Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks

Author:

T. S. AshwinORCID,Guddeti Ram Mohana Reddy

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Education

Reference70 articles.

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2. Aiqin, Z., & Luo, Q. (2006). Study on e-learning system model based on affective computing. In International conference on information and automation, 2006. ICIA 2006 (pp. 245–249): IEEE.

3. Arifin, S., & Cheung, P.Y. (2007). A computation method for video segmentation utilizing the pleasure-arousal-dominance emotional information. In Proceedings of the 15th ACM international conference on multimedia (pp. 68–77): ACM.

4. Ashwin, T., Jose, J., Raghu, G., Reddy, G.R.M. (2015). An e-learning system with multifacial emotion recognition using supervised machine learning. In 2015 IEEE seventh international conference on technology for education (T4E), (pp. 23–26): IEEE.

5. Balaam, M., Fitzpatrick, G., Good, J., Luckin, R. (2010). Exploring affective technologies for the classroom with the subtle stone. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1623–1632): ACM.

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