Paper ID #30541How to Use Q Methodology in Engineering Education ResearchMs. Renee Desing, The Ohio State University Renee Desing is currently a PhD Candidate at the Ohio State University in the Department of Engineering Education. Ms. Desing holds a B.S. in Industrial Engineering from the Georgia Institute of Technology and a M.S. in Industrial Engineering and Operations Research from the Pennsylvania State University. Most recently, Ms. Desing worked as a managing consultant for IBM Public Sector Advanced Analytics.Dr. Rachel Louis Kajfez, The Ohio State University Dr. Rachel Louis Kajfez is an Assistant Professor in the
Paper ID #28894From Q&A to Norm & Adapt: The Roles of Peers in Changing Faculty Be-liefsand PracticeAmber Gallup, University of New MexicoDr. Vanessa Svihla, University of New Mexico Dr. Vanessa Svihla is a learning scientist and associate professor at the University of New Mexico in the Organization, Information & Learning Sciences program and in the Chemical & Biological Engineering Department. She served as Co-PI on an NSF RET Grant and a USDA NIFA grant, and is currently co-PI on three NSF-funded projects in engineering and computer science education, including a Revolutionizing Engineering Departments
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
of Excellence that ad- vances interdisciplinary education and research. She served on the Naval Research Advisory Committee (2016-2018) . Gates received the 2021 Alfredo G. de los Santos Jr. Distinguished Leadership Award, the 2015 Great Minds in STEM’s Education award, the CRA’s 2015 A. Nico Habermann Award, the 2010 Anita Borg Institute Social Impact Award, and the 2009 Richard A. Tapia Achievement Award for Sci- entific Scholarship, Civic Science, and Diversifying Computing. She was named to Hispanic Business magazine’s 100 Influential Hispanics in 2006 for her work on the Affinity Research Group model.Dr. Elsa Q. Villa, University of Texas at El Paso Elsa Q. Villa, Ph.D., is a research assistant
Paper ID #31526The CAHSI INCLUDES Alliance: Realizing Collective ImpactDr. Elsa Q. Villa, University of Texas at 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 Center for Education Research and Policy Studies (CERPS). 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 Arts in Education from UTEP. She has led and co-led numerous grants from corporate foundations and state and
effectiveness of a cognitive learning computer system in improving mathematical skills” in 2014 The Texas forum of Teacher Education and ”Bilingual students benefit from using both language” in the proceeding of the 2016 World conference of soft computing.Dr. 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 Center for Education Research and Policy Studies (CERPS). 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 Arts in Education from UTEP
. She received her Bachelors of Engineering from MIT. Her research focuses on the nontraditional engineering student – understanding their motivations, identity development, and impact of prior engineering-related experiences. Her work dwells into learning in informal settings such as summer camps, military experiences, and extra-curricular activities. Other research interests involve validation of CFD models for aerospace applications as well as optimizing efficiency of thermal-fluid systems.Dr. Cheryl Q. Li, University of New Haven Cheryl Qing Li joined University of New Haven in the fall of 2011, where she is a Senior Lecturer of the Industrial, System & Multidisciplinary Engineering Department. Li earned her
research focuses on the nontraditional engineering student – understanding their motivations, identity development, and impact of prior engineering-related experiences. Her work dwells into learning in informal settings such as summer camps, military experiences, and extra-curricular activities. Other research interests involve validation of CFD models for aerospace applications as well as optimizing efficiency of thermal-fluid systems.Dr. Cheryl Q. Li, University of New Haven Cheryl Qing Li joined University of New Haven in the fall of 2011, where she is a Senior Lecturer of the Industrial, System & Multidisciplinary Engineering Department. Li earned her first Ph.D. in me- chanical engineering from National
