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2019 Pacific Southwest Section Meeting

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Ni Li, California State University, Los Angeles; Nancy Warter-Perez, California State University, Los Angeles; He Shen, California State University, Los Angeles

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students’ scores of the two groups. It can be seen that students’ performance of thetreatment group is significantly better than that of the control group in the midterm exam. Morenoticeably, the number of students who received “A”s in treatment group is twice of that in thecontrol group. Figure 2. Students grade distribution in mid-term exam Figure 3. Comparison of midterm exam score distributionFigure 4. Stacked bar graph of mid-term exam score (A: 90~100; B: 80~89; C: 70~79; Below C: 69 or lower)Similar to the study done for the midterm performance, the results and comparison of students’final exam scores of the two groups are shown in Figures 5 through 7, where

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Nicole Wagner, California State Polytechnic University, Pomona

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separated to show those students who did and those who did notcomplete the pre-class assignment. It is seen in the figure that the students who completed thework, by viewing the videos and slide modules, rated the pre-class assignment more effective inpreparing them for the PLC laboratory sessions than those students who did not complete thepre-class work. (a) (b)Figure 1: Student feedback on the effectiveness of (a) the pre-class assignments and (b) the pre- class quizzes on their preparation for the PLC laboratory sessions.In addition, students were asked to evaluate the effectiveness of the pre-class quiz in preparingthem for the PLC laboratory sessions. Figure 1b

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Ronald P. Uhlig, National University; Rich Yonts, Teradata; Benjamin W Cashman, National University; Richard S. Clark, National University ; Brett Nieman

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introduction to immutability of blockchains. Retrieved on July 20, 2018 from https://bitsonblocks.net/2016/02/29/a-gentle-introduction-to-immutability-of- blockchains/2 Posey, B. (Aug, 2014) Redundant Cloud Storage Ensures High Availability. Retrieved on July 20, 2018 from: https://searchstorage.techtarget.com/feature/Redundant-cloud-storage-ensures-high-availability3 Concordia, U. (2018) Transcripts. Retrieved on July 20, 2018 from: https://www.cui.edu/studentlife/registrar/index/id/27224 Orcutt, M. (Nov 16, 2017). Blockchains use massive amounts of energy-but there’s a plan to fix that. Retrieved on July 20, 2018 from: https://www.technologyreview.com/s/609480/bitcoin-uses-massive-amounts-of-energybut- theres-a-plan-to-fix-it/5

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Kaela M Martin, Embry-Riddle Aeronautical University, Prescott; Elif Miskioglu, Bucknell University; Maciek Czyz

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using this program for some time, the user should be able to create reasonableorbits from scratch in similar orbital mechanics programs. They would begin to understandfundamentals, like the relationship between a state vector and orbital elements, velocitiesnecessary to maintain an orbit, and the influence of gravity from surrounding bodies. Moon Moon Earth Earth a) Three-body (Earth, Sun, Spacecraft) b) Four-body (Earth, Sun, Moon, Figure 5: Three-Body and Four-Body Spacecraft MotionBy starting with these first principles, the foundation is laid for

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Zoe J. Wood, Cal Poly; Enrica Lovaglio Costello, CalPoly; Aaron W Keen, California Polytechnic State University; Christian Eckhardt, California Polytechnic State University San Luis Obispo

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.           Bibliography:    1. C. Alvarado  and Z. Dodds, “Women in CS: an evaluation of three promising practices,” in  Proceedings of  the 41st ACM technical symposium on Computer science education , 2010, pp. 57–61.    2. W. Bares and B. Manaris and R. McCauley, “Gender equity in computer science through computing in the  arts – a six­year longitudinal study”, in  Computer Science Education , 28:3, 2018, pp. 191­210.    3. Barker, L. J., Garvin­Doxas, K., & Roberts, E. (2005), “What can computer science learn from a fine arts  approach to teaching?”, in  Proceeding of the 36th ACM technical

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Xiaorong Zhang, San Francisco State University; Ricardo Jesus Colin, San Francisco State University; Alex David, San Francisco State University; Amelito G Enriquez, Canada College; Wenshen Pong P.E., San Francisco State University; Zhaoshuo Jiang P.E., San Francisco State University; Cheng Chen, San Francisco State University; Hamid Mahmoodi, San Francisco State University; Hao Jiang, San Francisco State University; Danny Daneth Ceron Garcia

