purpose of this paper is to recommend adapting new pedagogical methods to theaccepted topics in an introductory probability and statistics course for engineeringundergraduates—methods that better match the learning characteristics of Millennial students inour courses. In a nutshell, those characteristics may be summarized as: (1) They want relevanceto their major, and future engineering career; (2) They want rationale (for the textbook selected,and for specific course policies and assignments); (3) They revel in technology (to collect data,compute, communicate, and multi-task); (4) They want a relaxed, hands-on environment; (5)They prefer instructors who rotate among several classroom delivery methods.Considering the “Five R‟s” learning
and minorities continue to be underrepresented in engineering, both nationally and atRoger Williams University. In 2012, women constituted just 12% of engineering graduates at theuniversity, while minorities constituted just 4%. In an effort to boost the enrollment, performance,and persistence of underrepresented students, the university applied for and received an NSF S-STEM grant to integrate engineering, biology, and marine biology students into an existingprogram supporting underrepresented students on campus. The combined program, known asSTILAS, provides participants with a $10,000 NSF scholarship, supplemented by the university,as well as dedicated tutoring and advising, and co-curricular activities such as field trips and
metallurgical engineering at the South Dakota School of Mines and Technology (SD Mines). Between 2008-2013, he served as site director of the NSF I/UCRC Center for Friction Stir Processing (CFSP). Since then, he has been involved in a range of projects involving friction stir joining and alloy processing in a variety of metal alloys including aluminum alloys, ODS steels, titanium alloys, cast irons, and dissimilar metal alloys. He is also actively engaged in STEM-Ed projects and serves as the director for the NSF Research Experience for Undergraduates (REU) ”Back to the Future”, coordinator for the Army Educational Outreach REAP program for High school students at SD Mines, and PI for the S-STEM Culture and Attitude program
duein class the following week. Two midterm exams and one final exam were given, and studentscompleted two Matlab projects in groups of three.ParticipantsThe course was taught by the same instructor in both terms considered in this study. Theinstructor was a full-time faculty member at the university with over 10 years of teachingexperience. S/he had taught the DTSS course discussed here several times prior to the two termsin question. Student participants in the study were predominantly male, junior or senior students,majoring in electrical engineering. The majority of students were also domestic and in-state.However, they varied greatly in GPA. The students were also diverse in race/ethnicity with overhalf being either White or Asian. The
: 1. Include descriptions of the cultural context 2. “[A]ttend to the embodied nature of the protagonist” [39, p.17] 3. Consider how other people affect the central character 4. Identity choices and actions of the central character 5. Attend to past experiences and how they impact the choices and actions 6. Create a story with a beginning, middle, and end 7. The plot should bring all the data together into a meaningful story that explains why the central character acted the way s/he didQuality ConsiderationsThis project will be monitored by an external review board and an internal framework.Internally, we will use the Q3 framework outlined by Walther et al. [41] and Walther
Psychology, 101(4), 817-835.2 Kell, H. J., & Lubinski, D. (2013). Spatial ability: A neglected talent in educational and occupational settings. Roeper Review, 35(4), 219-230.3 Newcombe, N. S., Uttal, D. H., & Sauter, M. (2013). Spatial development. Oxford Handbook of Developmental Psychology, 1, 564-590.4 National Research Council (NRC). (2006). Learning to think spatially: GIS as a support system in the K-12 curriculum. Committee on the Support for the Thinking Spatially, National Research Council, Publisher: The National Academies Press, URL: http://books. nap. edu/catalog. php.5 Sorby, S. A. (2009). Educational research in developing 3‐D spatial skills for engineering students. International Journal
J K L M N O P Q R S T U 21 11 3 3 9 11 3Table 2 lists the research topics and physics associated with the 21 most successful models todate. Four topics
research questions, we decided to examine defining characteristics ofindividuals identified by our participants as exemplary engineering leaders. It was at this point Page 26.815.2that we noticed a significant over-representation of men in the pool of highly esteemed leaders.In this paper, we use a factor analysis and Chi-Square Goodness of Fit test to examine onepossible reason for this disparity—a gender difference in engineers’ leadership aspirations. Wethen use a focused literature review to hypothesize two alternative explanations for our finding. T ABLE 1 : S AMPLE C HARACTERISTICS Category Sub-Categories
