AC 2010-401: A LEARNING-BY-DOING APPROACH TO TEACHINGCOMPUTATIONAL PHYSICSRadian Belu, Drexel UniversityAlexandru Belu, Case Western Research University Page 15.46.1© American Society for Engineering Education, 2010 A Learning-by-Doing Approach to Teaching Computational PhysicsAbstractScientific research is becoming unthinkable without computing. The ubiquity ofcomputerized instrumentation and detailed simulations generates scientific data involumes that no longer can be understood without computation. Computational physics isa rapidly growing subfield of physics and computational science in large part becausecomputers can solve previously intractable problems or simulate natural
. In introductory physics courses a rich understanding of situations is more usefulthan procedural ability [1]. When students start to learn calculus-based physics the emphasis is shifted.Although situational understanding and the ability to identify a problem remain crucial to deepunderstanding and problem solving [2, 3], learning to carry out solution procedures simply consumes alarge portion of the students’ attention and takes up the available time. Therefore, it has beenunavoidable that more challenges are postponed until procedural mastery has been achieved. Recentdevelopment in user-friendly computer algebra software may offer new opportunities and tools to dosome more substantial analysis in calculus-based physics courses.This paper
Paper ID #13433Engineering and Physics Students’ Perceptions about Learning Quantum Me-chanics via Computer SimulationsMs. Yu Gong, Purdue University Yu Gong is a graduate student in the School of Engineering Education and School of Electrical and Computer Engineering at Purdue University. She holds B.S, M.S. degrees in electrical engineering from Jiangsu University in China. Her researches focus on model-based learning in nanotechnology education.Tugba Yuksel, Purdue University, Curriculum and Instruction & Dept. of Physics and Astronomy Tugba Yuksel is a Ph.D. candidate in curriculum and Instruction department at Purdue
, mathematics is the toughest, as it takesconsiderable time and effort to learn. In our school, the background of students is very diverse,and some of them even have trouble in doing simple integrals. Fortunately, technology comes tothe rescue. SAGE© is an open source symbolic computation tool, and it can be used for symbolicderivation, so every student can find the derivative, integral, and even gradient of functionseasily. In addition, it also supports programming in Python© style. With the challenge ofmathematics alleviated, more time is available to cope with the challenges of other issues, suchas new concepts and approaches. At the end of the semester, students were tested withConceptual Survey of Electricity and Magnetism, as well as surveyed on
AC 2008-180: USING COMPUTERS TO SUPPORT QUALITATIVEUNDERSTANDING OF CAUSAL REASONING IN ENGINEERINGDavid Jonassen, University of Missouri Dr. David Jonassen is Distinguished Professor of Education at the University of Missouri where he teaches in the areas of Learning Technologies and Educational Psychology. Since earning his doctorate in educational media and experimental educational psychology from Temple University, Dr. Jonassen has taught at the Pennsylvania State University, University of Colorado, the University of Twente in the Netherlands, the University of North Carolina at Greensboro, and Syracuse University. He has published 30 books and numerous articles, papers, and reports on
establishment of interdisciplinary project work in the early stages of the Bachelordegree program.The courses Information Systems and Programming in the first and second semester of theundergraduate degree program form the basis for early project and problem based learning. Inthe first semester a basic understanding of computers and data processing systems isestablished, and the foundations of the programming language C# are introduced. C# is amodern, object-oriented programming language of the C family that enables the students todevelop graphical user interfaces with comparatively little effort. In the second semester thestudents’ knowledge of software development and software documentation is deepened. Inaddition, the students have to complete
Paper ID #17238Integration of High Performance Computing into Engineering Physics Edu-cationDr. Evan C. Lemley, University of Central Oklahoma Professor Lemley teaches thermo-fluid engineering and works with undergraduates to perform fluid dy- namics research that is mostly focused on small scale flow problems. He is currently an Assistant Dean of Mathematics and Science and a Professor of Engineering and Physics at the University of Central Oklahoma, his home institution for more than fifteen years. Previously, Professor Lemley worked as a mechanical engineer in the power industry. His bachelor’s degree is in physics from