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Department of Education through the Minority Science andEngineering Improvement Program (MSEIP, Award No. P120A150014); and the NationalScience Foundation (NSF #1752255).Bibliography1. Olson S, Riordan DG: Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics. Report to the President. Executive Office of the President 2012.2. Gregerman SR, Lerner JS, von Hippel W, Jonides J, Nagda BA: Undergraduate student-faculty research partnerships affect student retention. The Review of Higher Education 1998, 22:55-72.3. Graham MJ, Frederick J, Byars-Winston A, Hunter A-B, Handelsman J: Increasing persistence of college students in STEM. Science

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Shant Aram Danielian, University of California, Irvine; Natascha Trellinger Buswell, University of California, Irvine

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: 100%-105% - A+ 83%-86.9% - B 70%-72.9% - C- 93%-99.9% - A 80%-82.9% - B- 67%-69.9% - D+ 90%-92.9% - A- 77%-79.9% - C+ 63%-66.9% - D 87%-89.9% - B+ 73%-76.9% - C 0% -62.9% - F2.2 Method of AnalysisThe content analysis of the support sheets was conducted in three steps. First, each sheet wasdeidentified and given a student number, next, the sheets were separated from theiraccompanying examination answers, and then content features and organizational features (seeCoding Framework below) were recorded against the student number.2.3 Coding FrameworkBefore attempting to identify features in all

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Pejman Akbari, California State Polytechnic University in Pomona ; Brad Kevin Thomas, Electro Mechanical Systems Engineering Technology; Christopher John Tait, Cal Poly Pomona; Colby Stark

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, M., Iancu, F., Potrzebowski, A., Akbari, P., Müller, N., and Piechna, J., “Numerical Simulation of Unsteady-Flow Processes in Wave Rotors,” ASME Paper IMECE2004- 60973, 2004.[6] Sun, G., Akbari, P., Grower, B., and Müller, N., “Thermodynamics of the Wave Disk Engine,” AIAA Paper 2012- 3704, 2012.[7] Parraga, P., Varney, M., Tarkleson, E., Müller, N., Akbari, P., and Piechna, J., “Development of a Wave Disk Engine Experimental Facility,” AIAA Paper 2012-3703, 2012.[8] Müller, N., Piechna, J., Sun, G., and Parraga, P., “Wave Disk Engine Apparatus,” US Patent 9,856,791, 2018.[9] Todd, R. H., “A Survey of Capstone Engineering Courses in North America,” Journal of Engineering Education, Vol. 84, No. 2, pp. 165-174, 1995.[10] Liu

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Christine E King, University of California Irvine

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collection.We would also like to thank the students, instructors, and teaching assistants of the course fortheir participation in the study.References:[1] Mennin, S. (2007). Small-group problem based learning as a complex adaptive system. Teaching and TeacherEducation, 23, 303-313.[2] Johnson, D. W., & Johnson, F. P. (1991). Joining together: Group theory and group skills. Prentice-Hall, Inc.[3] Bhavnani, S. H., & Aldridge, M. D. (2000). Teamwork across disciplinary borders: A bridge between collegeand the work place. Journal of Engineering Education, 89(1), 13-16.[4] Bahner, B. (1996). Report: curricula need product realization. Mechanical Engineering-CIME, 118(3), S1-S1.[5] Ford, M., & Morice, J. (2003). Using micro management techniques

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Paul Morrow Nissenson, California State Polytechnic University, Pomona

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) 3.09 ± 0.35 2.92 ± 0.58 3.13 ± 0.21 3.52 ± 0.30 a Students repeating course 1 2 0 0 (n = 19) b (n = 21) (n = 21) (n = 19) Know at least one computer language 15% 10% 29% c 16% Unsuccessfully tried to learn a computer language 35% 15% 29% 47% Never tried to learn a computer language 50% 75% 43% 37%a One-way ANOVA test indicates there is a statistically significant difference (p < 0.05) for the flipped W2015

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Aleksandr Sergeyev, Michigan Technological University; Prince Mehandiratta, Michigan Technological University ; Scott A Kuhl, Michigan Technological University

