Résumé:
Understanding learner engagement is a key principle in education. It helps adapt teaching methods and educational content to maximize the effectiveness and success of each
student. Various techniques have been proposed in the literature. In this work, we focus
on integrating the eye tracking model as a tool for extracting different ocular metrics
to detect learner engagement. The first objective of this work is to propose a structure
for presenting educational content. This structure aims to maximize the amount of relevant information captured by the model. The second objective is to detect moments
of learner engagement during the learning process. Machine learning algorithms, such as
Random Forest and XGBoost, have been applied to detect engagement in real-time. All
these proposals have been implemented in our E-track Learning system. This study opens
new perspectives for personalized teaching and continuous content adaptation, enabling
real-time feedback and more tailored learning experiences for students.
