score out of 80 pointswas determined for each team. 5: Excellent, 4: Above average, 3: Average, 2: Below average, 1: Poor Pitch Video/Q&A Content1. Product/concept was clearly conveyed2. Idea(s) were realistic/feasible3. Motivation/need for product was clearly addressed4. Market clearly identified5. Addressed all necessary components in detail6. Well organized with clear introduction of topic/idea, leading up to conflict and resolution with summary of key points/highlights7. Explains what their product/idea does and how it adds value8. Described why their product/idea is unique and different (and/or better)1. Knew material/answers to questions2. Spoke at
-university test was administered before the start of the program, and the post-testwas administered at the end of the first-year year. The data for Case 2 was collected in2014/2015 where the pre-university test was administered before the start of the program and themid-test was administered midway through first-year.The sample size in both cases was approximately 200 students for the pre-university test, with aslightly reduced group size due to attrition (approximately170) for both the mid and post-testscenarios. A summary of the results for each case is provided in Table 1. The data in Table 1 iscollated in terms of question number (Q), question type (Type), percent correct for the sampleconsidered both pre, mid and post-test, the number of
*) Q3 AT %QX2.5 : BOOL; (*Lamp 3*) INIT AT %IX0.7 : BOOL; (*Initialisation button*) STEP1 : RS; (*Instance of step 1*) STEP2 : RS; (*Instance of step 2*) STEP3 : RS; (*Instance of step 3*) STEP4 : RS; (*Instance of step 4*) TIMER1: TON; (*Instance of timer 1*) TIMER2: TON; (*Instance of timer 2*) TIMER3: TON; (*Instance of timer 3*) TIMER4: TON; (*Instance of timer 4*) END_VAR (*Step1*) TIMER1(IN := STEP4.Q1, PT := T#3s); STEP1(S := INIT OR STEP4.Q1 AND TIMER1.Q, R1 := STEP2.Q1); (*Step2*) TIMER2(IN := STEP1.Q1, PT := T#3s); STEP2(S := STEP1.Q1 AND TIMER2.Q, R1 := STEP3.Q1 OR INIT); (*Step3*) TIMER3(IN := STEP2.Q1, PT := T#3s
! ! ! a uf! c t! re U! ! !! ! se & R! !t!! ! ! !e! !! e ir em n t T!r! ! !! e! !! eat m! nt A! ! ! !! i!!!! ! ! c q u is t ion P! ! ! ! ! s!! ! ! roc es! ing &!A! ! e! !b
our energy future. Retrieved from http://www.neefusa.org/pdf/roper/Roper2002.pdf13. Bittle, S., Rochkind, J., & Ott, A. (2009). The energy learning curve. Retrieved from http://www.publicagenda.org/media/the-energy-learning-curve14. Southwell, B. G., Murphy, J. J., DeWaters, J. E., & LeBaron, P. A. (2012). Americans' perceived and actual understanding of energy. (RTI Press peer-reviewed publication No. RR-0018-1208). Research Triangle Park, NC: RTI Press. Retrieved from http://www.rti.org/rtipress15. Langfitt, Q., Haselbach, L., & Hougham, R.J. (2014). Artifact-based energy literacy assessment utilizing rubric scoring. Journal of Professional Issues in Engineering Education and Practice. Retrieved from
, together with data from the online forum, grade data, attendance, assignment submissions, and lab exercise scores, we will use the queue data to characterize successful students and their study habits, so we can prescribe behaviors that we believe will result in positive course outcomes.References1 : “NEMO-Q | Line Management Systems”, http://www.nemo-q.com2 : “Appointment Scheduling Software, Scheduling System | Q-nomy”,http://www.qnomy.com/Products/Queue-Management.aspx3 : “STEM Confidence Gap | Piazza Blog”, http://blog.piazza.com/stem-confidence-gap/4 : MacWilliam, Malan. “Scaling Office Hours: Managing Live Q&A in Large Courses.” Journal ofComputing Sciences in Colleges 28.3 (2013): 94-101
, polarization 5Half-wave dipole, simple reactive impedance matching, folded dipole 3Dipole equivalent circuit, bandwidth and Q, baluns 3Image theory and monopole antennas, ground effects 1Two-antenna arrays, linear antenna arrays 2Friis equation, practical system link analysis 2Intro to computational electromagnetics, method of moments concept, simulation project 1Examinations/review sessions 2 Table 3. Comparison of
) critically evaluating the state of research andrecommending improvements, and (c) identifying neglected topics that require the attention ofresearchers. Our completed systematic review will contribute in each of these three areas.Bibliography1. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A Page 26.1754.10 meta-analytic survey. Journal of Educational Psychology, 106, 901-918.2. Sabo, K. E., Atkinson, R. K., Barrus, A. L., Joseph, S. S., & Perez, R. S. (2013). Searching for the two sigma advantage: Evaluating algebra intelligent tutors. Computers in