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system setup is shown in Figure 5. Figure 5 (a) demonstrates front view of thehardware setup and consists of CLICK PLC, power supplies and relays. The inside view,provided in Figure 5 (b), shows all the solenoids controlling teach pendant keys and wiringapproach with implementation of the terminal blocks. The description of all the PLC outputconnections with all the teach pendant keys via actuators on middle plate is shown in Table 2.Figure 5: (a) The outer layer of remote robot which consists of CLICK PLC, Din Rail and itscover, 2 Power Supplies, 3 Relays and a sheet of glass to provide the protection, (b) The innerlayer of the remote robot which shows all the actuators attached and wired up to the terminalblocks on the left which is connected

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Deborah Won, California State University, Los Angeles; Gisele Ragusa, University of Southern California; Gustavo B Menezes, California State University, Los Angeles; Adel Sharif, California State University, Los Angeles; Masood Shahverdi, California State University, Los Angeles; Ni Li, California State University, Los Angeles; Arturo Pacheco-Vega, California State University, Los Angeles

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groups.Dr. Gisele Ragusa, University of Southern California Gisele Ragusa is a Professor of Engineering Education at the University of Southern California. She conducts research on college transitions and retention of underrepresented students in engineering and also research about engineering global preparedness and engineering innovation. She also has research expertise in STEM K-12 and in STEM assessment. She chairs USC’s STEM Consortium.Dr. Gustavo B Menezes, California State University, Los Angeles Menezes is a Professor in Civil Engineering Department at CalStateLA. His specialization is in Environ- mental and Water Resources Engineering. Since becoming part of the faculty in 2009, Menezes has also focused on

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Xi Zhou, Northern Arizona University; Fang Lei, Chongqing University of Posts and Telecommunications; Delbert D Willie, Northern Arizona University; Andrew Chrysler, Northern Arizona University; Liangbo Xie, Chongqing University of Posts and Telecommunications; Phillip A Mlsna, Northern Arizona University; Jie Yang, Northern Arizona University

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Chinese students is that they seldom ask anyquestions during the class, but they tend to gather around the instructor after the class and askquestions about the lecture. We believe this is related to the way students are expected to behaveduring lecture, which is to listen and not interrupt. As for NAU taught courses in China, theclassroom follows the more student-centered approach. Every NAU faculty would combine oneor several interactive class activities with traditional lectures to keep the students engaged inclass. Some typical activities include: a) Short Q & A sections: the roles are interchangeable as either the instructor or students can be the one to give questions. b) Asking students to answer short questions with a

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David A Trevas, Northern Arizona University; John T. Tester, Northern Arizona University

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Paper ID #27892Improving Machine Design Instruction by Developing Computational DesignToolsDr. David A Trevas, Northern Arizona University Lecturer in Mechanical Engineering at Northern Arizona University, 2016-present. Visiting Assistant Professor in Engineering at the University of the Incarnate Word (San Antonio, Texas), 2015-16. Held various positions in mechanical engineering and computer programming at Exxon Production Research, University of Texas Medical School, Halliburton, Baker-Hughes, GE Oil & Gas, and Cooper Power Sys- tems.Dr. John T. Tester, Northern Arizona University Dr. John Tester is a Professor in

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Lily Xiaolei Chen, California State University, Los Angeles

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Paper ID #27774Bringing Human Factors into Engineering Education Realm - A Case Study:Teaching Human Factors in Fire Protection EngineeringDr. Lily Xiaolei Chen, California State University, Los Angeles Assistant Professor, College of Engineering, Computer Science, and Technology, California State Univer- sity, Los Angeles c American Society for Engineering Education, 2019 Bringing Human Factors into Engineering Education Realm -A Case Study: Teaching Human Factors in Fire Protection EngineeringHuman-factors engineering has long been considered an integral part of

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Jay Tyler Davis II, California Polytechnic State University San Luis Obispo; Sonya Dick; Brian P. Self, California Polytechnic State University, San Luis Obispo; Bradley Wash, California Polytechnic University, SLO; Julia Holton, California Polytechnic State University: Department of Kinesiology and Public Health

