Abstract—With the continuous and rapid growth of online
courses, online learners’ engagement recognition has become a
novel research topic in the field of computer vision and pattern
recognition. While a few attempts to automatic engagement
recognition has been studied in the literature, learning a robust
engagement measure is still a challenging task. To address it, we
propose a new automatic engagement recognition method based
on Neural Turing Machine in this paper. In particular, we
firstly extract student’s eye gaze features, facial action unit
features, head pose features, and body pose features
respectively, then combine these multi modal features into the
final feature of our recognition task. Moreover, we propose the
engagement recognition framework based on the idea of Neural
Turing Machine to learn the weight of each short video feature.
In consequence, the feature fused by different weights will be
applied to identify the students’ engagement in learning online
courses. Empirically, we show improved performance over
state of the art methods to automatic engagement recognition
on DAiSEE dataset.
Index Terms—C3D network, engagement recognition, features fusion, neural turing machine, OpenFace.
The authors are with Capital Normal University, Beijing, China (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Xiaoyang Ma, Min Xu, Yao Dong, and Zhong Sun, "Automatic Student Engagement in Online Learning Environment Based on Neural Turing Machine," International Journal of Information and Education Technology vol. 11, no. 3, pp. 107-111, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).