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
topics revolving around game-based training and Virtual Reality (VR) applications. Fields of expertise and study are game development and algorithms, cutMr. Nicholas WallaDr. Chenn Q. Zhou, Purdue University Northwest Dr. Chenn Zhou is the founding Director of the Center for Innovation through Visualization and Simula- tion (CIVS), established in 2009, and the Steel Manufacturing Simulation and Visualization Consortium (SMSVC), established in 2006. She is the Professor of Mechanical Engineering at Purdue University Northwest, and also Professor by Courtesy at Purdue University West Lafayette. Dr. Zhou received her B.S. and M.S. degrees in power engineering from Nanjing University of Aeronautics and Astronautics, China
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
. If it fails, one will go back to the first step and build a new model [8].The diagnosis section consists of the estimation of Q-matrices and using these Q-matrices toprovide insight into the dependency between the variables of BoT and the TC. In this paper, weused the GDINA function from the CDM package [9], [10] to retrieve the delta matrices that areessential to the estimation of the Q-matrices. The initial Q-matrix given to the GDINA functionis always 1J x K. Both the Lasso and the Truncated L1 penalty (TLP) terms were used as tuningparameters to retrieve the delta matrices which were then converted to Q-matrices following asimilar expectation–maximization (EM) algorithm in [11]. We also used our experience to comeup with one expert
function L as the differencebetween the robot’s kinetic energy K and potential energy P , which are functions of the robot’s T Tconfiguration q = θ1 , θ2 and velocities q˙ = θ˙1 , θ˙2 : L(q, q) ˙ − P (q) ˙ = K(q, q) (1)To derive the Euler-Lagrange equations, the partial derivative of the Lagrangian with respect toeach of the generalized coordinates qi , i = 1, 2 are calculated, and the time derivative of the partialderivative of L with respect to the velocities q˙i are taken. The resulting expressions are set equal tothe
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
executed to ensure safe and ethical treatment for the respondents, as theyare treated as a subject. To follow IRB ethics, the interview and the discussion were confidentialand completely voluntary. The interview started by distributing a sheet asking their job title andfour questions about their company: What is the number of employees in your company? What isyour company type? Which sector does your company work in? For how long has your companybeen using BIM? Following that, a 1-hour panel discussion started, and the interviewer askedquestions about the implementation of BIM in their company. Some of the questions includedthe following (Table 1).Table 1: Interview and discussion questions Question Question Number Q.1 Which solutions does
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
steps with questions (Q) and answers (A). Whether it is the instructor/ laboratorytechnician or the student asking the questions or providing the answers does not matter – it is theactive conversation and engagement that matters.1. Turn off main breaker to start. Q: What is a closed circuit? A: A closed circuit is like a circular road with a drawbridge over a river. When the bridge is down, the roadway is complete (closed), and cars can flow on the roadway. When the drawbridge is up, traffic stops. A closed circuit similarly allows electrical energy to flow along the wire – a current. For example, when a light switch is in the off position, the drawbridge is up, but when it is in the on position, the drawbridge is down. The circuit
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
condenser coil. As thetemperatures rise, the system can transfer the excess heat to a thermal battery. A thermal batteryis a device that stores energy [5]. For this, water could be used for testing (explored later). Thissystem could be tested with major appliances such as refrigerators and ovens/stoves, with theidea being to store and redistribute the energy needed to heat the water.The potential energy that can be harnessed can be represented by the heat transfer equation Q = A × G × η × ΔTQ(BTU) = the amount of heat gathered and storedA = the surface areaG = solar irradiance (BTUs/hour)η = the efficiency of the solar paneΔT = the temperature difference between the panel temperature and the storage
