cooperative learningreduction strategies. teams on student achievement and race relations: This review of the literature is the beginning of a larger Treatment by race interactions," Sociology of Education,project focused on creating fairer peer assessments by pp. 174-180, 1981.teaching students techniques to address their own biases. Withthis knowledge of where bias exists and the strategies used to [8] L. Springer, M. E. Stanne, & S. S. Donovan, “Effects of small-group learning on undergraduates in science,mitigate it, the research team will develop a comprehensive
Chicago Legal Forum, 140, pp. 139-167, 1989.[2] E. Pascarella, L. S. Hagerdorn, E. Whitt, P. M. Yeager, M. I. Edison, P. T. Terenzini, A. Noura, “Women's Perceptions of a "Chilly Climate" and Their Cognitive Outcomes during the First Year of College,” Journal of College Student Development, 38(2).[3] M. Ong, C. Wright, L. Espinosa, and G. Orfield, “Inside the Double Bind: A Synthesis of Empirical Research on Undergraduate and Graduate Women of Color in Science, Technology, Engineering, and Mathematics,” Harvard Educational Review, 81(2), pp. 172–209, Summer 2011.[4] A. Bandura, “Self-efficacy,” in Encyclopedia of Human Behavior, V. S. Ramachaudran, Ed. New York: Academic Press, 1994, pp. 71-81
% 60% 40% 18% 15% 20% 0% CIT 12000 CIT 21400 Agree Neither agree or disagree Table 7: Perceptions of Mentees towards Mentoring CIT 12000 CIT 21400 The mentors gave me the sense that s/he and I The mentors modeled how to overcome challenges shared similarities in the background. and reach personal goals. The mentors helped me explore resources to The mentors showed me how to treat failed succeed academically. attempts as a
establish remarkable footprints and make an impact that matters. Simul- taneously, Daniel is the CEO of an EdTech start-up. Prior to joining FIU, Daniel had worked in Dubai for the ministry of Education as a STEM Educator and Lead Instructor. Previous work experience was in the United Kingdom (as an assistant Lead manager) and Nigeria. To date, he has co-authored 2 journal articles, authored 2 Physics textbooks, held many leadership roles and won several awards (one notable one is a World Bank award).Dr. Bruk T. Berhane, Florida International University Dr. Bruk T. Berhane received his bachelorˆa C™s degree in electrical engineering from the University of Maryland in 2003. He then completed a masterˆa C™s degree in
Sacramento State and by an NSF grant (DUE # 2235774).References [1] C. L. Dym, A. M. Agogino, O. Eris, D. D. Frey, and L. J. Leifer, “Engineering design thinking, teaching, and learning”, J. Eng. Educ., vol. 94, no. 1, pp. 103–120, Jan. 2005. [2] S. Rodenbusch, et al. “Early engagement in course-based research increases graduation rates and completion of science, engineering, and mathematics degrees,” CBE life sciences education, vol. 15, 2016, doi:10.1187/cbe.16-03-0117. [3] C. D. Wilson, J. A. Taylor, S. M. Kowalski, and J. Carlson, “The relative effects and equity of inquiry-based and commonplace science teaching on students’ knowledge, reasoning, and argumentation,” J. Res. Sci. Teach., 2009. [4] C. Katie, M. Blum Michelle, M. Julie, and S.-C. C
, alsohave the highest level of tentativeness in the LIWC analysis, suggesting that their leadership isexpressed in a way that invites others’ input. The GCA analysis (Fig. 4) is somewhat at variancewith the others, suggesting that S1 and S4 are the greater participators. The overallresponsiveness scores are very similar for all team members, but the social impact scorescorroborate the observation that S3 seems disempowered.Figure 3 - Scores for each member (S1-S4) of each team for each of the three LIWC constructs. Theresults for team F22 are skewed by S4’s very small number of utterances.Figure 4 - Scores for each member (S1-S4) of each team for each of the three GCA constructs. The resultsfor team F22 are skewed by S4’s very small number of
