. Issues in Information Systems, 2020. 21(4).3. Dwivedi, Y.K., et al., Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 2020. 55: p. 102211.4. Georgiadou, A., S. Mouzakitis, and D. Askounis, Working from home during COVID-19 crisis: a cyber security culture assessment survey. Security Journal, 2021: p. 1-20.5. Lallie, H.S., et al., Cyber security in the age of covid-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic. arXiv preprint arXiv:2006.11929, 2020.6. Furnell, S. and J.N. Shah, Home working and cyber security–an outbreak of unpreparedness? Computer Fraud
interactions are not be limited to stimulate theteam members’ energy and enthusiasm.References1. El-Sayed, M., and S. Beyerlein. "Design and integration of a capstone course to achieve program outcomes." In ASEE Annual Conference. 2008.2. El-Sayed, M., Engineering Design Education for Integrated Product Realization, Proceedings of the 2009 ASEE annual Conference, ASEE Paper # AC 2009-2234, June 2009.3. Johnson, R. The capstone course: A synergistic tool for pedagogical and assessment goals in higher education. Paper presented at the 10th AAHE Conference on Assessment and Quality, Boston, MA, June 1995.4. Moore, R. C., The capstone course, in W. G. Christ (Ed.), Assessing Media Education: A resource for educators and administrators
curricular and syllabi changesReferences[1] S. Schrader, W. M. Riggs and R. P. Smith, “Choice over Uncertainty and Ambiguity inTechnical Problem Solving,” Journal of Engineering and Technology Management, vol.10,1993, accessed on Nov. 30, 2019,https://dspace.mit.edu/bitstream/handle/1721.1/46980/choiceoveruncert00schr.pdf?s[2] N. J. McNeill, E. P. Douglas, M. Koro-Ljungberg, D. J. Therriault and I. Krause,“Undergraduate Students Beliefs about Engineering Problem Solving,” Journal of EngineeringEducation, vol. 105, no. 4, pp. 560–584, 2016[3] D. Jonassen, J. Strobel and C. B. Lee, “Everyday problem solving in engineering: Lessons forengineering educators,” Journal of Engineering Education, April 2016, pp 139-151[4] H. Simon, “The structure of ill
models, broadening participation initiatives, and S-STEM and LSAMP programs.Dr. Catherine Mobley, Clemson University Catherine Mobley, Ph.D., is a Professor of Sociology at Clemson University. She has over 30 years experience in project and program evaluation and has worked for a variety of consulting firms, non-profit agencies, and government organizations, including the Rand Corporation, the American Association of Retired Persons, the U.S. Department of Education, and the Walter Reed Army Institute of Research. Since 2004, she been a member of the NSF-funded MIDFIELD research project on engineering education; she has served as a Co-PI on three research projects, including one on transfer students and another on
1 1 Pipe Diameter Flow Rate 5 2-3 2–4 Pipe Diameter C. Problem generation.With these parameters identified, the problem generation algorithms can proceed. The problemgeneration process begins by selecting the fluid, entrance type, and pipe material. The entrancelocation is considered to be the reference location for the system elevation, and the materiallimits the pipe sizes that can reasonably be considered.Next, the pipe diameter(s) are randomly selected. The diameters are generated such that
(Salem, Mass.), vol. 95, no. 5, pp. 877–907, 2011, doi: 10.1002/sce.20441.[3] S. Y. Yoon, M. Dyehouse, A. M. Lucietto, H. A. Diefes-Dux, and B. M. Capobianco, "The Effects of Integrated Science, Technology, and Engineering Education on Elementary Students' Knowledge and Identity Development: Effects of Integrated STEM Education on Students," School science and mathematics, vol. 114, no. 8, pp. 380–391, 2014, doi: 10.1111/ssm.12090.[4] O. Pierrakos, T. K. Beam, J. Constantz, A. Johri, and R. Anderson, "On the development of a professional identity: Engineering persisters vs engineering switchers," in 2009 39th IEEE Frontiers in Education Conference, 2009, pp. 1–6.[5] B. Geisinger and D. R. Raman, "Why They Leave
in the meteorology community. Initially released in 2002, it isdeveloped by the Unidata Program Center (UPC)(Unidata | IDV FAQs, n.d.), which is a group ofinstitutions that develops and shares tools and data with the Earth Science research and educationcommunity. Unidata is primarily funded by the National Science Foundation and is part of the UniversityCorporation for Atmospheric Research (UCAR) (Unidata Tour, 2021). Figure 2 is a typical IDVvisualization included with the curriculum. Figure 2. An IDV visualization showing a constant pressure (a.k.a., isobaric) surface colored by windspeed along with surfaces of constant wind (a.k.a., isotach) at a value of 50 m/s. Note the orientationdirected from southeast to northwest across the Earth
