2
the local context, onewill likely be able to use data, at least in aggregate, such as students’ course and instructorevaluations for such understanding.References[1] S. Chandrasekaran, A. Stojcevski, G. Littlefair, and M. Joordens, “Learning through projects in engineering education,” in SEFI 2012: engineering education 2020: meet the future: proceedings of the 40th SEFI annual conference 2012. European Society for Engineering Education (SEFI), 2012.[2] C. S. Johnson and S. Delawsky, “Project-based learning and student engagement,” Academic research international, vol. 4, no. 4, p. 560, 2013.[3] C. Duhigg, “What google learned from its quest to build the perfect team,” The New York Times Magazine, vol. 26, no. 2016, p. 2016, 2016
Perspectives,” J. Eng. Educ., vol. 106, no. 3, pp. 398–430, 2017, doi: 10.1002/jee.20170.[5] C. J. Atman, J. R. Chimka, K. M. Bursic, and H. L. Nachtmann, “A comparison of freshman and senior engineering design processes,” Des. Stud., vol. 20, no. 2, pp. 131–152, Mar. 1999, doi: 10.1016/S0142-694X(98)00031-3.[6] S. R. Daly, R. S. Adams, and G. M. Bodner, “What Does it Mean to Design? A Qualitative Investigation of Design Professionals’ Experiences,” J. Eng. Educ., vol. 101, no. 2, pp. 187–219, 2012, doi: 10.1002/j.2168-9830.2012.tb00048.x.[7] J. S. Gero, “Fixation and Commitment While Designing and its Measurement,” J. Creat. Behav., vol. 45, no. 2, pp. 108–115, 2011, doi: 10.1002/j.2162-6057.2011.tb01090.x.[8] V. L. Vignoles
National Science Foundationunder Grant No. 1943811. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References[1] Josiam, M., Lee, W., Johnson, T., Pee, C., & Hall, J. (2022, August). Beyond Selecting aMethodology: Discussing Research Quality, Ethical, and Equity Considerations in QualitativeEngineering Education Research. In 2022 ASEE Annual Conference & Exposition.[2] D. M. Cable and J. R. Edwards, “Complementary and supplementary fit: A theoretical andempirical integration.,” Journal of Applied Psychology, vol. 89, no. 5, pp. 822–834, 2004.[3] P. M. Muchinsky and C. J. Monahan, “What is person
to feel comfortable with both their peers and their TA tobe able to recover from a setback quickly. 1. Student experiences a setback (lab does not go as planned). 2. Student looks to a) lab partner(s) or peers, and/or b) TA, and/or c) class and lab materials to decide how to respond. 3. Student's ability to move past the setback depends on whether a) others experience the same setback, b) others normalize setbacks, and c) they know where to look to help them troubleshoot. These factors impact whether they can effectively manage their frustration in the moment.Figure 1. Student Response to Setbacks in Lab Settings FlowchartConclusion To summarize, students’ ability to recover from
focus groups to understand participants’lived experiences around identity-mediated interest changes and enrollment choices. Thelongitudinal element of this work allows us to evaluate when a new interest was identified andthe choice(s) participants made regarding pursuing that interest as these two elements often donot occur in the same semester. A singular data point would not fully capture the story ofchanging interests and choices, rather we utilize focus group data from participants’ first sixsemesters in an undergraduate engineering program. Data were analyzed using directed contentanalysis to support the exploration of the phenomenon while allowing for the integration of atheoretical framework including identity and interest. Matrix
Practices and Processes,” Hollylynne S. Lee etel. developed a framework using the work of statistics educators and researchers to investigatehow data science practices can inform work in K–12 education. Their framework buildsfundamental practices and processes from data science [19]. The math field has contributed to data science research via the Common Core StateStandards Initiative (CCSSI), which is a joint project to develop common K–12 reading andmath standards designed to prepare students for college and careers. The CCSSI includes a datascience section for elementary students that focuses on data collection, data type, function,analysis type, and sample [20]. Similarly, the Launch Years Data Science Course Frameworkprovides broad
the career development of women. Journal of Vocational Behavior, 18(3), 326–339. https://doi.org/10.1016/0001- 8791(81)90019-1 [4] Hurst, M. A., Polinsky, N., Haden, C. A., Levine, S. C., & Uttal, D. H. (2019). Leveraging research on informal learning to inform policy on promoting early stem. Social Policy Report, 32(3), 1–33. https://doi.org/10.1002/sop2.5 [5] Removed for Double Blind Review [6] Lester, S., & Ruth, K. D. (2022, August). ’ook Who's Talking: Exploring the DEI STEM Librarianship Conversation. In 2022 ASEE Annual Conference & Exposition. [7] Roy, J. (n.d.). Engineering by Numbers - ira | ASEE. ASEE. Retrieved February 8, 2023, from https://ira.asee.org/wp-content/uploads/2019/07/2018
