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
presented at the conference. In addition, the review of criticalincidents related to RQ2 is ongoing. Future work pertaining to RQ2 will include (1) continuingextracting incidents for all remaining participants, (2) sorting incidents into current themes andcategories, and, as appropriate, defining new themes, and (3) disseminating results in a scholarlyjournal. Finally, RQ3 will seek to identify how Phase 1 and 2 results align with extant theoriesand frameworks utilized in engineering education.Acknowledgement:This material is based upon work supported by the National Science Foundation under Grant No.1737303. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily
also gratefully acknowledge the students, facilitators, courseparticipants, the University of Geneva (InZone) as a research and academic partner, and UNHCRas an implementing partner.References[1] A. S. Mahamud et al., “Epidemic cholera in Kakuma Refugee Camp, Kenya, 2009: the importance of sanitation and soap,” J. Infect. Dev. Ctries., vol. 6, no. 03, pp. 234–241, Nov. 2011, doi: 10.3855/jidc.1966.[2] M. Al-Addous, M. N. Saidan, M. Bdour, and M. Alnaief, “Evaluation of Biogas Production from the Co-Digestion of Municipal Food Waste and Wastewater Sludge at Refugee Camps Using an Automated Methane Potential Test System,” Energies, vol. 12, no. 1, p. 32, Dec. 2018, doi: 10.3390/en12010032.[3] P. Dankova and C
: Discuss. The instructor discusses the RL problem, how the engineer would use math and statistics to address the problem, and the impact of the RL problem on the society and community. If the instructor can also demonstrate the instruments used for data collection, then use of such instruments in the RL problem will be covered. (2) S: Solve. The students work on the problem, they device a plan to solve the problem, and implement their plan. This would follow Polya’s four-step method of solving mathematical problems, thus reinforcing that concept. (3) R: Reflect. Students reflect on the problem, and they use information related to the RL problem to check if the values calculated are reasonable
. References[1] N. Duval-Couetil, E. C. Kisenwether, J. Tranquillo, and J. Wheadon, “Catalyzing the adoption of entrepreneurship education in engineering by aligning outcomes with ABET,” in ASEE Annual Conference & Exhibition, 2014.[2] J. Gandhi and D. S. Deardorff, “An Implementation of Innovative Thinking in The Entrepreneurship Cur- riculum for Engineers An Implementation of Continuous Improvement in Instilling Innovative Thinking in The Entrepreneurship Curriculum for Engineers,” in ASEE Annual Conference & Exhibition, 2014.[3] J. F. Sullivan, L. E. Carlson, and D. W. Carlson, “Developing Aspiring Engineers into Budding Entrepreneurs : An Invention and Innovation Course,” J. Eng. Educ., no. October
momentum, angular momentum, total mechanical energy, orbital elements Satellite Subsystems overview Overview of electrical power system (EPS), on-board computing (CDH),Week 1 communications (TT&C), attitude determination & control (ADCS), structural and thermal (S&T), ground communication, payload systems Systems Engineering overview PNMSat systems engineering approach, requirements flowdown, mission mapping, N2 chart, components, interfaces, tasks, mission profile, circuit schematics, power
multicultural curriculum predict current attitudes and activities," Journal of College Student Development, vol. 51, no. 4, pp. 385-402, 2010.[12] P. Gurin, "Expert Report. "Gratz et al. v. Bollinger, et al." No. 97-75321 (E.D. Mich.); "Grutter, et al. v. Bollinger, et al." No. 97-75928 (E.D. Mich.)," Equity & Excellence in Education, vol. 32, no. 2, pp. 36-62, 09/01/ 1999.[13] S. Hurtado, "Linking diversity and educational purpose: how diversity affects the classroom environment and student development," in Diversity Challenged: Evidence on the Impact of Affirmative Action, G. Orfield, Ed. Cambridge, MA: Harvard Education Publishing Group, 2001, pp. 187-203.[14] C. Herring, "Does diversity pay?: Race
computational modeling activities areintegral to each educational learning module. When students formulate computational models,they develop understanding by engaging in the theory and observations of a situation. Studentscomplete each educational learning module in about three hours outside of class after they havebeen introduced to the individual topic in lecture(s) and completed a series of homeworkproblems. As students complete an activity, they are encouraged to refer to its correspondinggrading rubric, which conveys expectations of quality across different levels of expertise. Ourpedagogical model can be used to design learning modules for difficult concepts in other STEMsubjects.Keywords: cognitive apprenticeship, pedagogical model, engineering
Differences and the Differences They Make” Journal of Technology Transfer, 31, 325–333, 2006.[2] C. Corbett, & C. Hill. “Solving the equation: The variables for women’s success in engineering and computing”. Washington, DC: American Association of University Women, 2015.[3] L. Babcock, L., & S. Laschever, “Women don’t ask: The high cost of avoiding negotiation and positive strategies for change”. New York, NY: Bantam Books, 2007.[4] C A. Moss-Racusin, J. F. Dovidio, V. L. Brescoll, M. J. Graham, & J. Handelsman, “Science faculty’s subtle gender biases favor male students”. Proceedings of the National Academy of Sciences, vol. 109, no. 41, pp. 16474–16479, 2012.[5] S. Cheryan, S. A. Ziegler, A. K. Montoya, and L. Jiang
