Integrating Artificial Intelligence into Research on Emotions and Behaviors in Science Education
Author:
Publisher
Modestum Publishing Ltd
Subject
Applied Mathematics,Education
Link
https://www.ejmste.com/download/integrating-artificial-intelligence-into-research-on-emotions-and-behaviors-in-science-education-11927.pdf
Reference52 articles.
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2. Abd-El-Khalick, F., Boujaoude, S. Duschl, R., Lederman, N. G., Mamlok-Naaman, R., Hofstein, A., Niaz, M., Treagust, D., & Tuan, H-L. (2004). Inquiry in science education: International perspectives. Science Education, 88(3), 397-419. https://doi.org/10.1002/sce.10118
3. Adegun, I. P., & Vadapalli, H. B. (2020). Facial micro-expression recognition: A machine learning approach. Scientific African, 8, e00465. https://doi.org/10.1016/j.sciaf.2020.e00465
4. Artino, A. R., Holmboe, E. S., & Durning, S. J. (2012). Control-value theory: Using achievement emotions to improve understanding of motivation, learning, and performance in medical education: AMEE guide no. 64. Medical Teacher, 34(3), e148-e160. https://doi.org/10.3109/0142159X.2012.651515
5. Azari, B., Westlin, C., Satpute, A., Hutchinson, J. B., Kragel, P. A., Hoemann, K., Khan, Z., Wormwood, J. B., Quigley, K. S., Erdogmus, D., Dy, J., Brooks, D. H., & Barrett, L. F. (2020). Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience. Scientific Reports, 10, 20284. https://doi.org/10.1038/s41598-020-77117-8
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