. How well this process is conducted is the primary focus of quality in narrative research.Indicators of Quality in Narrative SmoothingRecent work has sought to establish frameworks capable of assessing the quality of qualitativeresearch methods. In line with Walther et al.'s work, we define quality interpretative research asresearch that is "idiographic in nature, in that it emerges from the unique perspective ofindividuals or groups but is transferrable to and meaningful for other contexts" [22]. We findgreat utility in tools such as Walther & Sochacka’s Q3 framework, which provides a versatileguide for implementing quality across various qualitative methods[23] . Tools such as this helpresearchers assess how they produce and manage
confidential.Furthermore, no attempt to oversample women or minorities was made in collecting the sampledata. All results are cross-sectional.InstrumentsThe self-reflection survey contained a total of 41 questions. Questions about learning outcomesrelevant to technical skills were developed based on Davis et al.’s conceptual model for capstoneengineering design performance and assessment and ABET’s student outcomes #3 [1]. Questionsrelating to non-technical outcomes were adapted from scales developed by Chandler et al. to studyentrepreneurs’ competencies [49] and scales developed by Keinänen et al. to measure innovationcompetencies of students in the applied sciences [50]. Table 1: Likert-Scale Survey Items associated with Student Learning OutcomesItem
Safety and Hazard Investigation Board, “Non-Condensable Gas System Explosion at PCA DeRidder Paper Mill,” Washington, DC, 2018.[5] United States Chemical Safety and Hazard Investigation Board, “Key Lessons from the ExxonMobil Baton Rouge Refinery Isobutane Release and Fire,” Washington, DC, 2017.[6] P. R. Amyotte, S. Berger, D. Edwards, J. P. Gupta, D. C. Hendershot, F. I. Khan, M. S. Mannan, and R. J. Willey, “Why major accidents are still occurring,” Current Opinion in Chemical Engineering, vol. 14, pp. 1–8, 2016, doi: 10.1016/j.coche.2016.07.003.[7] T. M. Osberg and J. S. Shrauger, “Self-Prediction: Exploring the Parameters of Accuracy,” J. Pers. Soc. Psychol., vol. 51, no. 5, pp. 1044–1057, 1986.[8
2005, American Society for Engineering Education References1. Boronkay, T. G., and Janak, D. “Introduction of Finite Element Methods in the Lower Division Mechanical Engineering Technology Curriculum.” Proceedings of the ASEE Annual Conference, Milwaukee, WI, 1997. Session 2238.2. Cole, W.: “Graphical Applications: Analysis and Manufacturing”. Engineering Design Graphics Journal, Spring, 1999, pp 43-49.3. Howell, S.: “Finite Element Analysis in a Freshman Graphics Course?” Engineering Design Graphics Journal, Winter, 1993, pp 29-32.4. Juricic, D., Howell, S., Jenison, R., and Barr, R. “Extending Engineering Design Graphics Laboratories to have a CAD/CAM Component – Part II
professional development model as a lens.Participants were nine sixth grade science teachers from three rural and Appalachian schoolsystems who engaged in the first year of the VT-PEERS project. The participants wereinterviewed prior to the first intervention activity, at the end of the first academic year, observedduring interventions, and asked to fill out an online questionnaire to capture their demographicinformation. The interviews lasted approximately 30-minutes. Pertinent questions for thisanalysis were: “What influenced your decision to participate in this project?”; What role(s) doyou expect to have during this collaboration?”; “What role(s) do you expect other partners(Industry or University) to have?”Through open coding (Miles, Huberman
/sunday/the- asian-advantage.html[2] D. E. Naphan-Kingery, M. Miles, A. Brockman, R. McKane, P. Botchway, and E. McGee, “Investigation of an equity ethic in engineering and computing doctoral students,” Journal of Engineering Education, vol. 108, no. 3, pp. 337–354, 2019, doi: 10.1002/jee.20284.[3] National Science Board, “The State of US Science and Engineering 2022,” National Science Foundation, Alexandria, VA, 2022. Accessed: Dec. 02, 2022. [Online]. Available: https://ncses.nsf.gov/indicators[4] L. D. Patton and S. Bondi, “Nice white men or social justice allies?: using critical race theory to examine how white male faculty and administrators engage in ally work,” Race Ethnicity and Education, vol. 18, no. 4, pp. 488–514
]. Founded in 2013, the focus of this capstoneprogram is to develop innovative technical solutions to pressing clinical and translational healthchallenges. Undergraduate and graduate students across engineering disciplines (e.g.,mechanical, electrical, biomedical, chemical, and materials science) are partnered with healthprofessionals (e.g., physicians, nurses, dentists, therapists, pharmacists) to solve unmet healthchallenges. In the first quarter, teams of 3–5 students work closely with the health professional(s)who originally proposed the unmet health challenge to develop a deep understanding of theunmet health need, including potential markets, stakeholder psychologies, prior solutions,intellectual property considerations, regulatory
Technological Student’s self perception of their REFERENCES Self-efficacy2 capabilities to utilize technology (tools 1. McGee, J. E., Peterson, M., Mueller, S. L., & Sequeira, J. M. (2009
Clue.References[1] S. Coffman-Wolph and K. Gray, “Computer coding scavenger hunt using quick response codes (resource exchange),” in 2020 ASEE Virtual Annual Conference Content Access Proceedings, 2020.