Education and a member of the Physics Department.Dr. Daniel Almeida, California Polytechnic State University, San Luis Obispo Dr. Daniel Almeida is an Associate Professor in Higher Education Counseling/Student Affairs at Califor- nia Polytechnic State University, San Luis Obispo. He is Lead Principal Investigator for the NSF-funded California State University Underrepresented Minority STEM Faculty Alliance for Graduate Education & the Professoriate (AGEP) Model: A Culturally-Informed Strengths-Based Approach to Advance Early- Career Faculty Success. Dr. Almeida is also Co-Principal Investigator for the NSF Scholarships in Sci- ence Technology Engineering & Mathematics (S-STEM) grant, Engineering Neighbors: Gaining
treatment activities included short labs to demonstrate water treatment processes, awastewater treatment plant tour, and a short treatment plant design project. Table 1. Overview of in-person module activities, formats, and durations. Activity Format(s) Duration Water quality introduction PowerPoint 0.5 h Water quality lab Hands-on activity 2h Water treatment introduction PowerPoint, 0.5 h hands-on activity Wastewater treatment plant tour Tour 1h Water treatment plant design Hands
Paper ID #35304Using NIST’s Shortwave Broadcast Signals to Experience and UnderstandIonospheric Radio PropagationDr. Paul Benjamin Crilly, United States Coast Guard Academy Paul Crilly is a Professor of Electrical Engineering at the United States Coast Guard Academy. He re- ceived his Ph.D. from New Mexico State University, his M. S. and B.S. degrees at Rensselaer Polytechnic Institute, all in Electrical Engineering. He was previously an Associate Professor of Electrical and Com- puter Engineering at the University of Tennessee and was a Development Engineer at the Hewlett Packard Company. His areas of interest include
instructional aspects communicated to the students; (2)provide clear organization of all teaching and learning updates; and (3) disseminate anyinstruction-related issues to the specific individual(s) or to the teaching team efficiently andeffectively.IntroductionNorth American universities have cohesive and established frameworks for the employment ofgraduate teaching assistants [4]. A very few universities, like McMaster University, employ bothteaching assistants (TAs) and instructional assistant interns (IAIs) along with instructors for acourse.With all university classes transitioning online from face-to-face, employing more teachingassistants might prove helpful, particularly for incoming first-year students whose needsoutweigh those of students
to leave engineering after their first year, half of thestudents remained at the University and selected a major in a different college and half left theUniversity altogether. While we do not want to keep students in a degree program that they arenot passionate about, we hope to continue to increase support for any student who struggles andencourage them to stick with engineering if it is the right choice for their future.References[1] Darbeheshti, M., & Schupbach, W., & Lafuente, A. C., & Altman, T., & Goodman, K., & Jacobson, M. S., & O'Brien, S. (2020, June), Learning Communities: Impact on Retention of First-year Students Paper presented at 2020 ASEE Virtual Annual Conference Content Access
. Technol., vol. 37, pp. 527-537, Jul. 2006, doi:10.1111/j.1467-8535.2006.00534.x[3] Pearce, K. and S. Scutter, “Podcasting of health sciences lectures: Benefits for students from a non-English speaking background.” Australas. J. Educ. Technol., vol. 26, pp. 1028- 1041, 2010, doi: 10.14742/AJET.1032.Rossana Villa-RojasRossana Villa-Rojas is an assistant professor of practice in the Department of Food Science andTechnology at the University of Nebraska-Lincoln (UNL). Dr. Villa-Rojas is associated with the3+1 Food Science Dual Degrees Program (FSDDP) established between UNL and NorthwestAgriculture and Forestry University (NWAFU), China. She teaches undergraduate courses in thearea of food science and engineering. Research interests
foundsimply by typing in the known values. In reality, computerized thermodynamics tables havebeen in use since the 1960’s as the printed tables used in thermodynamics textbooks from thattime forward have been based on a computer solution to a many variable fundamentalthermodynamic function. As an example, when the author was an undergraduate student takingThermodynamics I and II, along with the textbook, students were expected to obtain a copy of“Steam Tables” by Keenan, Keyes, Hill, and Moore1. A large part of the instruction in the useof such printed tables was always related to the act of interpolating between table entries todetermine property values at temperature and/or pressure values between those presented in thetable. As the use of