Paper ID #21777Investigating the Entrepreneurial Mindset of Engineering and Computer Sci-ence StudentsDr. Cheryl Q. Li, University of New Haven Cheryl Qing Li joined University of New Haven in the fall of 2011, where she is Associate Professor of the Mechanical and Industrial Engineering Department. Li earned her first Ph.D. in mechanical engineer- ing from National University of Singapore in 1997. She served as Assistant Professor and subsequently Associate Professor in mechatronics engineering at University of Adelaide, Australia, and Nanyang Tech- nological University, Singapore, respectively. In 2006, she resigned from
Paper ID #24674Broadening Participation of Hispanics in Computing: The CAHSI IncludesAllianceDr. 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 Center for Education Research and Policy Studies (CERPS). 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 Arts in Education from UTEP. She has led and co-led numerous grants from corporate
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
safety, and sustainable infrastructure.Mr. Edward Stephen Char Jr., Villanova University BS EE Villanova University 1996 MS EE Villanova University 1998Dr. John Komlos, Villanova University Page 26.27.1 c American Society for Engineering Education, 2015 ✁✂✄☎ ✁✂✆✄✝☎ ✁✂✞✟✂✠☎✠✡ ☛✠ ☞ ✌✄✂✍☎✎✡✏✑☞✝☎✒ ✓☛✄✝✡✏✔☎☞✄ ✕✠✖☛✠☎☎✄☛✠✖ ✗✘✙✚✛✜✚✢✣✚✤ ✥✦✚✛✦✜✚✧ ★✢✩ ✪✫✫✚✫✫✬✚✢✭✮✯✰✱✲✳✴✱✵✶✷✷✸✹✺✻✸ ✼✹✶✻✽✾✿✶❀❁ ✽❂❃✸✾❄✽❅ ✺✹ ✸ ✹✽❆ ❇✾✺❈✽❉❀❊❃✸✿✽❅ ✸❇❇✾✺✸❉❋ ●✺✾ ❀❋✽ ✾✽❍■✶✾✽❅ ●✶✾✿❀❊❁✽✸✾ ✽✹❏✶✹✽✽✾✶✹❏❑▲▼❑◆❖❑◗❑ ❖ ❘❙❙❚❯ ❱❲❖❳ ❑❨ ❩❖◆❳❱❬❭❑❪◆ ❑▲▼❑◆❖❑◗❑ ❨❪❳ ◆❑▼❫◆❱❑❴ ❖ ❱❲❑ ❘❙❵❙ ❛❜❝❝ ❛❞❪❡ ❢❫❩❑◆❑◗❑❣❤✐❥❦❦❧♠♥♦♣q rs❦ t
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
cross section of the questions of each survey.Table 3. A Sample of Survey Questions Current Secondary School Students survey Peer survey Q.4 When working through a Math problem, how Q.3 Are you currently a student in engineering? excited do you typically feel after you have solved it? (5=very excited, 1=very bored) Q.5 How interested are you in the way things work Q.4 If you are no longer a student, are you working (5=very interested, 1=not interested at all) in an engineering related field? Q.7 Have you ever learned about engineering? Q.7 Why did you choose to study engineering? Q.8 What do you think an engineer does in his/her
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
. Instructors’ names were abbreviated asRR, MS, and SF. RR’s course evaluation methods include attendance and participation (Att&P),assignments and quizzes (A&Q), two term exams & a comprehensive final exam (E), groupproject (P). For fall 2017 on campus: Att&P 15%, A&Q 30%, E 40%, and P 15%. For fall 2017online: Att&P 10%, A&Q 35%, E 50%, and P 10%. For the fall 2019 online: Att&P 10%, A&Q30%, E 45%, and P 15%. MS’s course evaluation methods include attendance and participation(Att&P), assignments and quizzes (A&Q), mid-term exam & a comprehensive final exam (E),two group projects (P). For fall 2018 and 2019 on campus: Att&P 5%, A&Q 20%, E 45%, and P30%. SF’s course evaluation methods include
at a field point P around a source point charge Q, we shall start with the definition of the electric field: F FE 1 2e E q+ q+ E = rˆ 1 Qq+ ˆ 4 0 r r 2 We shall position the source charge Q at theF= R origin of the coordinate system. The force on 4 0 r R 2 = Er the test charge q
has shown that using a written feedback process instead of an oral question andanswer (Q&A) feedback process increases fluency and usefulness of comments in anintroduction to design course, E4, at Harvey Mudd College.1 This study further examines writtenfeedback in the same setting and quantifies the degree to which students of different gendersbenefit from providing and receiving written feedback compared to oral feedback. The peerfeedback process is examined for design review presentations during a preliminary conceptualdesign project for first and second year college students in a conceptual design course. Theauthors of this study are able to note the differences in these topics as a function of the gender ofthe commenter. The study
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
time of flight, t = P + Q*sqrt(-1) for example, could have a physical interpretation.For an object being thrown upward inside a well of depth -120m under a gravity downwardpulling of 9.8 m/s/s, the equation 0 = v0*t + 0.5*9.8*t*t -120 would support a physical situation 2018 ASEE Mid-Atlantic Spring Conference, April 6-7, 2018 – University of the District of Columbiawith a modified depth of (-120 + 0.5*9.8*Q*Q) which carries P as the time of flight since thesqrt(-1) terms must cancel out. Kinematics learning requires a minimum memory capacity whencompared to other physics topics. The long term memory of putting the initial numerical valuesin their appropriate terms could be learned by analyzing each math term in a given equation. Theshort
