Paper ID #36751Using Academic Controversy in a Computer Science UndergraduateLeadership Course: An Effective Approach to Examine Ethical Issues inComputer ScienceMariana A. AlvidrezDr. Elsa Q. Villa, University of Texas, El Paso Elsa Q. Villa, Ph.D., is a research assistant professor at The University of Texas at El Paso (UTEP) in the College of Education, and is Director of the Hopper-Dean Center of Excellence for K-12 Computer Science Education. Dr. Villa received her doctoral degree in curriculum and instruction from New Mexico State University; she received a Master of Science degree in Computer Science and a Master of
research work is mainly focused on two areas, (a) designing novel materials for electronic and energy applications using ab-initio Density Functional Theory (DFT) which is imple- mented using Quantum espresso package (b). Designing computational tools for engineering education using Python/Matlab.Dr. Binh Q. Tran, Marian University Dr. Binh Q. Tran is the founding dean for the E.S. Witchger School of Engineering at Marian Univer- sity in Indianapolis. He has bachelor’s and master’s degrees in mechanical engineering from U.C. San Diego and San Diego State University, respectively, and received his doctorate in biomedical engineering from the University of Iowa. His research interests are related to applications of
engineering from Lehigh University in 19Dr. Laura P. Ford, The University of Tulsa LAURA P. FORD is an Associate Professor of Chemical Engineering at the University of Tulsa. She teaches engineering science thermodynamics and fluid mechanics, mass transfer/separations, and chemi- cal engineering senior labs. She advises TU’s chapter of Engineers Without Borders - USA. Her research is with the Delayed Coking Joint Industry Project.Dr. Tracy Q. Gardner, Colorado School of Mines Tracy Q. Gardner graduated from the Colorado School of Mines (CSM) with B.S. degrees in chemical engineering and petroleum refining (CEPR) and in mathematical and computer sciences (MCS) in 1996 and with an M.S. degree in CEPR in 1998. She then got
cybersecurity is beneficial. Sometimes, however, the call for diversity incomputing can be complicated, as diversity is a complex concept. While most of the research ondiversity in computing focuses on gender and race/ethnicity, some interpret diversity in otherways. Undergraduate students are stakeholders in the assessment of cybersecurity as a diverseand inclusive subfield of computing--as they may or may not consider these concepts as theymake curricular and career decisions. A goal of the study is to enrich our understanding ofdiversity perspectives in the field, and so we sought complexity of interpretation over anarrowing or codifying of viewpoints. Data for this piece come from three sources: Q-sortrankings, group interview transcripts, and
design their class.Among the multiple ways to reveal collaborative problem-solving processes, temporal submissionpatterns is one that is more scalable and generalizable in Computer Science education. In thispaper, we provide a temporal analysis of a large dataset of students’ submissions to collaborativelearning assignments in an upper-level database course offered at a large public university. Thelog data was collected from an online assessment and learning system, containing the timestampsof each student’s submissions to a problem on the collaborative assignment. Each submission waslabeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter orlonger than the 25th and 75th percentile. Sequential compacting and
considered except for motivation have a P-value greater than 0.05 for boththe Kolmogorov-Smirnov and Shapiro-Wilk tests. The normality plots (see appendix): Q-Q plotsand the box plots for all the variables show that the test fulfilled the normality assumptionoverall. Therefore, we assumed that the data fulfilled normality assumptions. Table 2: Normality test results Variables Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Course Learning Experience 0.102 51 0.200 0.986 51 0.802 Campus Facilities 0.116 51 0.083
SimulationsThe algorithm for microgrid optimization using the Q-learning [8] reinforcement learningtechnique was developed in MATLAB for the purpose of simulating the electrical microgridoptimal performance. The goal is to optimize the power flow in the network using the Q-learningtechnique. The microgrid configuration includes an islanded mode of operation, with aphotovoltaic array as a renewable power source and a diesel generator as the conventional powersupplier. The battery storage is available as well as a dumping load. Cost per kW, batterycapacity, size of diesel generator, learning rate, among others can be mentioned as theparameters that might be modified to test the algorithm. Real datasets associated with solarradiation [9] and electrical
