Paper ID #19397Incorporating the Raspberry Pi into laboratory experiments in an introduc-tory MATLAB courseDr. Naji S Husseini, Biomedical Engineering at NCSU and UNC-CH Naji Husseini is a lecturer in the Joint Department of Biomedical Engineering at the University of North Carolina at Chapel Hill and North Carolina State University. He received his B.S. and M.Eng. in En- gineering Physics from Cornell University and his M.S. in Electrical Engineering and Ph.D. in Applied Physics from the University of Michigan, Ann Arbor. He teaches classes in materials science, biomate- rials, MATLAB programming, and biomechanics for
, Professor Ghani frequently gets involved in various professional IT consulting assign- ments as well. He is currently teaching MIS graduate courses at Robert-Morris University. Dr. Ghani holds MSEE from Illinois Institute of Technology, MBA from Keller Graduate School of Management and Doctorate from Northern Illinois University.Dr. Ahmed S. Khan, DeVry University, Addison Dr. Ahmed S. Khan is a Senior Professor in the College of Engineering and Information Sciences at DeVry University, Addison, Illinois. Dr. Khan has more than thirty-two years of experience in research, instruction, curricula design and development, program evaluation and accreditation, management and supervision. Dr. Khan received an MSEE from
has over 30 years of combined academic and industrial management experience. He received his BSME and MSME degrees from Michigan Technological University.Dr. S. Patrick Walton, Michigan State University S. Patrick Walton received his B.ChE. from Georgia Tech, where he began his biomedical research career in the Cardiovascular Fluid Dynamics Laboratory. He then attended MIT where he earned his M.S. and Sc.D. while working jointly with researchers at the Shriners Burns Hospital and Massachusetts General Hospital. While at MIT, he was awarded a Shell Foundation Fellowship and was an NIH biotechnology Predoctoral Trainee. Upon completion of his doctoral studies, he joined the Stanford University Genome Technology
work was supported in part by the Ministry of Science and Technology (MOST),Taiwan, ROC, under Grant MOST 103-2511-S-224 -004 -MY3, MOST 104-2511-S-224-003-MY3, and MOST 105-2628-S-224-001-MY3.Reference 1. Torrance, E. P. (1963). Education and the creative potential. Minneapolis: University of Minnesota Press. 2. Guilford, J. P. (1950). Creativity. American Psychologist, 5(9), 444-454. doi: 10.1037/h0063487. 3. Guilford, J. P. (1967). Creativity: Yesterday, Today and Tomorrow. The Journal of Creative Behavior, 1(1), 3-14. doi: 10.1002/j.2162-6057.1967.tb00002.x.4. Mackinnon, D. W. (1965). Personality and the realization of creative potential. American
and recorded these as the naturalfrequencies, again assuming no damping in the system. The values from the multiple trials wereaveraged together to find the experimental values.Sample Student WorkUsing the theory, the dimensions of the bar and the material properties, students found the naturalfrequencies for principal axes designated as 𝑥 and 𝑧 in Table 1. Table 1: Analytically-Determined Natural Frequencies 𝜔𝑛 x-axis (rad/s) z-axis (rad/s) 1 617 494 2 1702 1361 3 3336 2669 4 5514
,CV,N) Table 6- Average visits to study materials (S) and assessment resources (A)The hypothesis tests comparing Fully Engaged (FE) and Consistent Viewers (CV) groups’average visits to assessments (A) were as follows:Test 1:Null hypothesis: µA,CV,T = µA,FE,TAlternate hypothesis: µA,CV,T < µA,FE,TTest 2:Null hypothesis: µA,CV,N = µA,FE,NAlternate hypothesis: µA,CV,N < µA,FE,NThese tests yield p-values of 0.001 for Thermoelectricity and 0.036 for Nanobiosensors. Takingthe standard threshold of p=0.05, we reject the null hypothesis in both tests and conclude thatConsistent Viewers group access assessments less frequently as compared to Fully Engagedlearners in both courses. The fact that we can accept both alternate hypotheses