- content/uploads/2012/01/EUR-ACE_Framework-Standards_2008-11-0511.pdf.(13) Passow, H. J. J. Eng. Educ. 2012, 101, 95. Page 26.1177.10(14) Brett, J.; Behfar, K.; Kern, M. C. In The Essential Guide to Leadership; Harvard Business Review, 2009; pp. 85–97.(15) Halverson, C. In Effective Multicultural Teams: Theory and Practice; Halverson, C. B.; Tirmizi, S. A., Eds.; Springer, 2008; pp. 81–110.(16) Pelled, L. H.; Eisenhardt, K. M.; Xin, K. R. Adm. Sci. Q. 1999, 44, 1.(17) Watson, W. E. Acad. Manag. J. 1993, 36, 590.(18) Horwitz, S. K. Hum. Resour. Dev. Rev. 2005, 4, 219.(19) Manning, M. L.; Lucking, R. Clear
. C. (1988). Developing a model of the library search process: Cognitive and affective aspects. RQ, 28(2), 232-242.5. Onwuegbuzie, A. J. (1997). Writing a research proposal: The role of library anxiety, statistics anxiety, and composition anxiety. Library & Information Science Research, 19(1), 14.6. Jiao, Q. G., Onwuegbuzie, A. J., & Lichtenstein, A. A. (1996). Library anxiety: Characteristics of “at-risk” college students. Library & Information Science Research, 18(2), 151-163.7. Jiao, Q. G., Onwuegbuzie, A. J., & Lichtenstein, A. A. (1996). Library anxiety: Characteristics of “at-risk” college students. Library & Information Science Research, 18(2), 151-163.8. Daly, J. A. (1978). Writing
, w2, T) 4. A function that plots a phase diagram (35 points) function PhaseDiagram(HA1, SA1, HB1, SB1, w1, HA2, SA2, HB2, SB2, w2, Tmin, Tmax)Project 2: Modeling HIV Response to Immune TherapyYour assignment is to write a computer program that will model the progress of the HIV infection in a patient that isbeing treated with a drug of a given effectiveness, Q. The HIV infected patient is assumed to start with a T-cellcount of T(0)=1, this being a healthy level, and having no infected T-cells, I(0)=0. We assume that infection occursat day 0 a viral load of V(0)=0.01. We will assume that if a drug is administered, therapy starts on the day ofinfection. In the model, if the HIV infected patient’s T-cell count, including
both mean value and standarddeviation, to return a numerical track quality indicator, which will inform the user whether or notthe given case of operation if feasible. In order to numerically determine the track quality, Q, anequation that effectively ranks each situation was elaborated.Additionally, serious wear marks were noticed on the Elizabethtown College outdoor athletictrack, which makes it one of the most difficult cases to achieve (Table 3). On the other hand,readings were collected from the new outdoor athletic track at Alvernia University (Reading,PA), to quantitatively rank more than one typical quality of outdoor tracks. It was found that theanalyses of these readings were much more conclusive, making it a far easier case. However
(ECDH), digital signature algorithm (ECDSA), and integrated encryptionscheme (ECIES) are placed. In all of these security protocols which are standardized by severalnational and international organizations, the main computation is point multiplication. Theelliptic curve point multiplication is defined as Q = k.P, where k is a positive integer, and Q andP are two points on the elliptic curve. The efficiency of computing point multiplication dependson finding the minimum number of steps to reach Q from a given point P.Some of the educational goals in this step were (a) understanding the implementation platforms(commonly referred to as hardware [ASIC/FPGA] or software platforms [microcontrollers])through which the overheads were derived, (b) soft
followingquestions on an end of the course evaluation form: This semester videos of your presentations were made available to each student group, and you were asked to provide feedback on your individual performance as well as your Page 26.927.6 teammates’ performance. Do you feel this experience helped you improve your presentation skills? Is it worth it for [the instructor] to continue recording student group presentations and providing videos to groups for their evaluation in the future? Which presentation feedback method did you find most helpful – evaluation of your own presentation/Q&