Hispanic Higher Education, 20(3), 297-312. 4. Prescott, A., Coupland, M., Angelini, M., & Schuck, S. (2020). Making School Maths Engaging: The Maths Inside Project. Springer. 5. Tobias, S. (1998). Anxiety and mathematics. Harvard Education Review, 50, 63–70. 6. Balfanz, R., & Byrnes, V. (2006). Closing the mathematics achievement gap in high- poverty middle schools. J. of Ed. for Students Placed at Risk, 11(2), 143-159. 7. Rowan‐Kenyon, H. T., Swan, A. K., & Creager, M. F. (2012). Social cognitive factors, support, and engagement: early adolescents’ math interests as precursors to choice of career. The Career Development Quarterly, 60(1), 2-15. 8. Bursal, M., & Paznokas, L. (2006). Mathematics
Kristine Denman is the Director of the New Mexico Statistical Analysis Center. She has over 20 years of experience in both applied research and program evaluation, including multiple evaluation projects focused on STEM internship experiences. ©American Society for Engineering Education, 2023An Engineering/Computer Science Project with Community Service FocusAbstract:This conference paper informs about a S-STEM (Scholarships in STEM) project awarded to theUniversity of New Mexico (UNM) School of Engineering (SOE). This NSF project is focused onproviding scholarships to students with merit who also demonstrate financial need. Thisparticular NSF project was focused on professional development activities as well as
Alliance (NCIIA). EMSresearch continued with support from the National Science Foundation (grant number 1636442).References[1] M. J. Fernandez, J. M. Trenor, K. S. Zerda and C. Cortes, "First generation college studentsin engineering: A qualitative investigation of barriers to academic plans.," in IEEE 38th AnnualFrontiers in Education Conference, Saratoga Springs, NY, 2008.[2] J. M. Trenor, S. L. Yu, W. C. L. and K. S. Zerda, "Influences for selecting engineering:Insights on access to Social Capital from two case studies.," in IEEE 38th Annual Frontiers inEducation Conference, Saratoga Springs, NY, 2008.[3] J. M. Trenor, " A phenomenological inquiry of the major choice processes of an overlookeddemographic: First generation college students in
failure will cause maximum degradation of network clustering. Further investigation willhave to be done for a 100 and 500 node network. Table for Notations N Number of vertices/nodes (N = |V |) M Number of edges/links (M = |E|) du The degree of u N (u) The set of neighbors of u T (u) The number of triangles containing u C (u), C (G) Clustering coefficients of u and G C˜v (u), C˜v (G) G Clustering coefficients of u and G after removing node v from G[S] The sub-graph
the whole program - tend to fail 5, 8.Charney and Libecap 9 assessed impact of entrepreneurship education and found that theeducation produces self-sufficient and innovative enterprising individuals.Simpeh10 examines various entrepreneurship theories including psychological entrepreneurshiptheories. The psychological theories highlight personal characteristics that defineentrepreneurship. Simpeh has included “traits theory” and “need for achievement theory” in thepaper 10. The “trait theory” hypothesizes that an individual has inborn qualities or potentials thatnaturally make him an entrepreneur. The issue with the trait model is that, there is no consistentevidence of unique entrepreneurial characteristics 11. Simpeh also quotes McClelland‟s
with real-world examplesas compared to theoretical examples traditionally employed in introductory engineering graphicscourses.This material is based upon work supported by the National Science Foundation under Grant No.1725874. 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] J. V. Ernst, T.O. Williams, A. C. Clark, and D. P. Kelly, “Psychometric properties of the PSVT:R Outcome Measure: A preliminary study of introductory engineering design graphics,” in 70th EDGD Midyear Conference Proceedings, Daytona, FL, USA, January 24-26, 2016.[2] S. A. Sorby and B. J. Baartmans
selection paths based on specific class or numerical valueof selected parameter (e.g., final test score). Each node represents a splitting rule for one specificattribute (e.g., answer to a test question). This analytic tool has as well the option to reducepredictive errors by searching for an optimal decision-tree development, according to a specifiedcriterion [12].The objective in this study is to search for dominant factors that predict positive test scoreimprovement when comparing pre-intervention to post-intervention evaluation of students’spatial visualization skills. Another goal is to identify influential test question(s) and/ordemographic factors that will move the predictive modeling efforts into a broader identificationand grouping of
