Attention is key to effective learning, but mind wandering, a phenomenon in which attention shifts from task-related processing to task-unrelated thoughts, is pervasive across learning tasks. Therefore, intelligent learning environments should benefit from mechanisms to detect and respond to attentional lapses, such as mind wandering. As a step in this direction, we report the development and validation of the first student-independent facial feature-based mind wandering detector. We collected training data in a lab study where participants self-reported when they caught themselves mind wandering over the course of completing a 32.5 min narrative film comprehension task. We used computer vision techniques to extract facial features and bodily movements from videos. Using supervised learning methods, we were able to detect a mind wandering with an F1 score of.390, which reflected a 31% improvement over a chance model. We discuss how our mind wandering detector can be used to adapt the learning experience, particularly for online learning contexts.