Analysis of Students’ Emotions in an Online Learning Environment

The COVID 19 pandemic has affected several sectors of human life, including the educational system. It has led to a rapid and forced shift towards online learning. This radical change has influenced the students’ behavior, emotional state as well as their ability to learn. In order to analyze this situation, we focus in this work on the automatic detection of students’ emotions while exploiting the techniques and methods of sentiment analysis and machine learning. The proposed solution aims to predict students’ emotions and some of the aspects related to online learning from students’ reviews and then infers the attitude of students using association rules and clustering. The data-set consists of students’ answers in a forum sent at the end of sessions and semesters, annotated manually, during online learning. The obtained results using precision and recall was satisfying and favorable.