In science, researchers explain the world using scientific models that they have developed with the help of observations and experiments. These models can be represented in many different ways, both visually and linguistically. In my research, I study how different forms of representations are used in learning and how this affects learning. The focus of the research is partly on the development of systems thinking for sustainability and partly on learning about scientific concepts. I participate in research concerning ages from secondary school up to university level, as well as learning at the science centre at Norrköping Visualisation Centre C.
Photo credit Thor Balkhed future sustainable society requires citizens to have the knowledge and willingness to act for sustainability, as well as confidence that change is possible. This is called action competence. Based on previous research, I believe that systems thinking can be key to developing this competence. In a project that started in 2026, I am working with colleagues to investigate the links between students' systems thinking, environmental views and action competence for sustainability in relation to the Swedish upper secondary school subject Science studies (sv. Naturkunskap). The project is funded by the Swedish Research Council (VR 2025-05724).
An important aspect that affects learning is the possibility of interacting with a visualization. Together with colleagues, I have investigated how learning in interactive virtual environments can take place in several previous projects. For example, in one project, we investigated how interaction with an adaptive visualization of the carbon cycle can support students' development of systems thinking. The carbon cycle is an important part of secondary school chemistry and biology teaching, and knowledge of it is necessary to understand how human intervention in the carbon cycle leads to global warming. In the project, we developed an interactive visualization that is adaptive, meaning that it adjusts the difficulty level of the tasks students are given based on how well they have performed on previous tasks. The project is funded by the Swedish Research Council (VR 2020-05147) with Konrad Schönborn as PI.
