Computational Vision for Analyzing Student Emotions in E-Learning: A Preliminary Study

Adaptive teaching, mediated by technology, has become a reality and a great ally in distance teaching and learning. Artificial intelligence, through machine learning, has been an important tool for the development of innovative solutions for education. One of the approaches for adaptive teaching has been the student emotion analysis in distance learning to propose didactic solutions that best fit the profile of each student. In this sense, this work aims to present the preliminary studies carried out with SVM models and the AffectNet database in order to propose a solution for the analysis of emotions in the context of distance learning. As a result of this study, it is expected to propose an additional resource for the Homero platform (A Learning Management System) capable of adapting content based on information about the students' emotional states. In addition, this information can also be used to predict potential evasion risks. The development and preliminary tests point to a result with an average accuracy of 73% for emotion recognition based on video. With the tests, it was also possible to verify some necessary adjustments for the development of an efficient model for the recognition and analysis of emotions.