This study proposes the new classification of analyzing academic emotions and facial expressions measured in online video-based learning contexts. To this end, we conducted a qualitative single-subject research on a learner to unpack the learner’s academic emotions and facial expressions revealed during the online learning process. Drawn from the review of relevant literature, seven types of emotions were classified as positive or negative. The types of positive emotions captured are: 1) excitement, enjoyment, and pleasure 2) confidence, and 3) aspiration, enthusiasm and expectation. The types of negative emotions are: 1) fear and anxiety, 2) embarrassment and shame, 3) frustration and alienation, and 4) boredom. Recall interview and observation were also used to infer the facial expressions matched to each emotion type, which were measured through the movements of eyes, eyebrows, lip, and jaw. Overall, 3 to 4 facial expressions were revealed per emotion. Based on the results, we propose a new scheme to classify academic emotions in online video-based learning, and suggest some areas for future research on utilizing affective computing technology in academic emotions, and appropriating emotional support in online learning.