Physiological measures associated with emotional expressions have been used extensively in lab- and, more recently, digital-learning settings. However, the portable and ubiquitous nature of hardware that measures these physiological features makes them particularly useful in situations where you do not want the hardware to be too obtrusive, like in contexts of informal learning. In this proof-of-concept study, we apply skin conductance methods that measure Electrodermal Activity (EDA) to a family everyday activities context, in which a parent and their children, complete several science learning activities, while being recorded by both video and EDA hardware. We analyse the resulting data in three different ways: (i) a peak analysis in software recommended by the hardware provider, (ii) a conventional, qualitative microanalysis, and (iii) a method mainly used by econometricians to discover 'structural breaks' in time series data. We conclude that all three provide a piece of the overall puzzle, revealing up- and down-sides of each method.