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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.

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.

In this study physics university teachers and undergraduate students were interviewed in order to capture their knowledge and beliefs structures about simulation competence and computational physics in university physics education. The analysis was done using a network analysis approach and the knowledge and beliefs structures were referred to as epistemic networks. The epistemic networks visualize how teachers and students conceptualize this particular learning situation and how these concepts are related. The results show 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 the these differences in order to meet students' expectations and to give support concerning the learning objectives of the assignment. The method chosen for this study shows that network analysis is a novel and useful method to analyze beliefs structures from textual data, such as interview transcripts. 

Solving physics problems in university physics education using a numerical approach requires knowledge and skills in several domains, for example, physics, mathematics, programming, and modeling. In this study students' mental models are monitored using interviews at several occasions during an assignment in computational physics. The interview data was analysed using a network analysis approach. Interview transcripts were coded according to the context dependent concepts that were used to define the particular context and situation of this assignment. The adjacency of concepts in the transcripts was assumed to reflect the associations between them made by students, and thus representing students' mental models of the problem solving situation at the time of the interview. For each student a network was built where the concepts were nodes and their adjacency formed the links between them. The changes in students' mental models between the interview occasions gave important information about what the students were focusing on at different stages of the solution process. What students focused on at the different interview occasions was assumed to be an indication of what they believed was useful in solving the task. The visualization of the mental models showed that at the beginning students were concerned about how to deal with writing the Matlab code that was needed to model the problem. As students got more comfortable with the coding process, the physics needed to assure that their simulation was following physics principles, such as energy conservation, became more and more central in their narratives. This study gives important contribution to how networks can be used to model students' thinking in a particular context and provides important knowledge about students' progress in a task in computational physics.