Software: Evolution and Process.Dr. James D Kiper, Miami University James Kiper is Chair and Professor of the Department of Computer Science and Software Engineering at Miami University. He teaches a variety of courses across the CS and SE curricula. His research is in the areas of software testing, software risk assessment, design rationale, and computer science and software engineering teaching and learning.Dr. Gursimran Singh Walia Gursimran S. Walia is an associate professor of Computer Science at North Dakota State University. His main research interests include empirical software engineering, software engineering education, human factors in software engineering, and software quality. He is a member of the IEEE
s Exams Tutor Measure Integrity E Grading … P Graduate Assistants Computerized Scholar
use that information to develop and testinterventions that may accelerate student development of engineering intuition.References1 Raskin, P. Decision-Making by Intuition--Part 1: Why You Should Trust Your Intuition. Chemical Engineering, 100 (1988).2 Gigerenzer, G. Short cuts to better decision making. (Penguin, 2007).3 Kahneman, D. Thinking, Fast and Slow. New York, NY: Farrar, Straus, and Giroux. (Macmillan, 2011).4 Elms, D. G. & Brown, C. B. Intuitive decisions and heuristics–an alternative rationality. Civil Engineering and Environmental Systems, 274-284 (2013).5 Dreyfus, S. E. & Dreyfus, H. L. A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition (1980
has been offered nine times since 2004, but this was the first time such an open-endedfinal project has been used. Anecdotally, the authors observed an obvious increase in excitementand enjoyment on the part of the students due to this project. We plan to continue to use suchprojects in the future.References [1] C. S. Burrus, “Teaching filter design using M ATLAB,” in Proceedings of the IEEE International Con- ference on Acoustics, Speech, and Signal Processing, pp. 20–30, Apr. 1993. [2] R. F. Kubichek, “Using M ATLAB in a speech and signal processing class,” in Proceedings of the 1994 ASEE Annual Conference, pp. 1207–1210, June 1994. [3] R. G. Jacquot, J. C. Hamann, J. W. Pierre, and R. F. Kubichek, “Teaching digital filter design
Structures, McGraw Hill, 1953.7. Ugural, A.C., Fenster, S.K., Advanced Strength and Applied Elasticity, Elsevier, 1975.8. Wang, C.T., Applied Elasticity, McGraw Hill, 1953.9. Timoshenko, S. Goodier, J.N., Theory of Elasticity, Third Edition, McGraw-Hill, 1934.10. Timoshenko, S. Gere, M.G., Theory of Elastic Stability, Second Edition, McGraw-Hill, 1961.11. Timoshenko, S., Woinowsky-Krieger, S., Theory of Plates and Shells, Second Edition, McGraw-Hill, 1959.12. Jones, R.M., Mechanics of Composite Materials, McGraw-Hill, 1975.13. Logan, D.L, A First Course in the Finite Element Method, Fifth Edition, Cengage Learning, 2016.14. Cook, R.D., Malkus, D.S., Plesha, M.E., Witt, R.J., Concepts and Applications of Finite Element Analysis, Fourth Edition
and the College ofDesign provided a 3-year contract for a shared faculty appointment and funds for travel,maintenance, and upgrades to the program with the the goal to be self-sustained and/or supportedin large part by external funds and grants.Session OverviewAs of June 2017, FLEx has delivered a total of 171 sessions both on campus and around the stateof Iowa (Figures 3 & 4). The number of sessions have continued to increase each year, with2017 poised to exceed 2016’s previously record total. Notable sessions and locations include theIowa State Fair, 4-H, Women in Science and Engineering (WiSE), Precollegiate Programs forTalented and Gifted, Upward Bound, and Science Bound.Sessions begin with a short 15-minute presentation on design