morewomen into the program. Results of these efforts and other success stories will be reported infuture.AcknowledgementThe project is funded by the NSF’s EEC Program. We would also like to thank NorthropGrumman Corporation and NASA Armstrong Flight Research Center for hosting the participantsand giving them a tour of their research labs and facilities. We would also like to thank NorthropGrumman Corporation, Lockheed Martin Corporation, and NASA AFRC for their continuedsupport of the Cal Poly Pomona’s UAV Lab.References1. Bhandari, S., Aliyazicioglu, Z., Tang, F., and Raheja, A., “Research Experience for Undergraduates in UAV Technologies,” Proceedings of American Society of Engineering Education Annual Conference, Salt Lake City, UT, 25-28
anxiety can bedevastating, as mathematics is an important part of life [3]. Math anxiety can cause individuals toavoid math or situations that require analytical and rational thought [4]. Several studies havebeen carried out with elementary and grade school students to learn more about human reactionto mathematics [5], yet fewer studies have been done on college students or later. Furthermore,the majority of these research studies on college students focus on first-year students enrolled ina mathematics course [3]. The current study of this paper is focused on a less studied populationof engineering technology students, and their tendency to engage in rational-analytical thoughtprocesses.Literature ReviewIn the 1970’s researchers indicated that
Paper ID #30680Engendering Community to Computer Science Freshmen through an EarlyArrival ProgramProf. Alark Joshi, University of San Francisco Alark Joshi is an Associate Professor in the Department of Computer Science at the University of San Francisco. He was a co-PI on the IDoCode project (http://coen.boisestate.edu/cs/idocode/) that led to a change in the landscape of computer science teacher preparation and education in the state of Idaho. Currently, he is a co-PI on the S-STEM proposal focused on engaging students in the local community to enable successful outcomes for them with respect to courses and internships/jobs
a STEM researchproject. National Science Foundation Middle/High School Student Attitudes Towards STEM (S-STEM) Survey [8] was used to assess the overall impact of the outreach program on the femalestudents’ self-confidence and motivation in pursuing future cross-disciplinary STEM careers.The results showed that the 21st Century skills related to critical-thinking, communication, andcollaboration was the section with the most radical improvement.Keywords: kinematics of mechanisms, protein kinematics, biomechanics, biochemistry, DNAnano-mechanismsIDEAL Online Summer Outreach Program Curriculum Plan and MethodsDuring the summer of 2019, mechanical engineering faculty and two undergraduate studentsfrom both NSM and ECS colleges offered a two
in the accommodations processStudents were asked about positive and negative experiences, and supportive or unsupportiveactions. Participants then had the opportunity to describe these experiences. Around 140 studentsresponded to questions about their experiences. 40 students described having a positiveexperience, while 67 students reported not having positive experiences in the accommodationsprocess. 22 students reported having negative experiences, while 104 students reported nothaving negative experiences in the accommodations process. These results are summarized intables 2-3.Table 2. Student positive experience(s) Theme n Example comments Emotional 23 “[Saying] I am there for you, take
