A Low-Cost Scalable Solution for Monitoring Affective State of Students in E-learning Environment Using Mouse and Keystroke Data

摘要

This study proposed a user-independent intelligent system that reports the affective state of students in a non-intrusive and low-cost manner by utilizing mouse record and keystroke data collected in dynamic world. A scalable client-server architecture for student affective state monitoring in e-learning environment is also demonstrated.