results collected from a microphone andUSB data acquisition system and discuss any discrepancies. Table 1. We organized the learning objectives into three categories: enduring understanding (EU, highlighted), important to know and do (IKD), and worth being familiar with (WF). Topic Learning Objective Priority Assessment Critique data visualizations and descriptive statistics for HW1, 3, 5-7, 9, 11- EU clarity and appropriateness 13; P1-3; Q Data
industry. The repeated cycle of training new hires due to labor turnover may affectorganizational and project performance. Construction firms should seek tactical human resourcesinitiatives to attract new hires, develop old hires’ skills, and retain talent in their workforce. Thisstudy investigates the differences in human dimensions of individuals engaged on construction jobsites. The aim of this paper is to identify distinctive human dimensions of skilled trades workers,essentially required for job transition within the construction industry. This study adoptedHEXACO personality inventory, Emotional Intelligence, and Q-DiSC behavioral diagnostics todetermine personality trait differences and peculiarities between 133 project managers workingfor
historically minoritized groups. Both the surveyquestions that were used to study emerging themes of self-advocacy in the graduate students, andfocus group questions have been presented to the engineering education research community atconferences and one-on-one meetings to get feedback from the broader community on thethemes of self advocacy and the questions. The focus groups will be conducted in Summer 2023and all students in the GREATS program will be invited to participate.Table 1. Focus group questions Question 1: Q.1 Can you describe your graduate-program trajectory story? Why did Background, you choose to pursue a graduate degree in science/engineering? Why Motivation, and did you choose and/or apply to the
, industry or government collaboration, and/or travel.Discussion topics will also include process requirements of applying, conducting, anddocumenting the outcomes of the sabbatical.The suggested layout of the panel session is: • 5-minute introduction of panel topic and panelists • Overview of each panelist’s sabbatical activity (5 minutes each) • Brief whole group Q&A session to engage audience and panelists • Small group activities with documentation of Q&A: o What resources did you find helpful in planning your sabbatical? o What was the timeframe of planning, applying for, conducting, and documenting your sabbatical? o What were the requirements of your sabbatical
programs. Washington DC: National Academies Press, 2016.[2] R. F. Clancy and A. Gammon, “The Ultimate Goal of Ethics Education Should Be More Ethical Behaviors,” ASEE Annu. Conf. Expo. Conf. Proc., 2021.[3] P.-H. Wong, “Global Engineering Ethics,” in Routledge Handbook of Philosophy of Engineering, D. Michelfelder and N. Doorn, Eds. 2021.[4] Q. Zhu and B. Jesiek, “Engineering Ethics in Global Context: Four Fundamental Approaches,” in ASEE Annual Conference and Exposition, 2017, doi: 10.18260/1-2-- 28252.[5] R. F. Clancy and Q. Zhu, “Global Engineering Ethics: What? Why? How? and When?,” J. Int. Eng. Educ., vol. 4, no. 1, 2022, [Online]. Available: https://digitalcommons.uri.edu/jiee/vol4/iss1/4?utm_source
per fiscal year depending on their grant contribution. Typically, this funding alignswith the company’s philanthropic mission or community outreach goals, and also provides amechanism for employee volunteerism. Industry partners are highlighted throughout the eventand are often guest speakers. They have the option to invite engineers and other STEMprofessionals to interact with the students, serve as panelists for the Q&A session, and model theSTEM activity alongside the students. Everyone supporting the event goes through intensivevolunteer training where they learn their roles and responsibilities, receive access to the kitguides, and learn the science behind the STEM kit. This allows volunteers to better instruct thestudent
6.864 Liberal 11 47.27 5.985 11 47.09 5.127 Arts Other 31 44.42 9.186 31 49.84 6.272Levene’s test indicated equal variances, while residual Q-Q plots and histograms showedhomoscedasticity and normality assumptions were largely met. Exceptions to normality werefound in integrative learning post for females (kurtosis = -1.076), as well as teamwork post formales (kurtosis = 5.060). ANOVA is robust to violations of normality, however a kurtosis valueover +/- 2.0 is too much of a violation of normality, and as such cannot be used to analyze theinteraction of teamwork and gender.Similar to measuring each construct against gender, residual
,” Soc. Psychol. Q., vol. 63, no. 3, pp. 224–237, 2000.[7] D. Collins, A. E. Bayer, and D. A. Hirschfield, “Engineering Education For Women : A Chilly Climate,” Women in Engineering Conference : Capitalizing on Today’s Challenges - 1996 WEPAN National Conference. pp. 323–328, 1996.[8] L. K. Morris and L. G. Daniel, “Perceptions of a chilly climate: Differences in traditional and non-traditional majors for women,” Res. High. Educ., vol. 49, no. 3, pp. 256–273, 2008, doi: 10.1007/s11162-007-9078-z.[9] K. F. Trenshaw, “Half as likely: The underrepresentation of LGBTQ+ students in engineering,” CoNECD 2018 - Collab. Netw. Eng. Comput. Divers. Conf., no. 2011, 2018.[10] J. Jorstad, S. S. Starobin, Y. (April) Chen
. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-End Factor Analysis for Speaker Verification,” IEEE Trans. Audio Speech Lang. Process., vol. 19, no. 4, pp. 788–798, May 2011, doi: 10.1109/TASL.2010.2064307.[7] L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized End-to-End Loss for Speaker Verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2018, pp. 4879–4883. doi: 10.1109/ICASSP.2018.8462665.[8] C. Wang, A. Zhang, Q. Wang, and Z. ZHU, “Fully supervised speaker diarization,” US11031017B2, Jun. 08, 2021 Accessed: Apr. 09, 2023. [Online]. Available: https://patents.google.com/patent/US11031017B2/en[9] J. K. Bergum, “Text Emotion Prediction in Browser
. Nadeem, “STEM Jobs See Uneven Progress in Increasing Gender, Racial and Ethnic Diversity,” Pew Research Center Science & Society, Apr. 01, 2021. https://www.pewresearch.org/science/2021/04/01/stem-jobs-see-uneven-progress-in- increasing-gender-racial-and-ethnic-diversity/ (accessed Feb. 04, 2023).[2] “The STEM Gap: Women and Girls in Science, Technology, Engineering and Mathematics,” AAUW : Empowering Women Since 1881. https://www.aauw.org/resources/research/the-stem-gap/ (accessed Feb. 04, 2023).[3] J. Handelsman et al., “More women in science,” Science, vol. 309, no. 5738, Art. no. 5738, 2005.[4] S. E. Carrell, M. E. Page, and J. E. West, “Sex and science: How professor gender perpetuates the gender gap,” Q. J
Teaching Module to Improve Student Understanding of Stakeholder Engagement Processes Within Engineering Systems Design. 57–67. https://doi.org/10.1007/978-3-319-32933-8_6Friedman, B., & Hendry, D. G. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press. https://books.google.com/books?hl=en&lr=&id=8ZiWDwAAQBAJ&oi=fnd&pg=PR13&d q=value+sensitive+design+moral+imagination&ots=vchlHBMvLP&sig=FHupw7lAlTzwR _2hSj601EwARU8#v=onepage&q=value sensitive design moral imagination&f=falseFriedman, B., & Hendry, D. G. (2012). The Envisioning Cards: A Toolkit for Catalyzing Humanistic and Technical Imaginations. SIGCHI Conference on Human Factors in Computing
. 100-112, 2022.[12] O. Simpson, “Access, retention and course choice in online, open and distance learning”.European Journal of Open, Distance and E-learning, 7(1), 2004.[13] M. Scott, and D.A. Savage, “Lemons in the university: asymmetric information, academicshopping and subject selection”. Higher Education Research & Development, 41(4), pp. 1247-1261, 2022.[14] D. Bukhari, “Data science curriculum: Current scenario”. International Journal of DataMining & Knowledge Management Process, Vol. 10, 2020.[15] D. Li, E. Milonas, and Q. Zhang, “Content Analysis of Data Science Graduate Programs inthe US,” 2021 ASEE Virtual Annual Conference Content Access, 2021.[16] Z. Chen, X. Liu, and L. Shang, “Improved course recommendation algorithm
will inform future initiatives aimed at supportingthe academic journeys of female minority STEM students and ensuring their success.6.2 Initiative Two: ActivitiesActivity One: Panel Discussion and Q&A • Description: A panel discussion and Q&A session featuring minority female STEM professionals from various STEM disciplines will be organized. The objective of this panel is to allow these female STEM professionals to share the educational, professional and personal experiences, including the challenges faced and the successes achieved with female minority STEM students. • Goal: This event will provide female minority students with the opportunity to see themselves represented in the STEM fields and
Exact Test is used with twonominal variables to find out if the proportions from one variable are different among values ofthe other [Bind & Rubin, 2020]. Due to the test's exact nature, it is more accurate than a Chi-SquareTest alone.Figure 1. Items A, G, and M relate to aspirational capital. Items B, H, and N relate to linguistic capital.Items C, I, and O relate to familial capital. Items D, J, and P relate to social capital. Items E, K, and Qrelate to navigational capital. Items F, L, and R relate to resistance capital. Additionally, Items A, B, C,D, E, and F relate to having or holding a CCW capital dimension. Items G, H, I, J, K, and L relate to adeveloping CCW capital dimension. Items M, N, O, P, Q, and R relate to not having a CCW
was run to examine if LCDLMs offered differentialbenefits or effects based on the gender of participants. Four modes of engagement were assessed:Interactive, constructive, active, and passive scores. Participants were grouped by their gender:male and female. First, we checked preliminary assumptions, and results revealed that data wasnormally distributed, as assessed by inspecting the Normal Q-Q plots. There were no univariateand multivariate outliers, as assessed by boxplot; there were linear relationships, as evaluated byscatterplot, and no multicollinearity; and variance-covariance matrices were homogeneous, asassessed by Box’s test of equality of covariance matrices (p = 0.473); variances werehomogeneous, as assessed by Levene’s Test of