differentengineering disciplines to solve many important manufacturing automaton problems. As a finalproject, students are expected to model and simulate a work cell for the selected application andto perform the same with the physical robots in the lab. They will compare both outcomes forevaluation of the calculated results. Students submit a comprehensive engineering report todocument all requirements. Experiments and projects are designed and implemented in asequence that would allow the students to acquire a complete manufacturing automationexperience. This included on-line and off-line robot programming (uploading and downloadingprograms between robots controllers and simulation software), robot integration (addingperipherals to a robot(s) to create a
expressed in this work are those of the author and do not necessarily representthose of the National Science Foundation.References[1] V. Hunt, S. Prince, S. Dixon-Fyle, and L. Yee, "Delivering through diversity," McKinsey & Company Report. Retrieved April, vol. 3, p. 2018, 2018.[2] ASEE, "Transforming Undergraduate Education in Engineering, Phase I: Synthesizing and Integration Industry Perspectives.," 2013.[3] J. L. Arminio et al., "Leadership experiences of students of color," NASPA journal, vol. 37, no. 3, pp. 496-510, 2000.[4] C. R. Romano, "A qualitative study of women student leaders," Journal of College Student Development, 1996.[5] A. Kezar and D. Moriarty, "Expanding our understanding of
engineeringfaculty at a research institution who collaborated on an NSF-funded research project aimed atstudying the impact of implementing oral exams in high enrollment courses. The primaryresearch questions were: How did the instructor’s perspectives and behaviors change as theyimplemented oral exams in their courses? How did the instructors act on a growth-orientedmindset?MethodsWe invited six teaching professors from the departments of Mechanical and AerospaceEngineering and Electrical Engineering to participate in the study. To protect the confidentialityof each individual, pseudonyms were used in lieu of using their full names in data analysis (SeeTable 1). Instructor Department Course(s) that implemented oral exams
diverse perspectives andfemale role models in STEM (Konowitz et al., 2022). Introducing students to the narratives andaccomplishments of women, minorities, and people from various cultural backgrounds canmotivate and empower underrepresented groups to pursue careers in STEM (Cheryan et al.,2015; Gilberth, 2015). Institutions, including K-12 and higher education, should develop moreinclusive and supportive environments for students interested in STEM. This involves offeringmentorship programs, networking opportunities, professional development for teachers, andresources suited to the needs of different student demographics. Such efforts align with Yeo etal.’s (2024) preliminary work that teachers use verbal and non-verbal cues to facilitate
s sections of theengineering course at a large Midwestern university. Over the semester, students were asked toreflect after each lecture on two aspects of their learning experience, i.e., what they found 1)interesting and 2) confusing in the lecture? In total, we collected reflections from 42 lectures, andthe average class size was 80 students in each section. To inform the study, we generated areflection summary for all reflection submissions in each lecture using both NLP approaches andhuman annotators. Furthermore, we evaluated the quality of reflection summaries by assessingthe ROUGE-N measure for each lecture’s reflection summary generated by all three approaches.These summaries were then aggregated for each approach by averaging
-levelthemes that capture the essence of the interview corpus, but it performed poorly in mapping theconcepts to specific files. Therefore, a hybrid approach that leverages the strengths of both AIand human expertise may be the most effective strategy for analyzing complex qualitative data ineducational research.AcknowledgmentThis material is based upon work supported by the U.S. National Science Foundation (NSF)under Grant No. (DUE 2120936). Any opinions and findings expressed in this material are of theauthors and do not necessarily reflect the views of the NSF.References:[1] S. Kulturel-Konak, "Overview of Student Innovation Competitions and Their Roles in STEM Education," in 2021 Fall ASEE Middle Atlantic Section Meeting, 2021. [Online
educational technology to plan, prepare, and deliver robotics lessons tofifth graders at a local school. The meeting times for the two courses were scheduled to overlapfor 75 minutes a week, allowing the engineering and education students to work collaborativelyduring multiple class sessions. Each team comprised one or two engineering student(s), onepreservice teacher, and one or two fifth grader(s). The teams engaged in the followingcollaborative activities over the course of the semester: ● Training phase. The first two collaborative sessions involved engineering students and preservice teachers meeting in a classroom on campus and partnering in teams to: ○ train with the Hummingbird BitTM hardware (e.g. sensors, servo motors) and
