Changes of affective states in intelligent tutoring system to improve feedbacks through low-cost and open electroencephalogram and facial expression

Abstract

Many works in the literature show that positive emotions improve learning. However, in the educational context, the affective dimension is often not adopted in the teaching-learning process. One of them is that there are many students for a teacher, making the practice of adapting the didactics and individualized feedbacks practically impossible. The low or sometimes no emotion analysis of those involved in learning also becomes a obstacle. One possibility to circumvent this problem is the use of Intelligent Tutoring Systems (ITS), to understand the student individually and adapt environments according to their use. It also adds the theories of emotions so that the ITS can understand the affective dimension of the student during activities. This paper aims to present a way to infer changes in a student’s affective states to improve feedbacks in ITS For this, facial expressions and brain waves (using a low-cost equipment called openBCI) were studied for acquisition and emotions. In the initial tests, the methodology has met what was expected, however, more studies with experiments must be carried out.