development of research self-efficacy in NHERI-REU participants, apre- and post- assessment was administered. A paired-samples t-test was used to determinewhether there was a statistically significant mean difference between the pre and post researchself-efficacy of REU participants. While outliers were detected (question pairs 1,5, 11, 13, and20) that were more than 1.5 box-lengths from the edge of the box in a boxplot, inspection of theirvalues did not reveal them to be extreme, and they were kept in the analysis. Since there weremore than 50 participants, the Normal Q-Q lot method was used to analyze and demonstrate thatthe difference score between question pairs was approximately normally distributed for allquestions. Further, paired samples t
, pp. 573–593, Dec. 2019, doi: 10.1007/s12564-019-09580-6.[3] S. E. Dempsey, “Critiquing community engagement,” Manag. Commun. Q., vol. 24, no. 3, pp. 359–390, 2010, doi: 10.1177/0893318909352247.[4] T. D. Mitchell, “Traditional vs. Critical Service-Learning: Engaging the Literature to Differentiate Two Models,” Mich. J. Community Serv. Learn., vol. 14, no. 2, pp. 50–65, 2008.[5] M. Checker, “‘But I Know It’s True’: Environmental Risk Assessment, Justice, and Anthropology,” Hum. Organ., vol. 66, no. 2, pp. 112–124, Jun. 2007, doi: 10.17730/humo.66.2.1582262175731728.[6] R. Holifield, “Environmental Justice as Recognition and Participation in Risk Assessment: Negotiating and Translating Health Risk at a Superfund
to abandon the type of instructions that requires a passive role from them [23].This is discussed further in Figure 4 at the end of this section.Table 2Questions associated with type of instruction. Items with Description Factor Item significant difference Type of instruction Active e, f, m, o, p, q e, f, m, q Type of instruction
Electrical Engineering. They presented the relation between pressure, flow rates, and designing a dam and solar panels for renewable energy.The second mini project: Each group picked their topic from Table 1. They were required toprepare a short 5–10 minutes presentation that included a title slide with the group’s info, aproblem statement slide, research and solution for the chosen topic, and a final slide for Q&A.The topics were broad in different areas to cover all the majors in the class. The objective of thisassignment was to familiarize students with the diverse real-world applications ofThermodynamics and to help them understand the significant challenges faced by our planet,climate, and emerging technologies. Through this
and M. Talha, “Turnitin: Is it a text matching or plagiarism detection tool?,”Saudi J. Anaesth., vol. 13, no. Suppl 1, pp. S48–S51, Apr. 2019, doi: 10.4103/sja.SJA_772_18.[6] E. Eckel, “Textual Appropriation and Attribution in Engineering Theses andDissertations: An Exploratory Study,” in 2014 ASEE Annual Conference & ExpositionProceedings, Indianapolis, Indiana: ASEE Conferences, Jun. 2014, p. 24.1184.1-24.1184.16. doi:10.18260/1-2--23117.[7] D. Simpson, “Academic dishonesty: An international student perspective,” High. Educ.Polit. Econ., vol. 2, no. 1, Art. no. 1, Apr. 2016, doi: 10.32674/hepe.v2i1.22.[8] Y. (Helen) Zhang, H. Lin, X. Zhang, and Q. Ye, “The next steps in academic integrity —education, awareness, norms
targetingHS counselors in Prince William and Loudoun counties, where most data centers are located, 2)emphasizing hands-on or active participation, 3) encouraging building professional networks.Table 1 provides an overview of the externship. Component Description Micron Technologies Tour Tour of advanced chip manufacturer Micron Technologies, In-person, 4 hours including clean room, power & electric, and water systems. Tour concludes with a Q&A from Micron recruiters and educational coordinators. Stack Infrastructure Tour Tour of boutique data center Stack Infrastructure, with an In-person, 4 hours
, pp. 17–33.[6] S. T. Fiske, “Controlling other people: The impact of power on stereotyping,” American Psychologist, vol. 48, no. 6, pp. 621–628, 1993.[7] E. A. Patall et al., “Student autonomy and course value: The unique and cumulative roles of various teacher practices,” Motiv. and Emotion, vol. 37, no. 1, pp. 14–32, Mar. 2013.[8] K. E. Barron and C. S. Hulleman, “Expectancy-value-cost model of motivation,” in International Encyclopedia of the Social and Behavioral Sciences, J. D. Wright, Ed. Amsterdam: Elsevier Ltd., 2015, pp. 503–509.[9] E. Q. Rosenzweig, A. Wigfield, and J. S. Eccles, “Expectancy-value theory and its relevance for student motivation and learning,” in The Cambridge Handbook of