), 1541–1547.[5] Mazumder, Q. H., Karim, R. M. (2012). Comparative Analysis of Learning Styles of Students of USA and Bangladesh, Paper no: AC2012-5075, 119th ASEE Annual Conference, June 10-13, 2012, San Antonio, TX, USA[6] Sadi, O. & Uyar, M. (2013). The relationship between cognitive self-regulated learning strategies and biology achievement: A path model. Procedia-Social and Behavioral Sciences; 93 (2013), 847-852.[7] Crede, M., & Philips, A. L. (2011). A meta-analytic review of the motivated strategies for learning questionnaire. Learning and Individual Differences; 21 (2011), 337-346.[8] Puteha, M., &, Ibrahimb, M. (2010). The usage of self-regulated learning strategies among form four students in
support during the research process. Additionally,authors would like to thank University of Michigan - Flint institutional review board, faculty and staff fororganizing necessary field trips and to various locations related to current study and equipment support. References[1] Savoji, A. P. (2013). Motivational strategies and academic achievements in traditional and virtual university students. Procedia-Social and Behavioral Sciences; 84 (2013), 1015-1020[2] Mazumder, Q. H. and Ahmed, K. (2014). “A Comparative Study of Motivation and Learning Strategies Between Public and Private University Students of Bangladesh” Proceedings of the 2014 ASEE North Central Section Conference
., Skokan, C., Kosbar, L., Dean, A., Westland, C., Barker, H., Nguyen, Q. & Tafoya, J. (2007). “K-12 outreach: Identifying the broader impacts of four outreach projects.” Journal of Engineering Education, 96 (2), 173-189.14. Tafoya, J., Nguyen, Q., Skokan, C. & Moskal, B. (2005). “K-12 outreach in an engineering intensive university.” Paper in the Proceedings of the annual meeting of the American Society for Engineering Education, Portland, Oregon (11 pages). REPRINT: Proceedings of 4th ASEE/AaeE Global Colloquium on Engineering Education, Sydney, Australia, September, 2005.15. Moskal, B., Skokan, C. & Duffield, J. (2004). "GK-12 learning partnership: An outreach program in engineering education.” Paper in the
(FSKD), 3, 2010, 1324 - 1327.[12] P. Bai, X. Song, J. Wang, W. Shi, Q. Wang, A Hillslope Infiltration and Runoff Prediction Model of Neural Networks Optimized by Genetic Algorithm, 2010 International Conference on Mechanic Automation and Control Engineering (MACE), 2010, 1256 – 1259.[13] J. Liu, B. Wang, Parameters Selection for SVR Based on the SCEM-UA Algorithm and its Application on Monthly Runoff Prediction, 2007 International Conference on Computational Intelligence and Security, 2007, 48 - 51.[14] W. Guo, H. Wang, J. Xu, Y. Zhang, BF Neural Network Model Based on Improved PSO for Predicting River Runoff, 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA), 2, 2010, 968 – 971.[15] H
rules for the addition, composition, and inversion of rational relations will now bestated and proved. The following theorem extends that given in 8 by adding a rule for the equalityof two rational relations. Also, the proof presented here uses relational identities.Theorem 10.2 The set of rational relations (Q, +, ·) is a subseminearring of L(C∞ ). For alla1 , a2 ∈ B, and all b1 , b2 , g ∈ B\{0}, a1 ga2 a1 a2 coprime(b1 , a2 ) ⇒ = , (43) b1 g b2 b1 b2 a1 a2 a1 b2 + a2 b1 coprime(b1 , b2
evaluated by the authors. Session 1 Session 2 Presentation Q&A Presentation Q&A 0.4286 0.9333 0.7241 0.5926 no input 4 8 10 12 -1 8 0 2 0 0 4 2 4 11 1 23 29 23 16 Observation: 1. The value of 0.4286 indicates the results could have been better but was still a worthwhile effort. Recall the value could go negative. Note there 23 out of 39 students indicated the
physics reasoning: A commitment to substance-based conceptions,” Cognition and Instruction 18, 1 (2000).12 P. V. Engelhardt, “Examining students' understanding of electrical circuits through multiple-choice testing andinterviews,“ Ph.D. Thesis, North Carolina State University, 1997. Page 26.158.1413 C. D. Whitlatch, Q. Wang, and B. J. Skromme, “Automated problem and solution generation software forcomputer-aided instruction in elementary linear circuit analysis,” in Proceedings of the 2012 American Society forEngineering Education Annual Conference & Exposition (Amer. Soc. Engrg. Educat., Washington, D.C., 2012
