The aim of our research is to investigate what methods can be used to explore learning experiences. In this case example, we describe how we extracted quantitative and qualitative data reflecting learning experiences from simulationbased learning (SBL) situations. Data collection was conducted in the fields of aviation and forestry. After the SBL situation, the students participated in a stimulated recall interview. The transcribed interview data were analysed using data-driven methods. To capture the dynamics in the (neuro)physiological signals associated with varying states of learning experiences, we recorded activity of the autonomic and central nervous systems. When analysing (neuro)physiological data, we focused on extracting reliable signatures reflecting both the state and the reactivity of the autonomic and central nervous systems. Later on, different data types will be integrated and analysed together. The aim of this article is to elaborate the extent to which different data types can be integrated in analysis to produce meaningful information about learning experiences. Our results based on the students’ interviews highlight the meaningfulness of the instructor’s guidance in SBL situations. We also show that it is possible to extract reliable features from (neuro)physiological signals measured during natural learning situations. These (neuro)physiological features also seem to vary depending on the phase of the simulation. Therefore, we conclude that by including (neuro)physiological measurements in research designs, it is possible to achieve a more comprehensive understanding of learning experiences. This type of multidisciplinary research is likely to provide novel insights in developing learning environments and guidance.