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Past research has demonstrated conclusive associations of facial movements and learning-centered emotions. This paper reports the first of its kind to explore students’ (N=50) facial behavior, emotions and learning in a narrative game. An automatic facial expression approach was used to detect teenager and university students’ facial movements while working on problem-solving and scientific-reasoning tasks. Results showed a set of seven facial movements (i.e., upper eyelid raising, eyebrow raising/lowering, lip tightening/pressing, lip corner pressing and mouth dimpling) have strong predictive power on task performance. Students frequently experienced negative emotions tended to have little prior knowledge. Students who experienced confusion and concentration were likely to engage in deep learning and thought the game content was meaningful.