, S. McCoid, T. Jenkins, and E. Livingston. Tackling engagement in computing with computational music remixing. in Proceeding of the 44th ACM technical symposium on Computer science education. 2013. ACM.8. McCoid, S., J. Freeman, B. Magerko, C. Michaud, T. Jenkins, T. Mcklin, and H. Kan, EarSketch: An integrated approach to teaching introductory computer music. Organised Sound, 2013. 18(02): pp. 146-160.9. Winters, D., Virtuous and Vicious Cycles. The Social State?, 2003: pp. 43.10. Sharan, S. and I.G.C. Tan, Student engagement in learning. Organizing schools for productive learning, 2008: pp. 41-45.11. Haraldsson, H.V., Introduction to systems and causal loop diagrams. System Dynamic Course
Computer Simulations of Conceptual Domains. Review of Edu. Research, 68(2), 179-201.[5] Donovan, S. & Bransford, J. D. (2005). How Students Learn. Washington, D.C.: The National Academies Press.[6] Dunbar, K. N., & Klahr, D. (2012). Scientific Thinking and Reasoning. In K. J. Holyoak and R. G. Morrison (Eds.), The Oxford Handbook of Thinking and Reasoning (pp. 701- 718). London: Oxford University Press.[7] Goleman, D. (2006). Emotional Intelligence. New York: Bantam Dell.[8] Grover, S. & Pea, R. (2013). Computational Thinking: A Review of the State of the Field. Educational Researcher, 42 (1), 38-43.[9] Hawkins, J. (2004). On Intelligence. New York: Times Books.[10] Hebb, D. (1949). The Organization of Behavior. New
Paper ID #19852Improving the Requirements Inspection Abilities of Computer Science Stu-dents through Analysis of their Reading and Learning StylesMr. Anurag Goswami, North Dakota State University Anurag Goswami is a Ph. D. Candidate in the department of Computer Science at North Dakota State University. His main research interests include empirical software engineering, human factors in software engineering, and software quality. He is a member of the IEEE Computer Society.Dr. Gursimran Singh Walia, North Dakota State University Gursimran S. Walia is an associate professor of Computer Science at North Dakota State University
Publishing, Available at mylabsplus.com [Accessed February 2017]..2. MasteringEngineering by Pearson Publishing, Available at masteringengineering.com [Accessed February 2017]..3. Bonham, S., Beichner, R. & Deardorff, D., Online Homework: Does it Make a Difference? The Physics Teacher 39 (6), 293-296 (2001).4. Doorn, D., Janssen, S. & and O'Brien, M., Student Attitudes and Approaches to Online Homework. International Journal for the Scholarship of Teaching and Learning 4 (1) (2010).5. Dodson, J. R., The Impact of Online Homeowrk on Class Productivity. Science Education International 25 (4), 354-371 (2014).
Studies are explicit in this approach: each Case Study makes the point to consider issues inrealistic practices. Instructors can present the Case Study while guiding students into furtherstudy and discussion of the practical issues in SV&V. The Class Exercises are designed forinteraction in the classroom during group discussions. The instructor brings the question(s) andserves as a moderator to guide the discussion session. The instructor may also use the ClassExercise to lead students into subsequent group mini projects or individual mini projects. The“Instructor Notes” component of the Class Exercise discusses these possibilities. Students arelikely to find the Case Study Videos by their nature as multimedia, as highly engaging. Thesevideos
to foster higher student retention rates inintroductory computer programming courses.References: DiSalvo, B., & Bruckman, A. (2011). From interests to values. Communications of the ACM,54(8), 27-29.Newhall, T., Meeden, L., Danner, A., Soni, A., Ruiz, F., & Wicentowski, R. (2014, March). Asupport program for introductory CS courses that improves student performance and retainsstudents from underrepresented groups. In Proceedings of the 45th ACM technical symposium onComputer science education (pp. 433-438). ACM. Goldweber, M., Barr, J., Clear, T., Davoli, R., Mann, S., Patitsas, E., & Portnoff, S. (2013). Aframework for enhancing the social good in computing education: a values approach. ACMInroads, 4(1), 58-7Guzdial, M. (2009