and was measured at 0.002000various frequencies (69Hz, 70Hz, and 74Hz).The voltage produced by the model is very 0.001500slight due to its small scale and the fact that 0.00 5.00 10.00 15.00 20.00the sound used to test does not contain very 0.001000 Time (s)much energy. Therefore, the OpAmp circuitwas necessary to measure it with the Figure 6b: Voltage measured at 70 Hz.Redboard. The operational amplifier used is 0.002000the MCP6002. This circuit was tested
NationalAcademies Press, 2015.[6] J. Smith and L. Nadelson, “Finding Alignment: The Perceptions and Integration of the NextGeneration Science Standards Practices by Elementary Teachers,” School Science &Mathematics, 117(5), 194–203, 2017.[7] Next Generation Science Standards. Ngss.data.org. https://ngss.nsta.org/About.aspx(Accessed March 28, 2021).[8] S.W. Bowers, T.O. Williams Jr., and J.V. Ernst, “Profile of an Elementary STEMEducator,” Journal of STEM Education: Innovations & Research, 21(1), 51–57, 2020.[9] S.M. Nesmith and S. Cooper, “Engineering process as a focus: STEM professionaldevelopment with elementary STEM‐focused professional development schools,” SchoolScience & Mathematics, 119(8), 487–498, 2019. https://doi
- weather-permitting months. In figure 2, the location search to limit the algae’s growth and protect the lake. The Scholars of Excellence in Engineering and Computer Sciences (SEECS), a multi-semester program at Gannon University supported by a S-STEM grant of these buoys around Presque Isle State Park are from the National Science Foundation, has partnered with the Regional Science Consortium to engineer a mapped with a satellite image of Lake Erie. submerged device that extends the data collection timeline and stores water quality data from Lake
, 2021, from https://www.grainger.com/product/IIG-1-2-in-x-12-in-x-12-in-Calcium-19NE43[4] Chen, M., Zheng, Y., Zhou, X., Li, L., Wang, S., Zhao, P., Lu, L., & Cheng, X. (2019). Recycling of paper sludge powder for achieving sustainable and energy-saving building materials. Construction and Building Materials, 229, 1–12. https://doi.org/10.1016/j.conbuildmat.2019.116874[5] Chen, W., Li, Y., Chen, S., & Zheng, C. (2020). Properties and economics evaluation of utilization of oil shale waste as an alternative environmentally-friendly building materials in pavement engineering. Construction and Building Materials, 259, 1–12. https://doi.org/10.1016/j.conbuildmat.2020.119698[6] Cox, R., & Goodman
, 2018.11 S. Usón, B. Peña, I. Zabalza, E. Llera, L. Romeo, “Combining Flipped Classroom Model and Edu- cational Videos for Improving Teaching-Learning Process in Thermodynamics and Thermal Engi- neering,” Proceedings, vol 2, no 21, 1329, 2018, https://doi.org/10.3390/proceedings221132912 T. Hattingh, W. van Niekerk, H. Marais and Y. Geldenhuys, “Engineering student experiences of a remotely accessed, online learning environment,” 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), pp 1-6, 2020, doi: 10.1109/WEEF- GEDC49885.2020.9293652.13 S. Habib and T. Parthornratt, “Anticipated and Actual Challenges Pertaining to Online Delivery of University Courses During COVID-19 Pandemic
developed and added to the sleep model including alcoholand caffeine functions to predict concentrations in the blood as a function ofconsumption, bodyweight, and time to metabolize the chemicals (alcohol and/orcaffeine). The REM fraction equation in GREG was redeveloped to adjust REM fractionas a function of REM latency, which varies as a function of amount and type ofchemical(s) in the body at bedtime. A function to predict the average time needed to fallasleep was also added as a third dependent variable. While there are several internaldependent variables that are then used to predict something else, the general sleep modelhas four dependent variables: active or stimulated efficiency, passive or un-stimulatedefficiency, time to fall asleep, and
E S G P P N S N Il/ NM M R T Total Cate- Topic t. E MA E C In W W S M N S
haveresulted in different design artifacts.4.2 Design Solutions + IterationsIn RtD, an artifact is designed and the rationale behind design decisions is used to create newknowledge. In HCI RtD studies, the artifact of design is often an object - a music player, awearable, or a piece of furniture - that prompts a new form of interaction. For us, we focused onthe design of learning environments - in particular the design and iteration of two specificundergraduate courses. At the beginning of our research inquiry, we focused on the course as adesign artifact. In our reflection meetings smaller designed artifacts emerged as a focus due tothe size and complexity of looking at the entire course as a designed object. For example, onefocus of Author 1's
