  Affective computing has garnered researchers' attention and interest in
recent years as there is a need for AI systems to better understand and react
to human emotions. However, analyzing human emotions, such as mood or stress,
is quite complex. While various stress studies use facial expressions and
wearables, most existing datasets rely on processing data from a single
modality. This paper presents EmpathicSchool, a novel dataset that captures
facial expressions and the associated physiological signals, such as heart
rate, electrodermal activity, and skin temperature, under different stress
levels. The data was collected from 20 participants at different sessions for
26 hours. The data includes nine different signal types, including both
computer vision and physiological features that can be used to detect stress.
In addition, various experiments were conducted to validate the signal quality.
