Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network

The precise assessment of cognitive load during a learning phase is an important pathway to improving students’ learning efficiency and performance. Physiological measures make it possible to continuously monitor learners’ cognitive load in remote learning during the COVID-19 outbreak. However, main...

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Detalles Bibliográficos
Autores principales: Wu, Chennan, Liu, Yang, Guo, Xiang, Zhu, Tianshui, Bao, Zongliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532827/
https://www.ncbi.nlm.nih.gov/pubmed/36197639
http://dx.doi.org/10.1007/s11517-022-02670-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532827/
https://www.ncbi.nlm.nih.gov/pubmed/36197639
http://dx.doi.org/10.1007/s11517-022-02670-5

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