Abstract
Affective interaction in tutoring environments has been of great interest among several researchers in this community, which has spurred the development of various systems to capture learners’ emotional states. Young children are one of the biggest learner groups in digital learning environments, but these studies have rarely targeted them. Our current study leverages computer vision and deep learning to analyze young childrens’ learning-related affective states. We developed an effective recognition system to compute the probability for a child to present neutral or positive affective state. Our results showed that the prototype was able to achieve an average affective state prediction accuracy of 93.05%.
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Farzaneh, A.H., Kim, Y., Zhou, M., Qi, X. (2019). Developing a Deep Learning-Based Affect Recognition System for Young Children. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_14
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