Online learning, also known as e-learning, has become an increasingly popular and important component of higher education. Current literature indicates that higher education institutions rely on online learning to meet instructional loads and mitigate increasingly complex course scheduling problems. Students also find it more convenient to finish their college curricula without having to be physically present for traditional in-residence education. With the increasing popularity of distance learning, scholars are more and more concerned with the effectiveness of online technologies in delivering class material and learning outcomes. Specifically, current research seeks to measure the extent to which distance education students are engaged with online material. So far, research has measured student engagement predominantly through self-reporting instruments. However, the accuracy and comprehensiveness of these is debatable.
This study proposes to model student engagement in online environments using real-time biometric measures and using acuity, performance and motivation as dimensions of student engagement. Real-time biometrics are used to model acuity, performance and motivation include Electroencephalography (EEG) and eye-tracking measures. These biometrics have been measured in an experimental setting that simulates an online learning environment. The methodology uses a mixed model ANOVA to investigate whether biometric measures can be used to predict student engagement. Results suggest that eye-tracking and EEG measures can be used to predict acuity, performance and motivation, dimensions of student engagement.
Key words: Online Learning, E-learning, Electroencephalography (EEG), Eye-Tracking, Real-Time Online Engagement