Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02578-z.pdf
Reference35 articles.
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3. Sümer, Ö., Goldberg, P., D’Mello, S., Gerjets, P., Trautwein, U., Kasneci, E.: Multimodal engagement analysis from facial videos in the classroom. IEEE Trans. Affect. Comput. (2021). https://doi.org/10.1109/TAFFC.2021.3127692
4. Sinatra, G.M., Heddy, B.C., Lombardi, D.: The challenges of defining and measuring student engagement in science. Taylor & Francis, UK (2015)
5. D’Mello, S., Dieterle, E., Duckworth, A.: Advanced, analytic, automated (AAA) measurement of engagement during learning. Educ. Psychol. 52(2), 104–123 (2017)
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