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

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.

Figure 1

Screen shot of the simulation students were building by using particle-spring systems to model an elastic object sliding over a rough surface. This model consists of $4\times 8$ mass particles connected with springs, up to eight springs for each particle. The springs are assigned damping in order to model the object with different elastic properties.

Figure 2

Sample epistemic network visualizing the epistemic frame given by an example of an interview excerpt.

Figure 3

Simplified epistemic networks before the problem-solving task. The size of each module corresponds to the importance of the module in the network, i.e., how much focus students put on associating between epistemic elements within that module, and the width of each link represents the information flow between modules. This network captures 97% of the node flow and 82% of the link flow.

Figure 4

Simplified networks after the problem-solving task. The size of each module corresponds to the importance of the module in the network, i.e., how much focus students put on associating between epistemic elements within that module, and the width of each link represents the information flow between modules. This network captures 95% of the node flow and 83% of the link flow.

Figure 5

Alluvial diagram showing how the modules in the epistemic networks rearrange between before and after the task.