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Paper ID #27859Observing Motor Learning and Control through Juggling and Motion Anal-ysis: A Collaboration Between Dynamics and Kinesiology StudentsMr. Jay Tyler Davis II, California Polytechnic State University San Luis Obispo Undergraduate Mechanical Engineering student at Cal Poly San Luis Obispo.Sonya Dick Sonya Dick is a Senior Mechanical Engineering Student at California Polytechnic State University - SLO. This is her second year working at the Human Motion Biomechanics Lab. As a research assistant, she helps create and teach interdisciplinary laboratories for undergraduate kinesiology and engineering students. Her

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Jinny Rhee, San Jose State University; Camille S. Johnson, San Jose State University

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Paper ID #27845Progress on Longitudinal Study of the Impact of Growth Mindset and Be-longing Interventions in a Freshman Engineering ClassDr. Jinny Rhee, San Jose State University Dr. Jinny Rhee is the associate dean of the College of Engineering at San Jose State University. She oversees all aspects of the undergraduate programs in the college. Her research interests include admin- istration of programs supporting student success, in addition to thermal management of heat and power systems. Dr. Rhee received a PhD in mechanical engineering from Stanford University (1995).Prof. Camille S. Johnson, San Jose State University

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Jim Kuo, California State University, Los Angeles; Justin Moon, California State University Los Angeles; Nancy Warter-Perez, California State University, Los Angeles

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benchmark to quantify the effects of videos introduced in Fall 2018 semester. In each of thetests administered, one conceptual problem and one problem-solving problem were chosen forcomparison with previous cohorts. The scores prior to Fall 2018 (total of 98 students pre-video)were aggregated and used as a benchmark to compare to the Fall 2018 results (59 students post-video).Student performance on these assessment problems were categorized in letter grades of A, B, C,D, F. The authors are interested in whether the supplemental videos are effective in reducing theproportion of low performing students with D or F grades, as well as increasing the proportion ofhigh performing students with A or B grades. Note that a C grade is neither ‘good’ nor ‘bad

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Pradip Peter Dey, National University

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, abaa, abaaa, abaaaa, abaaaaa, abaaaaaa, . . . }Assume that the Turing machine of Figure 2 has just begun to process the input by starting atthe start state (which is assigned number 1 for coding purposes). The Turing machine then takesthe first transition from the start state to state 3; this transition is marked by the triplet (a, a, R).According to this triplet, the machine reads the first symbol a from the leftmost cell of the tape,writes back a on the same cell and moves right. The result is shown in Figure 3, where themachine is scanning the second symbol, b, from the tape (after scanning the first symbol). Figure 3: A Turing machine for aba* which has just scanned the first symbolNext, the machine, from state 3, taking the transition

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Farbod Khoshnoud; Dario Robinson; Bruno Marco Quadrelli; Clarence W De Silva

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different engineering disciplines, which aretypically mechanical, electrical, aerospace, and automotive engineering students, collaborate indesign, analysis, building, and testing of an engineering system. (a) (b) (d) (c) Figure 1. Self-powered solar UAVs [29], [30] and [40], (a) FBD, (b) Quadrotor, (c) Octorotor, (d) Trirotor. Figure 2, illustrates a self-powered vibration control system consist of: a shaker, for producingthe vibration excitations (e.g. base excitations), a spring and a linear actuator

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James M Widmann, California Polytechnic State University, San Luis Obispo; John Chen P.E., California Polytechnic State University, San Luis Obispo; Brian P. Self, California Polytechnic State University, San Luis Obispo; Camaryn Elizabeth Chambers, California Polytechnic State University, San Luis Obispo; Alidod Ghazvini; Lisa Marie Kusakabe

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; all scores have been scaled to be out of 7Test Anxiety: The mean test anxiety score for Cal Poly (B=4.1) and the general SUCCESS survey(B=4.2) are nearly identical as shown in Table 5. These scores are similar to the other studiesexamined and reside in the middle of the range of scores. The other studies used for comparisonconsist of first-year engineering students at a U.S. school, STEM undergraduate students (majoritycomputer science major), and U.S. students of all different majors (attending either communitycollege, private university, or public university). The mean test anxiety scores from these studiesare 3.81-4.29. The students in the SUCCESS survey do not have significantly higher or lowerlevels of test anxiety than students from

