Abstract
Recent trends in education have shifted from traditional classroom learning to an online learning setting; however, research has indicated a high drop out rate among e-learners. Boredom, lack of motivation are among the factors that led to this decline. This study develops a platform that provides feedback to learners in real-time while engaging in an online learning video. The platform detects, predicts and analyses the facial emotions of a learner using Convolutional Neural Network (CNN), and further maps the emotion to a learning affect. The feedback generated provides a reasonable understanding of the comprehension level of the learner.
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Zakka, B.E., Vadapalli, H. (2021). Detecting Learning Affect in E-Learning Platform Using Facial Emotion Expression. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_23
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DOI: https://doi.org/10.1007/978-3-030-49345-5_23
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