applications and user needs.Co-author (Gutierrez) is currently an undergraduate student in computer science and was hired asa Student Cluster Intern (SCI) in July 2015 to assist and learn cluster system administration. Allcoauthors installed and tested a number of software packages from July - November of 2015.These software packages, both open source and commercial, were ones requested by UCOfaculty.Initial User Intake and TrainingBy September 2015 system users were given access to the system. The two modes of using theBuddy cluster are through secure shell (ssh) command line access and through a web-basedfront-end created by ACT called eQUEUE. Documentation was written and distributed to userson how to gain access and use some software on the
AC 2009-556: SYNERGISTIC LEARNING ENVIRONMENT USINGBLACKBOARD LEARNING CELLSAdrian Ieta, State University of New York, OswegoRachid Manseur, State University of New York, OswegoThomas Doyle, McMaster University Thomas E. Doyle holds a Ph.D. in Electrical and Computer Engineering Science (2006) from The University of Western Ontario, Canada. He also holds a B.E.Sc. in Electrical and Computer Engineering, a B.Sc. in Computer Science, and an M.E.Sc in Electrical and Computer Engineering from The University of Western Ontario. He worked on industrial projects with PlasSep Ltd, within the Applied Electrostatics Research Centre and the Digital Electronics Research Group at The University of Western
laboratory is used toreinforce concepts learned in class through the use of simulations and tutorials, while alsointroducing students to computational modeling using MATLAB. Descriptions of all laboratoriesdeveloped for this course can be found in the course’s curriculum development webpage.4We have extended two tutorials adapted from materials from the University of Colorado-BoulderPhET5 simulations to include a computational component along with short validationexperiments: Laboratory: The Wave Equation Conceptual goal: Become comfortable with the mathematical formalism of differential equations with boundary conditions. Understand how applying boundary conditions leads to quantization. Experimental goal: Use the pattern of standing
, particularlyin the beginning of the semester. Having enough support the first few weeks is key to asuccessful experience as students get easily frustrated when their program does not run and theydo not understand the error messages. Currently laboratories of 12 to 16 students are staffed bythe instructor plus one or two teaching assistants. Students work independently, but they sit intables of four and are encouraged to discuss the lab with each other.In order to have students practice the computational skills they are acquiring and to further tie thelecture and laboratory together, we have developed short homework exercises that closely tie towhat students are learning in lecture and in lab. For example, after students fill out an onlineclass survey
students rank-orderedapproximately 6 – 10 items that were important to them as they prepared to study for any course.Students were also asked to describe how they used each learning tool they identified. For thepurposes of this paper, Tables I and II illustrate the students’ number one item on their rank-ordered lists. Table I. Physics 100 (n = 43) Most Essential Learning Tool Needed to Study Number 1 Learning Tool Number of Responses % of Responses textbook, class notes 12 27.9 comfortable work place/quiet 11 25.6 computer 6
participants in theprocess, not passive listeners.”3 In this paper I describe a project to significantly improve student learning in my one semestersophomore course in modern physics for engineers by introducing technologies to enhance activelearning. None of the technologies is new; I only am describing my own experiences with aparticular combination, a classroom response system in conjunction with a tablet computer, acombination which is also not new. The Physics Education Research Group, University ofMassachusetts, web site provides many links to information about the technologies used here asdeveloped by themselves and a number of universities and companies4-6. Another excellentsource, emphasizing their own product, Classroom Presenter, is the
instruction (inquiry- oriented approached). Inquiry instructional strategies averagedthirteen percentile points higher in achievement measure over traditional text-lecture modes ofinstruction.[2] Heise, D., Asserting the Inherent Benefits of Hands-On Laboratory Projects vs. ComputerSimulations, Consortium for Computing Sciences in Colleges, Central Plains Conference, (2006).[3] Felder, R. M., Felder, G. N. and Deitz, E. J., A longitudinal study of engineering student performanceand retention. V. Comparisons with traditionally-taught students. J. ENGNG Edu., 1998, 87, 469-480.[4] Bransford, J. D., et al., How People Learn: Brain, Mind, Experience, and School, Washington, DC:National Academy Press, 2000.[5] Mataric, M. J., Robotics Education for All
AC 2008-104: IMPACT OF UNDERGRADUATE ROBOTICS RESEARCH ONRECRUITING FRESHMAN STUDENTS TO MAJOR IN ENGINEERINGPHYSICS, AND COMPUTER SCIENCE FIELDSBaha Jassemnejad, University of Central OklahomaWei Pee, University of Central Oklahoma Engineering Lab AssociateMathew Mounce, University of Central Oklhoma Student Page 13.697.1© American Society for Engineering Education, 2008 The Role of Designing Robots to Promote the Interest of Incoming College Freshman Students to Major in Engineering and Computer Science FieldAbstractThe goal of this robotic research activity in the UCO’s Engineering and Physics department wasto promote