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Siddharth Vyas, California Polytechnic State University - San Luis Obispo; Ali Dehghan-Banadaki, California Polytechnic State Uni.; Ali O. Shaban, California Polytechnic State University, San Luis Obispo

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and 𝑅𝑅𝐿𝐿 are lumped parameters representing quantities thatare distributed throughout the entire length of the coil. The Kirchhoff’s Voltage Law for thecircuit may be described by drawing a phasor diagram to show both magnitude and phase angleof the voltages in the circuit. Figure 9(b) illustrates such phasor diagram. Note that circuit currentis chosen to be the reference for the phasor diagram since current is common to all componentsin the circuit. In the lab, the magnitudes of 𝑉𝑉�𝑆𝑆 , 𝑉𝑉�𝑅𝑅 , and 𝑉𝑉�𝐶𝐶 are measurable, but not so for 𝑉𝑉�𝐿𝐿 and𝑉𝑉�𝑅𝑅𝑅𝑅 . This is where phasor diagrams are useful. Fig. 9(a). Series RLC Circuit with a Practical InductorTo determine the magnitudes of 𝑉𝑉�𝐿𝐿 and

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Michael Gee, Canada College; Anthony Akash Lal; Alex E Hercules; Tyler Sheaves, San Francisco State University; Amelito G Enriquez, Canada College; Cheng Chen, San Francisco State University; Hao Jiang, San Francisco State University; Zhaoshuo Jiang P.E., San Francisco State University; Wenshen Pong P.E., San Francisco State University; Xiaorong Zhang, San Francisco State University; Hamid Mahmoodi, San Francisco State University

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survey confirmed that the participants formed a higher level of confidence in pursuing STEM careers after participating in this program. The scalability of this program to reach to a border student body is a challenge.9. Bibliography [1] Wicht, Bernhard, Thomas Nirschl, and Doris Schmitt-Landsiedel. "Yield and Speed Optimization of a Latch- Type Voltage Sense Amplifier." IEEE JOURNAL OF SOLID-STATE CIRCUITS , VOL.39 NO.07, JULY 2004 [2] E. R. Hsieh et al., "The demonstration of low-cost and logic process fully-compatible OTP memory on advanced HKMG CMOS with a newly found dielectric fuse breakdown," 2015 IEEE International Electron Devices Meeting (IEDM), Washington, DC, 2015, pp. 3.4.1-3.4.4. [3] M. Deloge, B. Allard, P

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Sonya Christine Dick, Cal Poly Human Motion Biomechanics Laboratory; Jay Tyler Davis II, California Polytechnic State University San Luis Obispo; Bradley Wash, California Polytechnic State University; Brian P. Self, California Polytechnic State University, San Luis Obispo

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coordinates dynamic variables as a function of timeThe HMBL team pre-collected data for each experiment before students were brought into theHMBL, shown in Figure 1, to ensure consistency in results among teams. The relevant data foreach experiment was prepared in a spreadsheet for the students. An example section of thekinematic and kinetic output for the gait experiment can be found in Appendix B. A derivativecalculator spreadsheet, which used first and second order finite difference method differentiation,was created for the students to find velocities and accelerations from the positional data. Figure 1. Panoramic image of the Cal Poly Human Motion

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Harly Ramsey, University of Southern California

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. B. Phillips. (2017). “Student-Produced Podcasts in Language Learning – Exploring Student Perceptions ofPodcast Activities,” IAFOR Journal of Education, vol. 5, issue 3, pp 155-172. Available:https://doi.org/10.22492/ije.5.3.089. C. G. Johnson. (2018). “Student-Generated Podcasts for Learning and Assessment,” Proceedings of the 8thInternational Conference on Computing Education Research, pp. 84-87. Available:https://doi.org/10.1145/1595356.159537110. E. Alpay and S. Gulati. (2010). “Student-led podcasting for engineering education,” European Journal ofEngineering Education, vol. 25, pp. 415-427.11. C. Drew. (2017). “Educational Podcasts: A Genre Analysis,” E-Learning and Digital Media, vol. 14, issue 4,pp. 201–211.12. C. Forester. (2014