. 3 Page 26.10.42 Three-Dimensional MomentsGiven a three-dimensional density distribution function f (x, y, z), the (p+q+r)order moments are defined in terms of the Riemann integral as: +∞ +∞ +∞ mpqr = rxp ryq rzr f (x, y, z)dxdydz −∞ −∞ −∞ where ri is the normal distance to axis i, i = x, y, z, and p, q, r = 0, 1, 2, ... The integration extends over the domain of f . For an object with limitedvolume in the x, y, z space, the integration extends over the volume of theobject. The second order moments about x,y, and z axes, i.e., p
13 (Q) / % 40 14 discrimination rQ P 30 4 6 73 8 10 5 20 2 11 12
sequential circuits and the process of designing and simulating a Finite State Machineusing clocked signals on an FPGA board. Figure 1: State Diagram with two states: Q = 0 and Q = 1Laboratory Experiment Preparation:Using the state diagram provided in Figure 1, construct the transition table for a JK flip flop anddesign logic that us necessary to derive the two outputs Y1 and Y0 .Methods and Procedure:Part I:Create a Verilog HDL file named “FiniteStateMachine” and simulate process shown in Figure 1on an FPGA board. 12Part II:Construct the circuit designed in the laboratory preparation using LED’s to display the twooutputs Y1 and Y0
Q7 4.06 4 Q8 4.74 4.5 Q9 4.47 4.57 Q 10 4.68 4.57 Q 11 4.47 4.43 Q 12 4.42 4.33 Q 13 4.56 4.17 Q 14 4.11 4.43 Q 15 4.47 4.57 Q 16 4.42 4.71 Q 17
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
measure undergraduate engineering students’ decisions toparticipate in out-of-class activities and the students’ outcomes from involvement in theseactivities. Specifically, this paper details the development of the items and face and contentvalidity for the Postsecondary Student Engagement Survey (PosSES). The instrument development is guided by a thorough literature review, web searches, a Q-studyusing focus group meetings, a panel of experts, and finally, think aloud sessions to determineface and content validity. The instrument measures positive and negative involvement outcomesand factors that promote and prevent participation decisions in out-of-class activities; andengineering identification, sense of belonging, engineering major
transportation engineering with lecture and laboratorycomponents at the Pennsylvania State University. Specifically, the study seeks to determine howthe transition to remote instruction impacted student perceptions of the learning environment asit relates to the development of their professional expertise. Students’ perception on the learningenvironment was measured using the Supportive Learning Environment for ExpertiseDevelopment Questionnaire (SLEED-Q) [1]. The SLEED-Q was administered to students in Fall2018 and Fall 2019 (normal instruction) and compared with responses obtained from Fall 2020(remote instruction). Prior data (2018, 2019) was collected for baseline comparison as part of alarger curricular revision project to examine the impact of
changes in the parabola shape while thehorizontal distance versus time is always linear in shape. The causation of higher v0 for longerflight time can be deduced inductively after doing a few graphs. A deductive calculuspresentation of differentiating v0 with respect to flight time would belong to the academiclearning approach2, which is teaching time efficient for those students very familiar withcalculus. Every student is expected to be familiar with the quadratic equation solution in algebrathat time = P + Q*sqrt(-1) or P – Q*sqrt(-1) when b*b is less than 4*a*c given the a*x*x+ b*x +c = 0 format. The minimum initial velocity to reach ground from a depth of -120 m can bedemonstrated by extrapolation (about 48 m/s) on a graph of Q*Q versus
Subtopics: ❖ Introductions and icebreakers. ❖ Overview of “Engineering Bright Futures” program. ❖ Statistics and rankings. ❖ Engineering buildings and facilities. ❖ Technical, affinity, and academic engineering organizations. ❖ “Why engineering?” Week 2: Week of Topic: Engineering Majors Part 1 11/1 Subtopics: ❖ Summary of common engineering majors. ❖ Computer Engineering and Q&A. ❖ Software Engineering and Q&A. ❖ Aerospace Engineering and Q&A. ❖ Mechanical