; Medina‐Borja, A. (1999). The use of focus groups for minority engineering program assessment. Journal of Engineering Education, 88(3), 333-343. Ashford, S. N., Wilson, J. A., King, N. S., & Nyachae, T. M. (2017). STEM SISTA spaces. Emerging issues and trends in education, 3. Blosser, E. (2020). An examination of Black women's experiences in undergraduate engineering on a primarily white campus: Considering institutional strategies for change. Journal of Engineering Education, 109(1), 52–71. Brawner, C., Mobley, C., Lord, S. M., & Main, J. Fit, Faith, and Family: Counterspaces for Black Male Student Veterans in Engineering. Journal of Women and Minorities in Science and Engineering.Case, A. D., & Hunter, C. D. (2012). Counterspaces: A
, & A. W. Harrist (Eds.), Authoritative parenting: Synthesizing nurturance and discipline for optimal child development (pp. 11–34). American Psychological Association.Baumrind, D. (1996). The discipline controversy revisited. Family Relations, 45(4), 405-414. Bayati, N. (2023). Exploring Parental Factors That Influence Female Students STEM Major Choice: A Phenomenological Study Exploring Female STEM Students’ Experiences. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard university press. Chown, S. M. (1957). The formation of occupational choice among grammar school pupils. Thesis Ph. D., Liverpool University. Denson, C
thecamp.AcknowledgmentThe research team is very thankful for the support of the Texas Education Service Center ofRegion 20, the Charlotte Independent School District, and the USS Department of Agriculture.This research was supported by the intramural research program of the USS Department ofAgriculture, National Institute of Food and Agriculture, Women and Minorities in STEMProgram, award #: 2022-38503-37903. The findings and conclusions in this preliminarypublication have not been formally disseminated by the U. S. Department of Agriculture, andshould not be construed to represent any agency determination or policy.Reference[1] E. J. Haller and S. J. Virkler, "Another Look at Rural-Nonrural Differences in Students' Educational Aspirations," 1993.[2] M. S
Proceedings, 2018.[2] H. Xiao et al., “Are we in crisis? National mental health and treatment trends in college counseling centers,” Psychol Serv, vol. 14, no. 4, pp. 407–415, Nov. 2017, doi: 10.1037/ser0000130.[3] S. A. Wilson and J. H. Hammer, “Faculty Experiences with Undergraduate Engineering Student Mental Health,” In 2021 ASEE Virtual Annual Conference Content Access, 2021.[4] I. Jurewicz, “Mental health in young adults and adolescents-supporting general physicians to provide holistic care,” Clinical Medicine, vol. 15, no. 2, pp. 151–155, 2015.[5] C. J. Wright, S. A. Wilson, J. H. Hammer, L. E. Hargis, M. E. Miller, and E. L. Usher, “Mental health in undergraduate engineering students: Identifying facilitators
intervention. By leveraging these findings, educators, policymakers, and industrystakeholders can work collaboratively to strengthen the talent pipeline and drive innovation inthe semiconductor sector.References[1] A. Deichler, “Help Wanted: Manufacturing Sector Struggles to Fill Jobs,” SHRM, Jun. 2021,accessed: 2023-7-6. [Online]. Available: https://www.shrm.org/topics-tools/news/talent-acquisition/help-wanted-manufacturing-sector-struggles-to-fill-jobs[2] S. Alam, “Addressing the talent gap,” Accenture, Feb. 2023, accessed: 2023-6-30. [Online].Available: https://www.accenture.com/us-en/insightsnew/high-tech/semi-talent-shortage[3] C. Richard, K. Ramachandran, and I. Pandoy, Deloitte, “Looming Talent Gap ChallengesSemiconductor Industry,” Semi.org
N. Beard. "What do we teach when we teach tech ethics?: A syllabi analysis," in Proc. 51st ACM Tech. Symp. Comp. Sci. Educ. Portland, OR, USA, 2020, pp. 289-295.[2] B. C. Stahl, J. Timmermans, and B. D. Mittelstadt, "The ethics of computing: A survey of the computing-oriented literature," ACM Comp. Surv. (CSUR), vol. 48, no. 4, pp. 1- 38, 2016.[3] S. R. Komives, N. Lucas, and T. R. McMahon, Exploring Leadership: For College Students Who Want to Make a Difference, 3rd ed., San Francisco, CA, USA: John Wiley & Sons, 2009.[4] M. J. Quinn, “On teaching computer ethics within a computer science department,” Sci. and Eng. Ethics, vol. 12, pp. 335-343, 2006.[5] R. T. Johnson, D. R. Johnson