AC 2011-20: TRANSFORMATIVE LEARNING EXPERIENCE FOR IN-COMING FRESHMEN ENGINEERING STUDENTS THROUGH ROBOTICSRESEARCHBaha Jassemnejad, University of Central Oklahoma Chair and Professor of Engineering and Physics DepartmentMr. Wei Siang PeeMr. Kevin RadaMontell Jermaine Wright, University of Central Oklahoma, Robotics Research A freshmen engineering student. Attended Choctaw high school. Likes to fix computers and solve tech- nical problems in his spare time.Kaitlin Rose Foran, University of Central OklahomaEvan C. Lemley, University of Central Oklahoma Page 22.1545.1 c American Society for
: Page 12.170.2electromagnetic waves, including interference and diffraction; quantum mechanics, includingmodels of atoms, molecules and solids based on one dimensional finite potential wells; andspecial relativity, including Lorentz transformations, applications of mass-energy to fission andfusion, and a brief introduction to general relativity as applied in the global positioning system. An important aspect of the course is encouraging students to take responsibility for theirown learning. A great amount of information is posted on our WebCT site so that a student doesnot have to come to every class. During lecture I write on a tablet computer in its journal modeinstead of on the boards, and often write also on PowerPoint slides, which
). Page 13.1112.13[33] Hein, T. L. & Budny, D. D. (1999). Research on learning style: Applications in science and engineering. Electronic proceedings of the International Conference on Engineering and Computer Education (ICECE), Rio de Janeiro, Brazil.[34] Hein, T. L. & Budny, D. D. (1999). Teaching to students’ learning styles: Approaches that work. Electronic proceedings of the Frontiers in Education (FIE) Conference, San Juan, Puerto Rico. IEEE Catalog number 99CH37011. ISBN 0-7803-5643-8.[35] Hein, T. L. & Zollman, D. A., (2000). Digital video, learning styles, and student understanding of kinematics graphs. Journal of SMET Education: Innovations and Research, 1(2), 17 - 29.[36] Woolf, L. D. (2000). Seeing the
Paper ID #11230Active-learning for Physics (Electromagnetism) teachers in an EngineeringCourseProf. Rodrigo Cutri P.E., Maua Institute of Techonology holds a degree in Electrical Engineering from Maua Institute of Technology (2001), MSc (2004) and Ph.D. (2007) in Electrical Engineering - University of S˜ao Paulo. He is currently Titular Professor of Maua Institute of Technology, Professor of the University Center Foundation Santo Andr´e, and consultant - Tecap Electrical Industry Ltda. He has experience in Electrical Engineering with emphasis on Industrial Electronics and Engineering Education, acting on the following
Paper ID #18339Downstream Impact of an Active-Learning-Based Engineering Physics - Me-chanics CourseDr. Timothy J. Garrison, York College of Pennsylvania Timothy Garrison is Chair of the Engineering and Computer Science Department c American Society for Engineering Education, 2017 Downstream Impact of an Active-Learning-Based Engineering Physics – Mechanics CourseAbstractAt the 2014 and 2015 Annual ASEE conferences, the author presented papers on a completelyrestructured engineering physics - mechanics course. The traditional physics course structure,consisting of a separate lecture (3
. Oncethey had validated their simulation approaches, the students were to use the computational andexperimental methods to find the potential due to a more complex geometry that they could notcompute analytically.Three difficulties became apparent in this process. First, we learned that even when testingindicated that students were able to solve problems and answer conceptual questions, they mightlack the ability to transfer that knowledge to a practical problem. Second, we found that lab skillsthat students learned in other classes did not transfer to a new class. Third, students’ lackedconfidence in drawing conclusions about experimental results and wrote conclusions withoutsufficient reflection.In this paper, we discuss the experiments and
literature searching experience prior to this project”.Other students also pointed out that they had to do additional readings in order to progress in theproject. “I had to do research on voltage regulation, batteries, solar cells, and many other topics dealing with powering the motes. Also calibration and construction of temperature sensors”. “My project resulted in substantial additional research in wireless propagation, antenna and waveguide theory, basic electronic design, and computer programming”.Overall, the participants thought that wireless sensor networks can be used as an educational toofor learning science concepts and 60 percent indicated that they would definitely recommend
