A MULTIMODAL DEEP LEARNING FRAMEWORK FOR RECOGNIZING CLASSROOM EMOTIONS USING TEXT, VIDEO, AND GAN-AUGMENTED INFORMATION

Abstract

The development of learning results, the mainte- nance of interaction, and the provision of concentration inside the classroom setting are deeply connected with the understanding of, and the ability to identify the subtle emotional conditions of the learners. The traditional emotion recognition methods where in most cases the auditory or facial signals are used to detect an emotion are prone to missing the nuance of these forms of emotion as they are very complex.

To enhance the categorization of the emotional state of the students, this paper suggests a multimodal deep learning model that will combine sentiment analysis of the text inputs with the recognition of the facial expression of the video inputs. Long Short-term Memory (LSTM) networks are utilized to read the sentiment and contextuality that is imprinted in written responses, and the Convolutional Neural Networks (CNNs) are utilized to identify the significant spatial patterns on the face. Another application of Generative Adversarial Networks (GANs) is to produce synthetic samples of emotions not sufficiently rep- resented in available datasets to reduce the emotional imbalance that is common.

The manuscript poses significant questions, specifically: how to make sure that the data is diverse, how it is possible to have real-time processing functions, and how the ethical aspects of deploying such systems in a classroom are to be considered, and even survey existing data sets and explain their drawbacks.

The paper concludes with a series of recommendations to improve the process of multimodal integration, enhance datasets with the help of GAN-based synthesis, and introduce flexible frameworks that can be easily translated into realistic classroom solutions.