Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network-Reference-Cited by-同舟云学术

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

Published:2022-10-05 Issue:12 Volume:60 Page:3447-3460
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Wu Chennan,Liu Yang^ORCID,Guo Xiang,Zhu Tianshui,Bao Zongliang

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-022-02670-5.pdf

Reference67 articles.

1. Paas F, Renkl A, Sweller J (2004) Cognitive load theory: instructional implications of the interaction between information structures and cognitive architecture. Instr Sci 32:1–8

2. Sweller J (2010) Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ Psychol Rev 22:123–138. https://doi.org/10.1007/s10648-010-9128-5

3. Sweller J, van Merrienboer JJG, Paas FGWC (1998) Cognitive architecture and instructional design. Educ Psychol Rev 10:251–296. https://doi.org/10.1023/A:1022193728205

4. Paas F, Tuovinen JE, Tabbers H, Van Gerven PWM (2003) Cognitive load measurement as a means to advance cognitive load theory. Educ Psychol 38:63–71. https://doi.org/10.1207/S15326985EP3801_8

5. Baddeley A (1992) Working memory: the interface between memory and cognition. J Cogn Neurosci 4:281–288. https://doi.org/10.1162/jocn.1992.4.3.281

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