has over 8 years of work experience in the A/E/C (Archite ©American Society for Engineering Education, 2024 Technological Infrastructure Equity for Minority Serving Institutions in Construction EducationAbstract: In the U.S. and its territories, over 800 identified Minority Serving Institutions (MSI)exist. Despite the number of MSI and the diverse population that they targeted, there is a gap inthe number of higher education degrees obtained by minority students in relation to non-minoritystudents. The root cause(s) of the gap must be determined to take tangible actions to reduce and,ideally, eliminate this obtainment gap. When considering this gap, there is a question of
Student with ADHD and a Reading Disability,” in Promoting Safe and Effective Transitions to College for Youth with Mental Health Conditions, A. Martel, J. Derenne, and P. K. Leebens, Eds. Cham: Springer International Publishing, 2018, pp. 95–102.[3] M. A. Zapata and F. C. Worrell, “Disability Acceptance and Affirmation Among U.S. Adults With Learning Disabilities and ADHD,” J. Learn. Disabil., vol. 57, no. 2, pp. 79–90, Mar. 2024.[4] S. Maul and R. Figard, “Diminishing the data divide: Interrogating the state of disability data collection and reporting,” presented at the American Society for Engineering Education 2024, Portland, OR, 2024.[5] Learning Disabilities Association of America, “ADHD – Affects focus, attention and
Assessment Program, 2003.[2] C. R. Pace and G. G. Stern, “An approach to the measurement of psychological characteristics of college environments,” Journal of Educational Psychology, vol. 49, no. 5, pp. 269–277, Oct. 1958, doi: http://dx.doi.org/10.1037/h0047828.[3] P. T. Terenzini and E. T. Pascarella, “Twenty Years of Research on College Students: Lessons for Future Research,” Research in Higher Education, vol. 32, no. 1, pp. 83–92, 1991.[4] C. Kandiko Howson and F. Matos, “Student Surveys: Measuring the Relationship between Satisfaction and Engagement,” Education Sciences, vol. 11, no. 6, Art. no. 6, Jun. 2021, doi: 10.3390/educsci11060297.[5] P. C. Wankat and F. S. Oreovicz, Teaching Engineering
careers: Leaky pipeline or gender filter?” Gender and Education, 17(4), pp. 369–386, 2005.[2] R. Suresh, “The relationship between barrier courses and persistence in engineering.” Journal of College Student Retention, 8(2), pp. 215–39, 2006/2007.[3] T. Armstrong, Neurodiversity: A Concept Whose Time Has Come. Da Capo Press. 2010. p. 3.[4] T. Armstrong “The Myth of the Normal Brain: Embracing Neurodiversity.” AMA J Ethics.17(4): pp. 348-352, 2015. doi:10.1001/journalofethics.2015.17.4.msoc1-1504.[5] C. L. Taylor, A. Esmaili Zaghi, J. C. Kaufman, S. M. Reis, and J. S. Renzulli, “Divergent thinking and academic performance of students with attention deficit hyperactivity disorder characteristics in engineering
Paper ID #38459Work in Progress: Engineering Identity Development after Two Years ofUndergraduate EducationJanet Aderemi Omitoyin, Janet Omitoyin is a PHD student in the Department of Curriculum and Instructions, University of Illinois at Chicago (UIC). An astute scholar, Janetˆa C™s quest for a solution to the problems of mathematics learning based on her experience as a student andDr. Renata A. Revelo, The University of Illinois, Chicago Renata Revelo is a first-generation college student, migrated from Ecuador to the United States as a teenager with her parents and sister. She is the first in her family to obtain a
., examining the nuance in January and Srihari’s disability identities whenconsidering engineering and US cultural stigma regarding mental health disabilities). Bydeveloping a greater understanding of the ways student narratives intersect with their culturalformation as engineers, we can contribute to an engineering education culture that not onlyaccepts, but invites students to freely and simultaneously construct their personal andprofessional identities.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under AwardNumbers 2114241 and 2114242. 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
past chair of the Research in Engineering Education Network (REEN) and a deputy editor for the Journal of Engineering Education (JEE). Prior to joining ASU he was a graduate research assistant at the Tufts’ Center for Engineering Education and Outreach. ©American Society for Engineering Education, 2023 Examining the Unique Experiences of Transgender and Gender Nonconforming Students in a Pre-College Engineering CourseIntroduction Very little research on transgender and gender nonconforming (TGNC) students inengineering has been undertaken to better understand the experiences of this underrepresentedand largely ignored population. Pawley et al. 's [1] review of published articles in