, from http://www.usatoday.com/story/tech/2015/01/29/ky-computer-code-as-foreign-language/22529629/10. Victor, B. (2012). Learnable Programming. Retrieved March, 7, 2014, from http://worrydream.com/LearnableProgramming11. Ellis, R. (1994). The Study of Second Language Acquisition. Oxford: Oxford University Press.12. Krashen, S.D. (1981). Second Language Acquisition and Second Language Learning. Oxford: Pergamon Press.13. Krashen, S. D. (1982). Principles and practice in second language acquisition. Oxford: Pergamon Press.14. Krashen, S. D. & Terrell, T. (1983). The Natural Approach: Language Acquisition in the Classroom. London: Prentice Hall Europe.15. Williams, J. (1999). Memory, Attention and Inductive Learning
, R. M. (2002). Handbook of self-determination research. Rochester, NY: University of Rochester Press.Dörner, R., Göbel, S., Effelsberg, W., & Wiemeyer, J. (Eds.). (2016). Serious games: Foundations, concepts and practice. Cham: Springer International Publishing. doi:10.1007/978-3-319-40612-1Evans, J. S. B. (2009). How many dual-process theories do we need? One, two, or many?.Evans, J. S. B. (2003). In two minds: dual-process accounts of reasoning. Trends in cognitive sciences, 7(10), 454-459.Gee, J. P. (2003). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan.Hamari, J. ; Koivisto, J. ; Sarsa, H. (2014). Does Gamification Work? -- A Literature Review of
D ∆t Where, a = 2 - ; b = ; c = ;d = S 2 . ρ∆x 4 ρ∆x 4 ρ∆x 4 ρ∆xEquation (5) provides the displacement of section i of the manipulator at time step j + 1 . Itfollows from this equation that, to obtain the displacements y n -1, j+1 and y n, j+1 , displacements ofthe fictitious points y n + 2, j , y n +1, j and y n +1, j-1 are required. These can be obtained using theboundary conditions related to the
all’curriculum, in Improving science education: The contribution of research, J. Millar, Editor. 2000, McGraw-Hill Education: UK. p. 147-164.4. Varma, R., Making computer science minority-friendly. Communications of the ACM, 2006. 49(2): p. 129-134.5. Sjøberg, S., Investing all children in 'science for all', in Improving science education: The contribution of research, J. Millar, Editor. 2000, McGraw-Hill Education: UK. p. 165-186.6. Jenkins, E., 'Science for all’: Time for a paradigm shift, in Improving Science Education: The Contribution of Research, J. Millar, Editor. 2000, McGraw-Hill Education: UK. p. 207-226.7. Duschl, R., Making the nature of science explicit, in Improving science education: The
data such as parent educationlevel and ethnicity.5. AcknowledgmentsThe authors would like to thank the Cal State University Course Redesign with Technologyprogram for providing funding for this redesign effort. The authors also would like to thankundergraduate student assistants Chantel Ylaya and Sekani Robinson for assisting with the datacollection and analysis.6. References[1] Felder, R. M. and Brent, R. (2009). Active learning: An introduction. ASQ Higher Education Brief, 2 (4).[2] Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., and Wenderoth, M. P.(2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of theNational Academy of Sciences, 111, 8410-8415.[3
) to students and teachers whowish to master basic skills so as to enable focus on higher-level thought in math and computing.II. BackgroundGaming has become one of the most popular pastimes in world. In 2016, the EntertainmentSoftware Association surveyed over 4,000 U.S. households and found that 63% of householdshave at least one person who plays video games for at least 3 hours a week [5]. This amount is anincrease from the same study done in 2015 which found 42% of households had a gamer [7].Furthermore, Granic found that 97% of American children and adolescents play games for atleast one hour per day in the United States [9]. Since the first video games were released in the1950’s [10], games have improved on the visuals, storyline, and