majority of lab experience in LU’s IE curriculum occursduring the first two years in chemistry and physics that is not part of the 2+2 online program.After the 2nd year, a single weekend lab is used for our material process lab where studentsmake a hammer in our machine shop. The Work Design lab is mostly observational studies thatcan be conducted offsite. Computer aid manufacturing and automation labs are software based.Another challenge is students having consecutive multi-semester internship, co-ops and full-timeemployment where they take classes part time that extends the average time to graduation andcomplicates reporting program effectiveness including NSF S-STEM grant effectiveness. Highperforming students tend to take longer than 4 years
3 Jung Typology Extrovert (E) 2 9 5 Introvert (I) 7 8 4 Sensing (S) 1 7 4 Intuition (N) 8 10 5 Thinking (T) 4 12 8 Feeling (F) 5 5 1 Judging (J) 7 15 5 Perceiving (P) 2
and students of different disciplines and nationalities," in Proceedings of the 2018 ASEE Zone IV Conference, Boulder CO, 2018.[10] N. Kathryn, "The Engineering in the Museum: Helping Engineering Students Experience Technology as Art," in Proceedings of the ASEE 1996 Annual Conference and Exposition, Washington DC, 1996.[11] A. Rose and V. Grash, "Interaction of Engineering Technology and Fine Arts Through Instructor Collaboration," in Proceedings of the ASEE 2005 Annual Conference and Exposition, Portland OR, 2005.[12] L. Yu and F. Abarca, "ElectrizArte, combining engineering and arts," in Proceedings of the 2012 Interdisciplinary Engineering Design Education Conference, 2012.[13] S. Burkett and C. Snead, "Picasso's
instruction, and face-to-face instruction," Computers & education, vol. 55, no. 2, pp. 733-741, 2010.[2] L. Kinney, M. Liu and M. A. Thornton, "Faculty and student perceptions of online learning in engineering education," in 119th ASEE Annual Conference and Exposition, 2012.[3] P. Panindre and R. S. Thorsen, "Assessment of Learning Effectiveness in Online and Face- to-Face Learning Environment for Engineering Education," in ASEE Annual Conference Proceedings, 2020.[4] S. Papanikolaou, "E-Learning and Assessment in the Cloud: Engineering Courses," in ASEE Annual Conference Proceedings, 2020.[5] R. Zaurin, S. D. Tirtha and N. Eluru, "A Comparison between Mixed-Mode and Face-to- Face Instructional Delivery Approaches for
world we live in.Joseph Carl PriceCol. Aaron T. Hill Jr., United States Military Academy Colonel Aaron Hill is an Assistant Professor and Design Group Director in the Department of Civil & Mechanical Engineering at the United States Military Academy, West Point, New York. He holds a Bachelor of Science degree from West Point, a Master of Science degree in Engineering Management from Missouri S&T, a Master of Science degree in Civil Engineering from Virginia Tech, and a PhD in Civil Engineering from The University of Texas at Austin. Aaron has served in the military for 23 years as an Engineer Officer with assignments around the world to include Afghanistan, Egypt, and Bosnia- Herzegovina. He is a licensed
engineering population of the United States. While the institutionsused in this study share common matriculation practices, all institutions of the same type are notnecessarily identical to each other. For example, some institutions offer majors not availableelsewhere and some may have enrollment criteria for specific engineering majors that exceed therequirements for engineering at large.AcknowledgementThis material is based upon work supported by the National Science Foundation (NSF) underGrant No. 1545667. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NSF.References[1] A. Theiss, J. E. Robertson, R. L. Kajfez, K. M. Kecskemety, and
package [12]. JuMP and the Julia language allowed for very straightforward dataprocessing, and problem setup. Once the appropriate 𝑐𝑐, 𝑠𝑠, 𝑠𝑠, 𝑟𝑟, and 𝑟𝑟 constants are created basedon the survey data, the problem above can be expressed using the following JuMP code:opt = Model()@variable(opt, x[1:n_groups, 1:n_projects], Bin)@objective(opt, Min, sum(c.*x))for i in 1:n_groups @constraint(opt, sum(x[i, :]) == 1) # constraint (3)endfor j in 1:n_projects @constraint(opt, s̲ <= s'*x[:,j] <= 𝑠𝑠̅) # constraint (4) @constraint(opt, r*x[:,j] .>= ̲r[:, j]) # constraint (5)endThe Gurobi solver was able to find optimal
completing the course, student will be able todemonstrate their factual and conceptual knowledge about the data visualization process: 1. The basic stages for visualizing data. 2. What happens in each stage of the visualization process. 3. What stages are likely to initiate the iterative nature of the process. 4. Different techniques used to better understand data.After completing the course, students will be able to demonstrate the following proceduralknowledge: 1. Demonstrate actions to acquire data. 2. Demonstrate the ability to change raw data into a useful format for further processing. 3. Implement procedure(s) to extract data of interest from a larger dataset. 4. Choose the appropriate visualization chart for