noted as one persistent attribute that students exhibit during theseexperiences. For instance, one aspect of Behroozi et al.’s work [7] compared anxiety levels thattheir participants exhibited while conducting mock technical interviews either in a public settingor in a private setting. It was determined that participants who conducted technical interviews ina public setting exhibited higher levels of anxiety than their counterparts who were in a privatesetting. Similarly, Hall and Gosha [23] conducted a study that measured the correlation ofanxiety and preparation in a technical interview that targeted junior and senior CS majors at aSoutheastern Historically Black College/University (HBCU) in the United States. Keyinformation collected during
impact on improving student understanding ofspecific course concepts. However, these results counter that of Leininger-Frézal andSprenger [19], who find the use of a VFT did help to enhance student understanding.Common between ours and Dada, et al. [15]’s results are a high percentage (>75%) ofagreement to the pre-survey statement, and thus it is more difficult to make a meaningfulimprovement on student understanding.Comparatively, the remaining 4 statements showed significant differences between pre- andpost-DST survey results (p < 0.05). Observations from Figure 3 in conjunction with this dataimplies the DST was ineffective in assisting students to develop problem solving skills,enabling teamwork, and improving their ability to
; Plaza, D. (in preparation). Sweetheart Deals: informal promotion practices that produce gendered and racialized workplace inequities in higher education, ADVANCE Journal.Davis, S., Nolen, S., Cheon, N., Moise E., & Hamilton E. (in review). Engineering Climate for Marginalized Groups: Connections to Peer Relations and Engineering Identity.Davis, S., Nolen, S, & Koretsky M. (in preparation A). Shifting Instructional Practices through Co-teaching: A CHAT Analysis of Organizational LearningDavis, S., Nolen, S, & Koretsky M. (in preparation B). Inclusive Excellence: Synergies Between Equity and Student Learning in PracticeEfu, S. I. (2019). Exams as learning tools: A comparison of traditional and collaborative assessment in
Center for Science and Engineering Statistics, “Women, minorities, and persons with disabilities in science and engineering: 2019,” https://ncses.nsf.gov/pubs/nsf19304/data, 2019, accessed: 2021-5-24. [4] H. S. Al-Khalifa, H. R. Faisal, and G. N. Al-Gumaei, “Teaching mobile application development in 20 hours for high school girls: A web-based approach,” in 2019 IEEE Global Engineering Education Conference (EDUCON), 2019, pp. 16–21. [5] Y. Chen, Z. Chen, S. Gumidyala, A. Koures, S. Lee, J. Msekela, H. Remash, N. Schoenle, S. Dahlby Albright, and S. A. Rebelsky, “A middle-school code camp emphasizing digital humanities,” in Proceedings of the 50th ACM Technical Symposium on Computer Science Education, ser. SIGCSE ’19. New York
of an underlying factor(s), indicating that factor analysis is possible. Bartlett’s test ofsphericity measures the hypothesis that the item correlation matrix is an identity matrix, whichrepresents that factor analysis is not possible as the items are unrelated. A significant test result (p< 0.05) rejects the null hypothesis, indicating that the data are factorable [25].The number of factors were then determined using a scree plot examination, Kaiser test, andparallel analysis [24]. The scree plot is a line plot of eigenvalue factors that shows the point atwhich extracting more factors does not explain more variance. The Kaiser method retains factorswith eigenvalues greater than 1 [24]. Parallel analysis helps determine meaningful factors
academicsettings, was found to be significantly (p<0.05) different for males and females. This isconsistent with the results from Rodriguez & Esparrago [21]’s study which used the intrinsicmotivation inventory to determine that male and female students have significant differences inhow they are motivated by choice. Their study, which used a pretest/posttest design to study theimpacts of a multinational design project on motivation, found that female students did notexperience a reduction in their choice score after the design project. It is possible that the resultsof the current study describe consistency in the female student’s motivational scores alongside adecrease in overall academic motivation for males although this cannot be determined for