Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2022

Abstract

With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the facial videos with a deep learning architecture consisting of Graph Attention Networks and Gated Recurrent Units. The ablation study confirmed that the differencing of consecutive frames of facial landmarks and the addition of head poses enhance the detection performance. The results further demonstrated that the model performed well in comparison with other models and more importantly, is suited for implementation on mobile devices with its low computational requirements.

Keywords

spatial, temporal, affective states, facial landmarks, graph attention network, gated recurrent unit

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 14th International Conference on Computer Supported Education, Virtual Conference, 2022 April 22-24

Volume

2

First Page

27

Last Page

34

ISBN

9789897585623

Identifier

10.5220/0010921200003182

Publisher

Science and Technology Publications

City or Country

Setúbal, Portugal

Additional URL

https://doi.org/10.5220/0010921200003182

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