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Incoherent scatter radar (ISR) techniques provide reliable measurements for the analysis of ionospheric plasma. Recent developments in ISR technologies allow the generation of high-resolution 3D data. Examples of such technologies employ the so-called phased-array antenna systems like the AMISR systems in North America or the upcoming EISCAT_3D in the Northern Fennoscandia region. EISCAT_3D will be capable of generating the highest resolution ISR datasets that have ever been measured. We present a novel fast computational strategy for the generation of high-resolution and smooth volumetric ionospheric images that represent ISR data. Through real-time processing, our computational framework will enable a fast decision-making during the monitoring process, where the experimental parameters are adapted in real time as the radars monitor specific phenomena. Real-time monitoring would allow the radar beams to be conveniently pointed at regions of interest and would therefore increase the science impact. We describe our strategy, which implements a flexible mesh generator along with an efficient interpolator specialized for ISR technologies. The proposed strategy is generic in the sense that it can be applied to a large variety of data sets and supports interactive visual analysis and exploration of ionospheric data, supplemented by interactive data transformations and filters.

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.

Research on how epistemic beliefs influence students' learning in different contexts is ambiguous. Given this, we have examined the relationships between students' scientific epistemic beliefs, their problem solving, and solutions in a constructionist computer-simulation in classical mechanics. The problem solving process and performance of 19 tenth grade students, with different scientific epistemic beliefs, was video recorded and inductively coded. Quantitative analysis revealed that different sets of epistemic beliefs were conducive to different aspects of students' problem solving process and outcomes.  Theoretically sophisticated beliefs were in general associated with logical strategies and high solution complexity. However, authority dependence was associated with high degree of adherence to instructions. Hence, there might not be a universal relationship between theoretical sophistication of students' epistemic beliefs and quality of learning outcomes. We suggest that the conduciveness to desired outcomes is a better measure of sophistication than theoretical non-contextualized a priori assumptions.

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.

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.

It is a widely held view that students’ epistemic beliefs influence the way they learn and think in any given context. However, in the science learning context, the relation between the sophistication of epistemic beliefs and success in scientific practice is sometimes ambiguous. Taking this inconsistency as a point of departure, we examined the relationships between students’ scientific epistemic beliefs (SEB), their epistemic practices, and hence their epistemic cognition in a computer simulation in classical mechanics. The 19 tenth grade students’ manipulations of the simulation, spoken comments, behavior, and embodied communication were screen and video-recorded and subsequently described and coded by an inductive approach. The screen and video recordings were triangulated with a stimulated recall interview to access a broader understanding of the dynamic processes of epistemic cognition. Our findings focusing on three different students reveal a dynamic pattern of interactions between SEB and knowledge, i.e., epistemic cognition, showing how epistemic cognition can be understood in a specific problem solving context due to the actions the student express.

It is a widely held view that students’ epistemic beliefs influence the way they learn and think in any given context. However, in the science learning context, the relation between the sophistication of epistemic beliefs and success in scientific practice is sometimes ambiguous. Taking this inconsistency as a point of departure, we examined the relationships between students’ scientific epistemic beliefs (SEB), their epistemic practices, and hence their epistemic cognition in a computer simulation in classical mechanics. The 19 tenth grade students’ manipulations of the simulation, spoken comments, behavior, and embodied communication were screen and video-recorded and subsequently described and coded by an inductive approach. The screen and video recordings were triangulated with a stimulated recall interview to access a broader understanding of the dynamic processes of epistemic cognition. Our findings focusing on three different students reveal a dynamic pattern of interactions between SEB and knowledge, i.e., epistemic cognition, showing how epistemic cognition can be understood in a specific problem solving context due to the actions the student express.

In this case study, basic school (8th grade) students’ (N=24) problem-solving processes are studied while implementing a design-based science learning (DBSL) approach. DBSL combines the processes of engineering design with scientific inquiry attempting to engage students in scientific reasoning through solving authentic problems. Three DBSL modules are developed by the research team within which students are expected to design an ice cream and a soda „machine“ plus a battery from simple and easily available materials. It is expected to find out: (a) how science knowledge learned before or during the inquiry session within a DBSL module is transferred to and applied in a design situation; (b) what are the characteristics of students’ scientific reasoning while solving different types of problems in DBSL setting; (c) what kinds of peer interactions can be observed during DBSL activities? Data are gathered by video recorded classroom observations and students’ written reports. Written transcripts of classroom discourse are analyzed interpretively using qualitative content analysis approach. Data analysis is expected to be finalised by May, 2016. The findings from this study have potential to improve our understanding of how students construct knowledge while solving complex problems in DBSL setting but also provide practical guidelines for teachers to facilitate further adoption of DBSL in science classroom.

Research has shown that students’ epistemic beliefs influence the way they learn, think and reason in any given context (Schommer-Aikins, 2004). However, in the science learning context, the relationship between the level of epistemic sophistication, learning, and learning outcomes is sometimes ambiguous (Elby & Hammer, 2001). Taking this result as a point of departure, we examined the relationships between students’ scientific epistemic beliefs (SEB), their approaches to a computer simulated task, and the quality of their solutions. 19 tenth grade students, with different SEB, were selected to participate in a constructionist computer-simulation in classical mechanics. Constructionist learning environments emphasize the scope for students’ to take control of their own learning, draw their own conclusions, and use their own knowledge in order to construct objects (Harel & Papert, 1991). Students’ manipulations of the simulation and any spoken comments were video-recorded and subsequently coded by an inductive approach. Relationships between students’ SEB and problem solving quality were explored by hierarchical orthogonal partial least squares analysis. The results revealed that different sets of SEB were conducive to different aspects of students’ problem solving process and outcomes.  Theoretically sophisticated beliefs were in general associated with logical strategies and high solution complexity. However, our results suggest that there might not be a universal relationship between the degree of theoretical sophistication of students’ SEB and quality of learning outcomes. The relationship can only be understood in terms of the actions they induce, and the results of these actions. It is therefore of great importance to further explore the productiveness of SEB in different types of learning situations.