"Thermal comfort" (TC) -- how comfortable or satisfied a person is with the temperature of her/his surroundings -- is one of the key factors influencing the "indoor environmental quality" of schools, libraries, and offices. We conducted an experiment to explore how TC can impact students' learning. University students (n = 25) were randomly assigned to different temperature conditions in an office environment (25[degrees]C [right arrow] 30[degrees]C, or 30[degrees]C [right arrow] 25[degrees]C) that were implemented using a combination of heaters and air conditioners over a 1.25 hour session. The task of the participants was to learn from tutorial videos on three different topics, and a test was given after each tutorial. The results suggest that (1) changing the room temperature by a few degrees Celsius can stat. sig. impact students' self-reported TC; (2) the relationship between TC and learning exhibited an inverted U-curve, i.e., should be neither too uncomfortable nor too comfortable. We also explored different computer vision and sensor-based approaches to measure students' thermal comfort automatically. We found that (3) TC can be predicted automatically either from the room temperature or from an infra-red (IR) camera of the face; however, (4) TC prediction from a normal (visible-light) web camera is highly challenging, and only limited predictive power was found in the facial expression features to predict thermal comfort. [For the full proceedings, see ED599096.]