platform is the frequent online quiz, which givesstudent access to quizzes by means of modern technology (smartphones, computers, tablets, etc.),which catches their attention and makes the learning process friendlier. In an extra online classquiz, students can review the taught physics concepts periodically, while avoiding accumulatingsubjects without previously studying for final tests. Through this online quiz, students can alsocontrol their learning, by identifying their points of doubt, correcting themselves by means offeedback, and asking for help from the online moderator. Another important advantage is thateach student can learn at his/her own pace, since the activity is not synchronous; rather, it givesthem the opportunity to review
Paper ID #17350An Evaluation of a Digital Learning Management System In High SchoolPhysics Classrooms (Evaluation)Dr. Meera N.K. Singh PEng, University of Calgary Meera Singh obtained her PhD. from the University of Waterloo, Canada, specializing in fatigue life prediction methods. Following her PhD studies, she joined the Department of Mechanical Engineering at the University of Manitoba, Canada, where she was a faculty member for 12 years. During that time, she conducted research primarily in the area of the fatigue behaviour of composite materials, regularly taught courses in applied mechanics, and served as the Chair
is now an Assistant Professor at Murray State University in the Department of Engineering and Physics. Page 13.988.1© American Society for Engineering Education, 2008 PRACTICAL APPROACHES TO PROJECT-BASED LEARNING INCORPORATING PEER FEEDBACK IN ORDER TO ENHANCE CREATIVITY IN ENGINEERING COURSESWe report on innovative approaches to integrating student feedback into teachingengineering physics courses. Project-based learning, presentations, and peer-feedbackcontributed to an enhanced class experience. This interactive method was applied inOptics and Engineering Measurements courses. The Optics course was mainly focused
, computer architecture, and peripheral hardware issues are discussed throughout thecourse so that the students gain a working knowledge of these topics. Hands-on learning isemphasized through simulation, hardware and software labs, and a final project. Also weemphasize the system-level design, high-level language, and connections between the Clanguage, assembly, and the underline hardware architecture. The outcomes of this course haveshown that this approach (1) inspires engineering physics students to be interested inmicrocontrollers, (2) provides students with a less compartmentalized view of manyhardware/software topics, and finally (3) underscores the importance of system-level design withjust enough understanding about individual components or
25.198.3nano-scale systems), and then to solids, with applications interwoven throughout. Time in thelaboratory is used to reinforce concepts learned in class through the use of simulations andtutorials, while also introducing students to computational modeling using Matlab, a high-levelcomputer language that is widely used in scientific and engineering applications. Both Physicsand Electrical Engineering students use Matlab throughout their upper-level courses.Furthermore, competency in Matlab is an in-demand skill on the jobs market. The computationalmodeling is followed by brief, illustrative experiments to test the validity of the models. Thecommon thread in the laboratories is the “particle-in-a-box,” as shown in the following diagram:Classical
Education in the United States, 2011. The Sloan Consortium, November 2011. See also URL http://sloanconsortium.org2. Twigg, C.A. Innovation in Online Learning: Moving Beyond No Significant Difference. The Pew Learning and Technology Program, 2001.3. Wasfy, H.M., Wasfy, T.M., Peters, J.M. and Mahfouz, R.M., "Virtual Reality Enhanced Online Learning Environments as a Substitute for Classroom Instruction." ASME DETC2011-48826, Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2011), Washington D.C., August 2011. Page
community around Stanford University’s d.School (Carleton and Leifer, 2009) and DesignFactory Global Network initiated and coordinated by Aalto University (Oinonen, 2012)Before joining the course, the students were asked about their experience in similar projectcourses, project work, and international collaboration, and over half of them (n=26) had workedin an international team several times, and only a small fraction (n=8) had no internationalteamwork experience.Their average self-reported time of using a computer was 6,5 hours per day, and most of themidentified being active in social media (28 yes, 17 sometimes, 1 no). The students also had afairly positive approach towards computer-based learning tools, averaging to 8.4 out of 10 inLikert
, 2006. Conference Proceedings. Page 13.1343.128. Gardner, H. (1983). Frames of mind: the theory of multiple intelligences. New York: Basic Books, Inc.9. Gray, J.T., Camp, P.J., Holbrook, J., Owensby, J., Hyser, S. and Kolodner, J.L. (2001). Learning by Design Technical Report: Results of Performance Assessments for 1999-2000 and 2000-2001. College of Computing, Georgia Institute of Technology, Atlanta, GA.10. Kolodner, J.L. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann.11. Kolodner, J.L., Crismond, D., Gray, J., Holbrook, J., & Puntambekar, S. (1998). Learning by design from theory to