) Determine M W,Hog [MT-m] from Eqn 3.5.1.a. Determine total bending moment, MT,Hog = M W,Hog + M SW. (G) Determine M W, horiz [MT-m] from Eqn 3.5.3. (H) Determine wave vertical Shear, Q W,V from Eqn 3.5.5. [MT] Determine total vertical shear Q T,W = Q W,V + QSW. [MT] (I) Determine minimum value of the section modulus = Z min [m3] from Eqn 3.6.3. Compare the value with the minimum (deck or keel) section modulus for the barge.Part II: POSSE CalculationsModel the barge in POSSE geometrically and structurally using Reference (2) as a guide. Input the fullload condition and determine shear, bending moment and stress for still water, hogging and saggingconditions (SW, S, H). All answers are to be in the
. Page 26.1552.1210. Oyserman, D.; Destin, M.; Novin, S. Self Identity 2014, 1–16.11. Fugate, M.; Kinicki, A. J.; Ashforth, B. E. J. Vocat. Behav. 2004, 65, 14–38.12. Ibarra, H. Adm. Sci. Q. 1999, 44, 764–791.13. Ibarra, H. Identity transitions: possible selves, liminality and the dynamics of career change; 2005.14. Kerpelman, J. L.; Pittman, J. F. J. Adolesc. 2001, 24, 491–512.15. Godwin, A.; Potvin, G. Int. J. Eng. Educ. (In Press. 2015.16. Pizzolato, J. E. Cultur. Divers. Ethnic Minor. Psychol. 2006, 12, 57–69.17. Committee on K-12 Engineering Education. Engineering in K-12 education: Understanding the Status and Improving the Prospects; Katehi, L.; Pearson, G.; Feder, M. A., Eds.; The National Academies Press
providing thetextbook copies used in our laboratory experiment and for his support of the project.References1 C. D. Whitlatch, Q. Wang, and B. J. Skromme, “Automated problem and solution generation software forcomputer-aided instruction in elementary linear circuit analysis,” in Proceedings of the 2012 American Society forEngineering Education Annual Conference & Exposition (Amer. Soc. Engrg. Educat., Washington, D.C., 2012), p.Paper 4437.2 B. J. Skromme, C. D. Whitlatch, Q. Wang, P. M. Rayes, A. Barrus, J. M. Quick, R. K. Atkinson, and T. Frank,“Teaching linear circuit analysis techniques with computers,” in Proceedings of the 2013 American Society for
the Summitagenda in Appendix A). Because group input was a key objective, almost half of each panelsession was devoted to Q&A with the audience. Detailed session notes capture theconversations for these and all sessions at the Summit.6Table 1. Research-Based Panel Sessions at the Epicenter Research SummitSession Title Central Questions for PanelistsResearch on Students’ How can we learn about students’ entrepreneurialEntrepreneurial Development development through an interactive lens, i.e., the interplayand Pathways between individual characteristics and contexts? How diverse are students’ entrepreneurial pathways? What are the implications for
the inconsistent results for theresonant frequency and Q-factor measurements with respect to theoretical predictions, as somehidden circuit features are not considered at first. The “trick and think” approach describedabove in this contribution addresses measurements in the time domain, where perhaps thedisparity between underdamped and overdamped response offers a more dramatic and immediatevisual effect. Page 26.136.8References[1] “Confidence-Building in a Circuits Course,” by Ilan Gravé, in Proceedings of the ASEE 2005 Conference inPortland, OR, June 2005.[2] “Study of the phase relationships in resonant R C L circuits using a dual-trace
’ occupational aspirations. Perceptual and Motor Skills, 81: 701-702. 9. NAE (National Academy of Engineering), (2008). Changing the conversation: Messages for improving public understanding of engineering. Washington, D.C.: The National Academies Press. 10. Wixson, Karen K. (1984) Vocabulary Instruction and Children's Comprehension of Basal Stories. Paper presented at the National Reading Conference, St. Petersburg, FL. 11. Schwartz, R.M., & Raphael, T.E., (1985). Concept of Definition: A Key to Improving Students' Vocabulary. The Reading Teacher, 39(2): 198-205. 12. Patton, M. Q. (2001). Qualitative Research & Evaluation Methods (3rd ed.). SAGE Publications, Inc. 13. Sacks, H
Efficient Management of Wind Power Generation with the Application of Wind Tunnel Attachment on a Wind Turbine”. ATMAE Annual Conference, Cleveland, USA5. Dakeev, U., & Mazumder, Q., (2014). “Analysis of Wind Power Generation with Application of Wind Tunnel Attachment”, ASEE 2014-8501, 121st ASEE Annual Conference, June 13-15, 2014, Page 26.447.7 Indianapolis, USA6. Dakeev, U., Lam, C., Pung, J. (2015). “Analysis of Wind Power Generation with Wind Guide Attachment”. International Journal of Engineering Research and Innovation, 067J.7. NREL, (2015). National Renewable Energy Laboratory. www.nrel.org8. Toshio M., Shinya T. & Seeichi