Paper ID #27140Impact of a Research Experience Program in Aerospace Engineering on Un-dergraduate Students: Year TwoDr. Jacques C. Richard, Texas A&M University Dr. Richard got his Ph. D. at Rensselaer Polytechnic Institute, 1989 & a B. S. at Boston University, 1984. He was at NASA Glenn, 1989-1995, worked at Argonne National Lab, 1996-1997, taught at Chicago State University, 1997-2002. Dr. Richard is a Sr. Lecturer & Research Associate in Aerospace Engi- neering @ Texas A&M since 1/03. His research is focused on computational plasma modeling using spectral and lattice Boltzmann methods for studying
scalefrom 1, very inaccurately, to 7, very accurately. A higher score in each personality trait shows thestudent’s personality is strong in that trait.Grit: This construct was proposed by the psychologist Angela Duckworth and is defined as thepassion and perseverance for long-term goals.13 Grit is usually unrelated or inversely related tointelligence or talent. The two subcategories of grit are consistency of interest and perseverance ofeffort. Perseverance of effort is a superior predictor of GPA while consistency of interest is a betterpredictor of number of lifetime career changes.14 Undergraduates who scored higher in Grit alsoearned higher GPAs than their peers despite having lower SAT scores.13 The Grit-S (short gritscale), which is comprised
outreach with underrepresented groups in STEM.Dr. Lauren Anne Cooper, California Polytechnic State University, San Luis Obispo Lauren Cooper earned her Ph.D. in Mechanical Engineering with a research emphasis in Engineering Education from University of Colorado Boulder. She is currently an Assistant Professor in Mechanical Engineering at California Polytechnic State University in San Luis Obispo. Her research interests include project-based learning, student motivation, human-centered design, and the role of empathy in engineering teaching and learning.Dr. Trevor Scott Harding, California Polytechnic State University, San Luis Obispo Dr. Trevor S. Harding is Professor and Department Chair of Materials Engineering at
study focused primarily on short-term outcomes that were specific to relevantcoursework and content, which limits the types of conclusions that can be drawn. Future researchshould explore relevant dynamics in greater detail, including the longer-term effects from suchexperiences, outcomes that extend well beyond the scope of pair programming, the conditionsunder which cross-national groupwork is most effective, and the ways in which these findingsmay or may not be similar for other forms of collaborative learning (e.g., problem-basedlearning, jigsaw classrooms). Qualitative, quantitative, and mixed-method research designswould be helpful for providing an in-depth understanding of these issues.References[1] S. Freeman, S. L. Eddy, M. McDonough
by National Science Foundation Experiential Learning for Emergingand Novel Technologies (ExLENT), Award No. ITE- 2322532. References:1. Teaching Critical Skills in Robotic Automation: iR-Vision 2D Course in Robotic Vision Development and Implementation, A. Sergeyev, S. Parmar, N. Alaraje, Technology Interface International Journal, 013-T-16, V17, #2, p. 13, 2017.2. Robotics and Automation Professional Development Workshop for Faculty, A. Sergeyev, N. Alaraje, Technology Interface International Journal, V17, #1, p.99, 2016.3. University, Community College and Industry Partnership: Revamping Robotics Education to Meet 21st Century Workforce Needs, A. Sergeyev, N. Alaraje, S. Kuhl
theyare more capable of performing a task. In this vein, constructive feedback plays a crucial role indeveloping strong self-efficacy beliefs. The fourth source of self-efficacy beliefs is emotionalarousal. Emotional arousal, that happens during challenging situations, can also help peopleinform themselves of their expectations of self-efficacy. High levels of emotional arousal canhamper an individual’s performance by increasing anxiety and stress.3. Research Question(s)This type of research, called sequential explanatory mixed-methods research, is practical in itsapproach. The research questions play a crucial role in guiding and shaping the entire process,including choosing the research design, determining the sample size, and selecting
regarding the eligibility of ChatGPT as an author [31], [32]. These ethicalconcerns play a valuable role by offering opportunities to steer the implementation of GAI inethically responsible ways.Research Questions a) What are students’ and instructors’ perceived literacy of GAI (e.g. knowledge, skills, and abilities)? b) How do students and instructors experience the usefulness and effectiveness of GAI in their course(s)?Theoretical FrameworkThere are many theoretical lenses that one can consider when investigating the experiences ofstudents and instructors using GAI. This paper is primarily interested in the participant literacyregarding GAI and their perceived usefulness and effectiveness of the technology. To explorethis, we