Speaker
Description
Modern physics is deeply shaped by the development and use of models. From detector simulations and statistical inference to the interpretation of experimental data, scientific knowledge is rarely derived from observations alone but emerges through layers of modeling that represent physical processes, instruments, and uncertainties.
At the same time, the role of models as representations of the physical world often remains implicit in how physics is taught in schools. In school contexts, physical laws and formulas can appear as direct descriptions of reality rather than as idealized representations with specific assumptions, limited domains of validity and associated uncertainties. As a consequence, learners frequently encounter difficulties in revising or replacing previous models when more refined ones are introduced. More broadly, this also limits their general understanding of the provisional character of scientific knowledge and the role of uncertainties in scientific reasoning.
Drawing on examples from astroparticle physics and perspectives from physics education research, this talk examines models as epistemic tools that mediate between phenomena, experiments, and observable data. Making this model‑based structure of physics explicit - both in research practice and in education - can help bridge the perceived divide between “school physics” and “real physics”. Developing an understanding of models as representations is therefore not only central to learning physics, but also increasingly important for navigating a world shaped by scientific modeling, data analysis, and algorithmic methods.