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Forrest Mobley, Embry - Riddle Aeronautical University; Shigeo Hayashibara, Embry-Riddle Aeronautical University, Prescott

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A grid computing system was developed using virtual machine technology to increaseusability and economic practicability. The nodes of this system were built into each workstationcomputer housed in the ACSL at Embry – Riddle Aeronautical University and used portions ofthe resources available in each computer, thus allowing the grid computer to run in thebackground and each workstation to still be used while parallel processing occurred. Though notvery powerful in comparison to commercial and government supercomputers, this system isextremely well suited for undergraduate level research due to its versatility and economic value.BIBLIOGRAPHY1: Barney, B. (2019, February 11). Introduction to Parallel Computing. Retrieved from Lawrence Livermore

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Cheng Chen, San Francisco State University; Wenshen Pong P.E., San Francisco State University; Amelito G Enriquez, Canada College; Nicholas Langhoff, Skyline College; Zhaoshuo Jiang P.E., San Francisco State University; Hamid Mahmoodi, San Francisco State University; Xiaorong Zhang, San Francisco State University; Yifeng Xu, San Francisco State University ; Alexander Carlson, ASPIRES Program; Julissa Rico Ruiz , ASPIRES Program; Karina Reyna; Moises Arturo Vieyra, Canada College

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-0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 Rotation Rotation Figure 5. Comparison between experiment and OpenSees output, (a) pre- and (b) post-PSO analysis The PSO results are taken as the initial parameter set of MCMC, and the total simulationnumber is selected to be 10000. Figure 6 present the probability density of all parameters giventhe observation of the experiment measurements. It can be overserved that parameters like 𝐾𝐾 and𝑀𝑀𝑦𝑦+ and 𝑀𝑀𝑦𝑦− have smaller variance when compared with the rest parameters such as

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Greg Placencia, California State Polytechnic University, Pomona; Pauline Salim Muljana, Old Dominion University

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, pp. 115–127, 2009.[15] D. E. Hammond and C. Shoemaker, “Are there differences in academic and social integration of College of Agriculture Master’s students in campus based, online and mixed programs?,” NACTA J., vol. 58, no. 3, pp. 180–188, 2014.[16] B. Simunich, D. B. Robins, and V. Kelly, “The impact of findability on student motivation, self-efficacy, and perceptions of online course quality,” Am. J. Distance Educ., vol. 29, no. 3, pp. 174–185, 2015.[17] “Research-Based Web Design & Usability Guidelines,” Washington, DC.[18] S. J. Guastello, Human Factors Engineering and Ergonomics: A Systems Approach, 2nd ed. Boca Raton, FL: CRC Press, 2014.[19] N. Sclater, Learning analytics explained. New York, NY

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Jean-michel I. Maarek, University of Southern California

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bioinstrumentation laboratory course” in Paper # 12819, 122nd ASEE Annual Conference and Exposition, Seattle, Washington, 20152. C. Waters, S.J. Krause, J. Callahan, B. Dupen, M.B. Vollaro, and P. Weeks, “Revealing student misconceptions and instructor blind spots with muddiest point formative feedback” in Paper # 15012 123rd ASEE Annual Conference and Exposition, New Orleans, Louisiana, 20163. T.A. Angelo and K.P. Cross, “Classroom assessment techniques: a handbook for college teachers”, 2nd Edition, Jossey Bass, San Francisco, California, 19934. J. Mansfield, T.L. Alford, and N.D. Theodore, “Misconception clarification in online graduate courses” in Paper # 22495, 125th ASEE Annual Conference and Exposition, Salt Lake City, Utah, 20185. S.J

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Zhen Yu, California State Polytechnic University, Pomona; Ha Thu Le, California State Polytechnic University Pomona

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instructor about their experiences with the course and any improvement that they believe is necessary. 5) The common goals for all projects are: (a) college students effectively learn the technical content; and (b) students learn to work with community partner(s) and provide meaningful service to the partner(s).The course taught in spring 2018 involved two separate team projects, which were independentlysupervised by two instructors, Ha Le and Zhen Yu. The first project is a solar project and thesecond project is a 3-D printing project. The projects will be described in details in followingsections.It should be noted that the prerequisite for this final capstone course includes two senior projectcourses