Pitfalls in data-based argumentation in the physics lab

Although students conduct and analyze experiments involving quantitative measurement data, they often do not use these data to justify derived physical hypotheses (e.g., Pols et al., 2021). Instead, they tend to rely on theoretical concepts, personal experiences, or arguments from authority (e.g., Sandoval & Millwood, 2005). Even targeted promotion of data literacy – for instance, through effective learning apps – while necessary, is not a sufficient condition for data-based reasoning (Kardaş, 2023).

Against this background we conducted two quantitative and one qualitative study that identified pitfalls in data-based argumentation. The talk introduces an initial approach to developing a model that explains how such pitfalls arise in the physics lab for students. The aim of this model is to gain insights into the causes of these pitfalls and, building on this understanding, to design a training to overcome these obstacles.