the designated amount, mark the pressure read from the digital pressure gage. 7. Once the pressure is marked, collect the wastewater in a graduated beaker for 20-30 seconds. Accuracy is important for volume-flow rate calculation. 8. With the wastewater collected, turn off the hose and allow the source tank to empty. 9. Repeat steps 1-8 two more times with the wastewater valve turned to different angles. 10. Measure elevation head at point 2Calculations: 1. Calculate the volume flow rate (Q) 𝒑 2. Read pressure head using digital pressure gage and calculate the pressure head 𝜸 3. Find velocity by using formula Q = 𝒗 * A
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
windows are available to a given user at a single time,allowing an individual to analyze multiple features of submitted data simultaneously.As previously noted in the Related Work section of this paper, if a resource were to behaveunresponsively, the system scheduler would purge the task on the resource, providing an errormessage for user guidance (alongside additional messages if a GRC file were to fail atcompile-time, runtime, etc.) and enabling re-submission to an alternate resource. (a) Three-channel time sink I/Q data - GRC (b) Three-channel time sink I/Q data - Our system
. Chapter 29, pp. 929-950Appendix 1. CHEMICAL ENGINEERING WORD PUZZLEBy Joaquin Rodriguez and Lisa Marie Huff, University of Pittsburgh D Y U S S E C O R P N B S S V R R W V X M V V N M D Q T A X C Q V O E M E O A H C O E O E S S E N I N E A N N H S J L O P B D S T P R G M V C V E E S C J N N T L E I C F Z O P T N R S N S I D F T I Q L G U E W K A I G K S R S M F C R M T I N D G Z N D Y S R
: x¨ θ¨ = (6) ℓSubstituting Eq. 6 into the moment equation for dynamic case will result in g x¨ = (x − u) (7) ℓ pLet q be a non-dimensitonalized variable where q = (ℓ/g) t. This simplifies Eq. 7 into: x¨ = (x − u) (8)where x is differentiated with respect to q. Both Eq. 7 and 8
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
See detailed view I J I P Q R Figure 2: Detail of PHX assembly (front view). Labels reference Table 1. I I IH HJ J V
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
course learning outcomes [1].From the students’ perspective, review sessions serve as an opportunity to learn about the examformat and get a general understanding of the types of questions they will be expected to answeror the types of problems they will be expected to solve. On the other side of the classroom, for aninstructor, exam reviews may feel like a tedious and redundant exercise, where one is expectedto regurgitate topics already covered in detail and to solve a series of example problems teasinglysimilar to what might appear on the exam.Another approach to exam reviews is hosting a question-and-answer (Q&A) session withstudents without a set agenda. This approach usually leads to disastrously low classroomparticipation and
comfortable they felt about certain topics or situations on aLikert scale of 1-5. The content domains were then mapped and correlated to the dimensions ofglobal competence from the PISA framework, shown in Table 1.Table 1: Mapping Content Domains to Global Competency Dimensions from the PISA global competencyframework Q# Content Domain Dimension of Global Competence 1 Self-efficacy regarding global issues 1 2 Awareness of global issues 1 3 Perspective-taking 2 4 Respect for people from other
suggests fillet radii and key chamfer dimensions [1]. Peterson [8] refers totypical fillet radii of r/d = 1/48 in, where d is the shaft diameter. In either source, thesedimensions do not correspond to common radii on bull nose end mills.Shaft Failure at KeyseatKeyseats are a common point of failure for shafts because they act as stress raisers. In powertransmission, a shaft essentially carries an alternating stress due to bending and a mean stress dueto torsion.Notch SensitivityThe effective stress in a part is typically lower than what one would predict from theoretical stressconcentration. The effective fatigue stress concentration is given by, Kf = 1 + q(Kt − 1) (1
face-to-face class and permanentlyimplemented into ME curriculum as a core graduate course taught during regular semesters.Students from any engineering or science department who need to predict microstructureevolutions or understand process-structure-property relationships of materials will benefit fromthis class. The present course development approach can also be adjusted for the development ofother online numerical modeling and analysis courses.References1. L.-Q. Chen, “Phase-field models for microstructure evolution”, Annual Review of Materials Research, 32(1), 2002, 113-140.2. L. Chen, C.O. Yenusah, Y.-Z. Ji, Y.-C. Liu, T.W. Stone, M.F. Horstemeyer, and L.-Q. Chen, “Three-dimensional phase-field simulation of γ” precipitation