abstract
- © 2021 IEEE.Online education has gained relevance due to the flexibility of courses with students. Acceptance of online education has led to a growing demand for courses that were designed under a traditional format to be adapted to a fully online format. However, not all courses have had a correct transition to this new format due to different reasons such as course content, activities, lack of teacher training, etc. This problem leads to students to have a bad User Experience (UX) and therefore to have a low academic performance. In this paper, it is proposed the development of a system that allows to evaluate UX for courses with an online mode. The purpose of this system is to identify the scenarios where the student could be experiencing some discomfort or when it is more susceptible to a distraction during the class. It is used non-intrusive methods for the evaluation process to not generate a bias in the student. The method to be used will be the analysis of facial expressions by means of computer vision techniques. Using facial expression analysis, we can identify student emotions during class sessions. The obtained data from the system will be used to make correlations with the scenarios in which the student is in a negative mental state.