A Machine Learning Framework for Academic Teaching and Learning based on Emotional Reactions

Education suffers from the most significant weakness, which is that teachers are unable to observe their students' learning and, as a consequence, are unable to determine the degree to which their pupils are concentrating on the activities they are being taught.The present model offers a solution to the aforementioned challenge.The courses can be made more difficult by utilising our algorithm's better concentration prediction, which allows us to offer more hard options.By contributing to the expansion of educational theory and practice, this work makes a contribution.The purpose of this paper is to extract facial expression characteristics by utilising a convolutional neural network and manual features from a multi-visual bag-of-words model and support vector machine (SVM) for emotion classification.This is accomplished through the utilisation of a multi-convolutional network-based facial expression identification approach.