Customizing an Affective Tutoring System Based on Facial Expression and Head Pose Estimation

TL;DR

This paper presents a personalized affective tutoring system that uses facial expression and head pose recognition to adapt to learners, resulting in fewer test attempts and higher satisfaction.

Contribution

It introduces a novel system integrating facial emotion and head pose analysis for personalized e-learning, enhancing learner performance and satisfaction.

Findings

Learners required fewer attempts to pass tests with the system.

Participants showed improved test scores and satisfaction.

The system effectively personalizes learning based on affective states.

Abstract

In recent years, the main problem in e-learning has shifted from analyzing content to personalization of learning environment by Intelligence Tutoring Systems (ITSs). Therefore, by designing personalized teaching models, learners are able to have a successful and satisfying experience in achieving their learning goals. Affective Tutoring Systems (ATSs) are some kinds of ITS that can recognize and respond to affective states of learner. In this study, we designed, implemented, and evaluated a system to personalize the learning environment based on the facial emotions recognition, head pose estimation, and cognitive style of learners. First, a unit called Intelligent Analyzer (AI) created which was responsible for recognizing facial expression and head angles of learners. Next, the ATS was built which mainly made of two units: ITS, IA. Results indicated that with the ATS, participants needed less efforts to pass the tests. In other words, we observed when the IA unit was activated, learners could pass the final tests in fewer attempts than those for whom the IA unit was deactivated. Additionally, they showed an improvement in terms of the mean passing score and academic satisfaction.