type of discussion and the other activity to the other discussion tool. While in the Piazzaactivity, they engaged in a forum-based discussion and critiqued each others answers on Piazzafor the next 48 hours, the CONSIDER discussion phase was organized as two 24-hour rounds,where students engaged in a rounds-based discussion and posted their responses anonymously asdescribed in Section 2. Figure 3 shows an example discussion in CONSIDER. The student whosealias is S2 disagrees with S1’s initial post (indicated by the red background for that post) and pro-vides explanation for why she disagrees with S1 in the text box at the bottom of the screenshot. InPhase-3 for both conditions, students were asked to submit their final answers to the same ques
by NSF, Air Force and DoD. She have several publications regarding to the research and educational projects.Dr. James D Kiper, Miami University James Kiper is Chair and Professor of the Department of Computer Science and Software Engineering at Miami University. He teaches a variety of courses across the CS and SE curricula. His research is in the areas of software testing, software risk assessment, design rationale, and computer science and software engineering teaching and learning.Dr. Gursimran Singh Walia, North Dakota State University Gursimran S. Walia is an associate professor of Computer Science at North Dakota State University. His main research interests include empirical software engineering, software
or rubric, suitable for thespecific assignment.CPR1’s rubric asks students to provide two levels of peer evaluation: analytical (a list oforthogonal, atomistic criteria) and holistic (a single score based on the overall success of thesubmission). Within the CPR1 authoring template, the instructor composes the analyticalquestions, using as many individual items as need. The system automatically includes a last itemasking the student reviewer to rate the whole piece on a scale of 1 to 10. As indicated below,three types of rating scales are available for analytical items and one for the holistic rating. Response Type Scoring Method Display Analytical questions Binary – Yes / No
course was developed and deployed makes it model forpossible replicated at other institutions.Bibliography1. Hansen, R. E. (1995). Five principles for guiding curriculum development practice: The case of technological teacher education. Journal of Industrial Teacher Education. 32(2). Winter 1995.2. Arnold, A & Flumerfelt, S. (2012). Interlacing Mission, Strategic Planning, and Vision to Lean: Powerful DNA for Change. AASA Journal of Scholarship and Practice, 9(1), 26 - 47.3. Emiliani, B., Kensington, C., & Most, U. S. (2005). Lean in higher education.Center for Lean Business Management. Available at http://www. superfactory. com/articles/lean_higher_ed. Aspx.4. Emiliani, M. L. (2004). Improving business school
from http://dl.acm.org/citation.cfm?id=23306664. Ryan SJ Baker, Albert T. Corbett, and Vincent Aleven. 2008. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In International Conference on Intelligent Tutoring Systems, 406–415. Retrieved February 12, 2017 from http://link.springer.com/chapter/10.1007/978-3-540- 69132-7_445. Benjamin S. Bloom. 1984. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher 13, 6: 4–16.6. William G. Bowen, Matthew M. Chingos, Kelly A. Lack, and Thomas I. Nygren. 2014. Interactive Learning Online at Public Universities: Evidence from a Six-Campus
Excitement in the Classroom. ASHE- ERIC Higher Education Report 1, 1991.[2]. Briggs, T. 2005. Techniques for active learning in CS courses. J. Comput. Small Coll. 21, 2 (Dec. 2005), pp. 156- 165.[3]. Bull, G., Bell, R., Garofalo, J., & Sigmon, T. (2002). The case for open source software. Learning and Leading with Technology, 30(2), 10-17. Available: http://www.iste.org/LL/pdfs/index.cfm?sku=30210b.[4]. Clement, J. (2008). Creative model construction in scientists and students: The role of imagery, analogy, and mental simulation. Dordrecht, the Netherlands: Springer.[5]. Hundhausen, C., Douglas, S., and Stasko, J. A meta-study of algorithm visualization effectiveness. Journal of Visual Languages