success variables, college grades a (i.e., first year GPA) and creativity.Preliminary findings suggest that specific college experiences have a greater influence on first-year GPA and that students with ADHD are more likely to self-report high levels of creativity.We also plan to conduct the analysis for resilience, a less-common measure of collegiateacademic success that may be relevant for students who have ADHD.Table 2. Model components, constructs, and survey items from the HERI instrument [32], [33]. Components and constructs of our model Item(s) from the HERI instruments Precollege characteristics & experiences Gender Gender of respondent; Survey choices: Female, Male Sociodemographic
had been highly rated at the time of original review. Inpart because of this and in part because it is an important part of proposal review, our reviewerswere asked to closely read the current program description and calls for proposals and evaluatethe proposals with respect to how well they matched the current call. This allowed for apotentially greater range of quality evaluations, with the understanding that there would be amismatch between the current call and the call the original proposals responded to. The callsused in this training were the Preparing Future Engineers: Research Initiation in EngineeringFormation (PRF: RIEF), Scholarships in Science, Technology, Engineering & Math (S-STEM),and the Faculty Early Career Development
/translating-theory-on-color-blind-racism-to-an-engineering-educatio n-context-illustrations-from-the-field-of-engineering-education.[10] S. Johnston, A. Lee, and H. McGregor, “Engineering as Captive Discourse,” Society for Philosophy and Technology Quarterly Electronic Journal, vol. 1, no. 3/4, pp. 128–136, Oct. 1996, Accessed: Jul. 06, 2021. [Online].[11] M. G. Eastman, M. L. Miles, and R. Yerrick, “Exploring the White and male culture: Investigating individual perspectives of equity and privilege in engineering education,” J. Eng. Educ., vol. 108, no. 4, pp. 459–480, Oct. 2019.[12] E. Rap and M. T. Oré, “Engineering Masculinities: How Higher Education Genders the Water Profession in Peru,” Eng. Stud., vol
chemical engineer before, and mentorvideos and interactions helped them meeting with professional chemical engineers and seeingtheir future in them.Future WorkWe had collected both qualitative and quantitative data during three semesters ofimplementation. All data was cleaned, organized, coded individually and as a group. This data iscurrently being analyzed.AcknowledgmentsThis work was supported through the National Science Foundation’s funding under a PFE: RIEFGrant No. (2024960). Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the National ScienceFoundation’s views. We wish to thank survey and interview participants for their participation inthe
; less than 28% of the total IT workforceand only 12% of engineers are female [2]. By the time students reach college, 1 in 5 young menplan on majoring in engineering or computing while only 1 in 17 young women declare the same[3]. Since 1990, the percentage of female computing professionals dropped from 35% to about24% today, and if that trend continues, the share of women in the nation’s computing workforcewill decline to 22% by 2025 according to Girls Who Code [4]. These statistics provide themotivation for a program called Project-based Work Studio (PWS) developed at a mid-sizedAppalachian primarily undergraduate university supported by an NSF S-STEM grant to build amore proportionate female workforce in computer science, engineering, and
Foundation under Grant No.EEC 2144213. References[1] N. Hillman and T. Weichman, "Education deserts: The continued significance of “place” inthe twenty-first century," American Council on Education, Washington, DC, 2016.[2] M. Reyes, A. Dache-Gerbino, C. Rios-Agular, M. Gonzalez-Canche and R. Deil-Amen, "The“geography of opportunity” in community colleges: The role of the local labor market instudents’ decisions to persist and succeed," Community College Review, vol. 47, no. 1, pp. 31-52, 2019.[3] F. S. Laanan and D. Jain, "Advancing a new critical framework for transfer student research:Implications for institutuional research," New Directions for Institutional Research, vol. 170, pp.9-21, 2017.[4] S. S
found a noticeable but insignificant difference in scores. All calculations wereperformed using Microsoft Excel. Table 2. Summary of results. Mean Standard Shapiro-Wilk Mann-Whitney Result Duration (s) Deviation (s) Normality U test Normal Pre-COVID 149 84 Statistically (p>0.05) U=329 significant
Revolution to Industry 4.0: A Literature Review,” in 2020 ASEE Virtual Annual Conference Content Access Proceedings, Virtual On line, Jun. 2020, p. 35318. doi: 10.18260/1-2--35318.[4] S. R. Brunhaver, R. Korte, S. Barley, and S. Sheppard, “Bridging the Gaps between Engineering Education and Practice,” in U.S. Engineering in a Global Economy, University of Chicago Press, 2018, pp. 129–163. doi: 10.7208/chicago/9780226468471.001.0001.[5] K. Tonso, “Teams that work: Campus culture, engineer identity, and social interactions,” J. Eng. Educ., vol. 95, no. 1, pp. 25–37, 2006.[6] A. C. Loignon, D. J. Woehr, M. L. Loughry, and M. W. Ohland, “Elaborating on Team- Member Disagreement: Examining Patterned Dispersion in Team-Level Constructs