Virginia University, Morgantown, WV, Mar. 27 – 28, 2020. 2. Michael, R.J. and Piovesan, D., “Use of Engineering Software Programs for Self-Directed Learning,” Acad. Process Educators 2018 Conference, Gannon University, Erie, PA, June 2018. 3. Pollino, M., Sabzehzar, S., Michael, R., “Mechanical Behavior of Base Isolated Steel Storage Racks Designed for Sliding-Rocking Response,” Eleventh U.S. National Conference on Earthquake Engineering, Los Angeles, CA, June 25 – 29, 2018. 4. Piovesan, D., Church, D., Herron, S., Oldham, C. Sebald, M., Michael, R., Bitticker, S., “Orthopedic anterior cruciate ligament evaluator (or A.C.L.E.),” Proc. ASME 2015 International Mechanical
practitioners might. When requested by thestudents, faculty would provide suggestions based on student ideas and/or concerns. Self-selecting software remains the course standard as long as the software was within the resourcesavailable to the AE program. Over the years and amongst teams, selected software varied in typeand number of platforms relative to how teams wanted to customize their experience.Considerations for software were based on perceived benefits that the software could aid theteam towards meeting the capstone goal of designing integrated engineered system(s) solutions.Faculty observations found that software could be grouped into two overarching categories: Design documentation software that students use to convey solutions to a
collection. Through GORP, the observer can select codes forobserved classroom activity for both the instructor(s) and students. Observations are coded in 2-minute intervals until the class session is over. If the observer makes a mistake, they can note itduring the next interval, and adjust the data accordingly by hand, after class. Data isautomatically analyzed in GORP and can be exported to a spreadsheet for further analysis.The COPUS evaluation process was also part of the development of this Work-in-Progress. Wefollowed the clustering convention put forth by Stains et al. [86] in order to better capture thebroader types of instructor and student behaviors that we were interested in at this stage in thestudy -- who's talking, who's working, who's
informstheir presentation.Acknowledgement: This material is based upon work supported by the National ScienceFoundation under Grant #s 1758317 and 1339951.Disclaimer: 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.References[1] R. W. Bybee, Case for STEM Education: Challenges and Opportunities, Arlington, VA, USA: National Science Teachers Association, 2013.[2] United States Department of Education, Fundamental Change: Innovation in America’s Schools Under Race to the Top, Washington, DC, USA, Nov. 2015. Available: https://www2.ed.gov/programs/racetothetop/rttfinalrptfull.pdf[3] United
information about the program and its successes at a wide variety ofconferences and meetings. A list of such presentations is given in Appendix B for the readerwho would like more detailed information about a particular aspect of STEER. The reader isalso encouraged to contact members of the leadership team directly.AcknowledgementThis project was supported in part by National Science Foundation IUSE grant No. DUE-1525574. We are grateful to the Office of Decision Support at the University of South Floridafor the permission to publish the course and institutional data presented here.References[1] G. Meisels, R. Potter, P. Stiling, J. Wysong, and S. Campbell, “Systemic transformation ofevidence-based education reform (STEER),” 2019 ASEE Annual
the author(s) and do not necessarily reflect theviews of the National Science Foundation.References[1] I. A. Toldson, I, “Why historically black colleges and universities are successful with graduating black baccalaureate students who subsequently earn doctorates in STEM (editor’s commentary),” J. Negro Educ., vol. 87, no. 2, pp. 95–98, 2018.[2] R. Winkle-Wagner and D. L. McCoy, “Feeling like an “Alien” or “Family”? Comparing students and faculty experiences of diversity in STEM disciplines at a PWI and an HBCU,” Race Ethn. Educ., vol. 21, no. 5, pp. 593-606, 2018.[3] R. T. Palmer, R. J. Davis, and T. Thompson, “Theory meets practice: HBCU initiatives that promote academic success among African Americans
as a scholar, dedicated to their course of study, hasbeen in decline since the 1960’s [1], [2]. A Bachelor’s degree was a means of academicintegration and status [3], facilitated by conventional lecture where an expert addresses thestudents using authoritative lecture style [4]. In this paradigm, students benefit from the student-mentor relationship having prepared for the interaction with intensive study. This has beenshown to be less beneficial to non-traditional students [5]. Non-traditional students come frompopulations such as community college transfer students or people who work temporary jobs thatare unrelated to their course of study [6]–[8]. Heavy workloads—greater than part-time workobligations—hamper their ability to do well