It's Written on Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming

摘要

We built detectors capable of automatically recognizing affective states of novice computer programmers from student-annotated videos of their faces recorded during an introductory programming tutoring session. We used the Computer Expression Recognition Toolbox (CERT) to track facial features based on the Facial Action Coding System, and machine learning techniques to build classification models. Confusion/Uncertainty and Frustration were distinguished from all other affective states in a student-independent fashion at levels above chance (Cohen's kappa = .22 and .23, respectively), but detection accuracies for Boredom, Flow/Engagement, and Neutral were lower (kappas = .04, .11, and .07). We discuss the differences between detection of spontaneous versus fixed (polled) judgments as well as the features used in the models.