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

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

A lgodoo (http://www.algodoo.com) is a digital sandbox for physics 2D simulations. It allows students and teachers to easily create simulated "scenes" and explore physics through a user-friendly and visually attractive interface. In this paper, we present different ways in which students and teachers can use Algodoo to visualize and solve physics problems, investigate phenomena and processes, and engage in out-of-school activities and projects. Algodoo, with its approachable interface, inhabits a middle ground between computer games and "serious" computer modeling. It is suitable as an entry-level modeling tool for students of all ages and can facilitate discussions about the role of computer modeling in physics.

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It is a widely held view that students’ epistemic beliefs influence the way they think and learn in a given context, however, in the science learning context, the relationship between sophisticated epistemic beliefs and success in scientific practice is sometimes ambiguous. Taking this inconsistency as a point of departure, we examined the relationship between students’ scientific epistemic beliefs (SEB), their epistemic practices, and their epistemic cognition in a computer simulation in classical mechanics. Tenth grade students’ manipulations of the simulation, spoken comments, and behavior were screen and video‐recorded and subsequently transcribed and coded. In addition, a stimulated recall interview was undertaken to access students’ thinking and reflections on their practice, in order to understand their practice and make inferences about their process of epistemic cognition. The paper reports on the detailed analysis of the data sets for three students of widely different SEB and performance levels. Comparing the SEB, problem solutions and epistemic practices of the three students has enabled us to examine the interplay between SEB, problem‐solving strategies (PS), conceptual understanding (CU), and metacognitive reflection (MCR), to see how these operate together to facilitate problem solutions. From the analysis, we can better understand how different students’ epistemic cognition is adaptive to the context. The findings have implications for teaching science and further research into epistemic cognition.