Cargando…
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...
Autores principales: | Wu, Chennan, Liu, Yang, Guo, Xiang, Zhu, Tianshui, Bao, Zongliang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
|
Materias: | |
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 |
Ejemplares similares
-
Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals
por: Guo, Xiang, et al.
Publicado: (2022) -
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network
por: Zhai, Xiaolong, et al.
Publicado: (2017) -
Anticipated adaptation or scale recalibration?
por: Edelaar-Peeters, Yvette, et al.
Publicado: (2013) -
Adaptive Modular Convolutional Neural Network for Image Recognition
por: Wu, Wenbo, et al.
Publicado: (2022) -
Analysis and Recognition of Clinical Features of Diabetes Based on Convolutional Neural Network
por: Wang, Rui, et al.
Publicado: (2022)