3Ms for Instruction, Part 2: Maple, Mathematica, and Matlab

Chonacky, Norman; Winch, David M.

doi:10.1109/mcse.2005.62

Cited by 7 publications

(3 citation statements)

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“…Autonomous behavior can be encouraged by guidance from teachers as well as the feedback from the results of their exercises, e.g., computer simulations and visualizations or a physical product such as a building or a construction. The problem-solving environment used in this study, MATLAB, requires programming skills but also provides support concerning functions and graphics in order to numerically solve a physics problem and visualize the result in a simulation [7]. The particle-spring model system together with the MATLAB environment used in this study is therefore considered to represent a situation which provides autonomy in the problem-solving process as well as feedback in terms of visualizations and compilation messages.…”

Section: A Simulation and Modeling In Physics Educationmentioning

confidence: 99%

“…Increasing computer capacity together with newly developed modeling and problemsolving environments have cleared the way for new approaches in physics education, for physics majors, engineering students, as well as for other physics students. By using a problem-solving environment such as, for example, MAPLE or MATLAB, students can numerically solve physics problems, create visual simulations, practice mathematical and physical modeling, and investigate physics phenomena, rather than just calculating an answer [3][4][5][6][7]. This approach differs from the traditional way of teaching and learning physics where problem solving is generally limited to analytical problems that can be solved with pencil and paper.…”

Section: Introductionmentioning

confidence: 99%

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Mapping university students’ epistemic framing of computational physics using network analysis

Bodin

2012

Phys. Rev. ST Phys. Educ. Res.

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Solving physics problem in university physics education using a computational approach requires knowledge and skills in several domains, for example, physics, mathematics, programming, and modeling. These competences are in turn related to students’ beliefs about the domains as well as about learning. These knowledge and beliefs components are referred to here as epistemic elements, which together represent the students’ epistemic framing of the situation. The purpose of this study was to investigate university physics students’ epistemic framing when solving and visualizing a physics problem using a particle-spring model system. Students’ epistemic framings are analyzed before and after the task using a network analysis approach on interview transcripts, producing visual representations as epistemic networks. The results show that students change their epistemic framing from a modeling task, with expectancies about learning programming, to a physics task, in which they are challenged to use physics principles and conservation laws in order to troubleshoot and understand their simulations. This implies that the task, even though it is not introducing any new physics, helps the students to develop a more coherent view of the importance of using physics principles in problem solving. The network analysis method used in this study is shown to give intelligible representations of the students’ epistemic framing and is proposed as a useful method of analysis of textual data

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Section: A Simulation and Modeling In Physics Educationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping university students’ epistemic framing of computational physics using network analysis

Bodin

2012

Phys. Rev. ST Phys. Educ. Res.

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“…For example, tools like Easy JAVA Simulation environment [4] and VPYTHON [7] allow the student to model physics problems by setting up the physics and mathematics needed to describe the simulation and create visual interactive simulations. Problem-solving environments, such as MATLAB and MAPLE, provide some support concerning functions and graphics, but also require programming skills in order to numerically solve a physics problem and visualize the result in a simulation [17].…”

Section: A Modeling and Simulations In Physics Educationmentioning

confidence: 99%

Role of beliefs and emotions in numerical problem solving in university physics education

Bodin

Winberg

2012

Phys. Rev. ST Phys. Educ. Res.

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Numerical problem solving in classical mechanics in university physics education offers a learning situation where students have many possibilities of control and creativity. In this study, expertlike beliefs about physics and learning physics together with prior knowledge were the most important predictors of the quality of performance of a task with many degrees of freedom. Feelings corresponding to control and concentration, i.e., emotions that are expected to trigger students’ intrinsic motivation, were also important in predicting performance. Unexpectedly, intrinsic motivation, as indicated by enjoyment and interest, together with students’ personal interest and utility value beliefs did not predict performance. This indicates that although a certain degree of enjoyment is probably necessary, motivated behavior is rather regulated by integration and identification of expertlike beliefs about learning and are more strongly associated with concentration and control during learning and, ultimately, with high performance. The results suggest that the development of students’ epistemological beliefs is important for students’ ability to learn from realistic problem-solving situations with many degrees of freedom in physics education

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Scientific programming with Java classes supported with a scripting interpreter

Papadimitriou

2007

IET Software

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The jLab environment provides a Matlab/Scilab like scripting language that is executed by an interpreter implemented in the Java language. This language supports all the basic programming constructs and an extensive set of built in mathematical routines that cover all the basic numerical analysis tasks. Moreover, the toolboxes of jLab can be easily implemented in Java and the corresponding classes can be dynamically integrated to the system. The efficiency of the Java compiled code can be directly utilized for any computationally intensive operations. Since jLab is coded in pure Java the build from source process is much cleaner, faster, platform independent and less error prone than similar C/C++/Fortran based open source environments (e.g. Scilab, Octave). Neuro-Fuzzy algorithms can require enormous computation resources and at the same time an expressive programming environment. We demonstrate the potentiality of jLab by describing the implementation of a Support Vector Machine (SVM) toolkit and by comparing its performance with a C/C++ and a Matlab version and across different computing platforms (i.e. Linux, Sun/Solaris, Windows XP).

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3Ms for Instruction, Part 2: Maple, Mathematica, and Matlab

Cited by 7 publications

References 0 publications

Mapping university students’ epistemic framing of computational physics using network analysis

Mapping university students’ epistemic framing of computational physics using network analysis

Role of beliefs and emotions in numerical problem solving in university physics education

Scientific programming with Java classes supported with a scripting interpreter

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