Research paper Predicting students’ attention in the classroom from Kinect facial and body features was published in the EURASIP Journal on Image and Video Processing, vol. 80, 2017.
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.
Zaletelj and Košir, ” Predicting students’ attention in the classroom from Kinect facial and body features”, EURASIP Journal on Image and Video Processing (2017) 2017:80