Predicting students’ attention in the classroom from Kinect facial and body features

Predicting students’ attention in the classroom from Kinect facial and body features 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png EURASIP Journal on Image and Video Processing Springer Journals

Predicting students’ attention in the classroom from Kinect facial and body features

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by The Author(s)
Subject
Engineering; Signal,Image and Speech Processing; Image Processing and Computer Vision; Biometrics; Pattern Recognition
eISSN
1687-5281
D.O.I.
10.1186/s13640-017-0228-8
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

EURASIP Journal on Image and Video ProcessingSpringer Journals

Published: Dec 1, 2017

References

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