Abstract
Due to dynamic changes in education filed, e-learning plays a crucial role in the success of students. Unlike in classroom sessions it is hard to estimate learning efficiency in e-learning. In order to assert this situation, we need a technology that can understand, analyze and calculate the efficiency of learners in the class. Emotion estimation using deep learning is one such technique that can determine the competence of the learner based on facial expression using cognitive state analysis. In a typical e-learning environment, we extract emotions of learner from facial images using deep learning technique to analyze cognitive state. In the present day approaches for cognitive state analysis, emotions are not considered to a significant extent. However, emotion estimation has significant role in cognitive state analysis. Image, audio and video sequences contribute in estimating the emotional quotient of the learner in the class. Moreover, studies show numerous solutions proposed facial expressions as source to estimate emotional quotient. The primary focus of this paper is to research multiple techniques used in existing methodologies for emotion estimation and attempt to compare across each other to unveil the merits and demerits. At the end, the focal point of this paper is to research and analyze the emotion estimation and make it available to the e-learning ecosystem.
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Mahendar, M., Malik, A., Batra, I. (2021). A Comparative Study of Deep Learning Techniques for Emotion Estimation Based on E-Learning Through Cognitive State Analysis. In: Luhach, A.K., Jat, D.S., Bin Ghazali, K.H., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2020. Communications in Computer and Information Science, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-3660-8_21
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