LAUSR

This paper proposes a novel approach to automatic estimation of attention of students during lectures in the classroom. The approach uses 2D and 3D data obtained by the Kinect One sensor to build a feature set characterizing both facial and body properties of a student, including gaze point and body posture. Machine learning algorithms are used to train classifiers which estimate time-varying attention levels of individual students. Human observers’ estimation of attention level is used as a reference. The comparison of attention prediction accuracy of seven classifiers is done on a data set comprising 18 subjects. Our best person-independent three-level attention classifier achieved moderate accuracy of 0.753, comparable to results of other studies in the field of student engagement. The results indicate that Kinect-based attention monitoring system is able to predict both students’ attention over time as well as average attention levels and could be applied as a tool for non-intrusive automated analytics of the learning process.