A Review of Data Augmentation and Data Generation Using Artificial Intelligence in Education
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8255-4_21
Reference23 articles.
1. Alam, A.: Possibilities and apprehensions in the landscape of artificial intelligence in education. In: 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), pp. 1–8. IEEE, Nagpur, India (2021)
2. Ali, S., DiPaola, D., Lee, I., Hong, J., Breazeal, C.: Exploring generative models with middle school students. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–13. ACM, Yokohama, Japan (2021)
3. Ali, S., DiPaola, D., Breazeal, C.: What are GANs?: introducing generative adversarial networks to middle school students. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15472–15479. AAAI, Online (2021)
4. Baruah, A.J., Baruah, S.: Data augmentation and deep neuro-fuzzy network for student performance prediction with MapReduce framework. Int. J. Autom. Comput. 18, 981–992 (2021)
5. Bian, C., Zhang, Y., Yang, F., Bi, W., Lu, W.: Spontaneous facial expression database for academic emotion inference in online learning. IET Comput. Vision 13(3), 329–337 (2019)
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