Predicting students' performance using emotion detection from face-recording video when interacting with an ITS

This research aims to predict the academic performance of students when interacting with an Intelligent Tutoring System (ITS) from emotions detection and analysis. We use data from 47 university students in a virtual learning environment. We have used data gathered form face recording of students' interactions with the system to detect students' emotions and determine to what extent they can predict the final students’ performance during the learning session.