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Mapping students' epistemic framing of computational physics using network analysis
Umeå University, Faculty of Science and Technology, Department of Physics.
2012 (English)In: Physical Review Special Topics : Physics Education Research, ISSN 1554-9178, E-ISSN 1554-9178, Vol. 8, no 1, 010115- p.Article in journal (Refereed) Published
Abstract [fa]

Solving physics problem in university physics education using a computational approach requires knowledge and skills in several domains, for example, physics, mathematics, programming, and modelling. These competences are in turn related to students’ beliefs about the domains as well as about learning. These knowledge and beliefs components are here referred to as epistemic elements, who 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 modelling 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, help 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.

Place, publisher, year, edition, pages
2012. Vol. 8, no 1, 010115- p.
Keyword [en]
physics education, computational physics, epistemic framing, simulation, network analysis, beliefs
National Category
Didactics
Research subject
didactics of physics
Identifiers
URN: urn:nbn:se:umu:diva-53233DOI: 10.1103/PhysRevSTPER.8.010115ISI: 000302640900001OAI: oai:DiVA.org:umu-53233DiVA: diva2:510858
Available from: 2012-03-20 Created: 2012-03-19 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Computational problem solving in university physics education: Students’ beliefs, knowledge, and motivation
Open this publication in new window or tab >>Computational problem solving in university physics education: Students’ beliefs, knowledge, and motivation
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Solving physics problem in university physics education with a computational approach requires knowledge and skills in several domains, for example, physics, mathematics, programming, and modelling. These competences are in turn related to students' beliefs about these domains as well as about learning, and their motivation to learn. The purpose of this thesis was to investigate the role of university physics students' knowledge, beliefs and motivation when solving and visualizing a physics problem using a computational approach. The results showed that expert-like beliefs about physics and learning physics together with prior knowledge were important predictors of the quality of performance. Feelings corresponding to control and concentration, i.e., emotions that are expected to be good indicators of students' motivation were also good predictors of performance. However, intrinsic motivation, as indicated by enjoyment and interest, together with beliefs expressing students' personal interest and utility value, did not predict performance to any higher extent. Instead, my results indicate that integration and identification of expert-like beliefs about learning and concentration and control emotions during learning are more influential on the quality of performance. Thus, 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. In order to investigate knowledge and beliefs structures network modeling has been applied as a novel tool for analysis. Students' epistemic frames are analyzed before and after the task in computational physics using a network analysis approach on interview transcripts, producing visual representations of mental models. The results show that students change their epistemic framing from a modelling 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, helped the students to develop a more consistent view of the importance of using physics principles in problem solving. When comparing students' framing with teachers,' it is shown that although teachers and students agree on the main features of simulation competence in physics, differences in their epistemic networks can be distinguished. For example, while teachers believe that numerical problem solving facilitates fundamental understanding of physics and mathematics, this is not obvious to students. This implies that university teachers need to be aware of these differences as well as students' beliefs in order to challenge students' expectations and to give support concerning the learning objectives of the assignment. 

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2012. 60 p.
Series
Studies in Science and Technology Education, ISSN 1652-5051 ; 54
Keyword
physics education, computational physics, simulation, beliefs, motivation, mental models, network analysis
National Category
Didactics
Research subject
didactics of physics
Identifiers
urn:nbn:se:umu:diva-53317 (URN)978-91-7459-398-3 (ISBN)
Public defence
2012-04-16, KBC-huset, KB3A9, Umeå universitet, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2012-03-26 Created: 2012-03-20 Last updated: 2012-03-22Bibliographically approved

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Bodin, Madelen

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