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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. 

The Algodoo 2D simulation environment is built upon advanced real-time physicssimulation technology. (Demonstration video is provided on Youtube:http://www.youtube.com/watch?v=qa9xn-xYQQk)  It has a graphical user interface whichmakes it possible for anybody to create and explore scenes that are physicallyinteractive. In Algodoo it is possible to create and edit scenes using simple drawingtools, save and load scenes, start and stop simulation, interact with simulation byclick, drag, tilt and shake. Color traces, force and velocity vectors can be addedfor enhanced visualization. The built-in physics simulation engines treat rigidbodies, fluids, chains, gears, gravity, contacts, friction, restitution, springs,hinges, lock, motors and also laser rays and optics. Algodoo is based on highlycompetitive technologies for interactive multiphysics simulation, includingvariational mechanical integrators and high performance numerical methods. Algodoo isparticularly intended to be used in order to encourage and make use of the students’own creativity in order to construct knowledge and giving the student a sense ofownership of their own learning. The sense of having control of the learningsituation is considered being one of the most important factors that generatemotivational behaviour. Algodoo offers new strategies for working with computers inteaching and learning physics. This study demonstrates the use of Algodoo withteacher students in physics and also with 8-9 year old girls in a science class.

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. 

In order to make careers in science visible to girls, 24 girls from an uppendary school science program were chosen through a contest, to participate in a trip to Grenoble, France, and visit the research facilities ESRF (European Synchrotron Research Facility) and ILL (Institut Laue-Langvin:Neutrons for Science). The girls were presented a program, which included talking to researchers, doing experiments, presenting results and experiencing the environment where science is performed. In addition, 11 teachers participated in a similar trip in order to gain knowledge and experience to contribute to development of classroom material where science is brough into the classroom. Results show that attitudes towards science as a career choice was more positive after the trip than before. The teachers' trip resulted in several ideas that are still to be developed through a research-based design circle involving teachers, reserarchers and eventually students, in classroom activities making use of science and scientific results.

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.

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.

Algodoo (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.

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.