IJIET 2021 Vol.11(3): 107-111 ISSN: 2010-3689
doi: 10.18178/ijiet.2021.11.3.1497

Automatic Student Engagement in Online Learning Environment Based on Neural Turing Machine

Xiaoyang Ma, Min Xu, Yao Dong, and Zhong Sun

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: 2181002054@cnu.edu.cn, xumin@cnu.edu.cn).


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).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), UGC care, EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net

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