LAUSR

ABSTRACT Many studies have shown that learners’ sustained attention strongly affects e-learning performance, particularly during online synchronous instruction. This work thus develops a novel attention monitoring and alarm mechanism (AMAM) based on brainwave signals to improve learning performance via monitoring the attention state of individual learners and helping online instructors or teaching assistants to improve the sustained attention levels of learners with low-attention states as they perform online synchronous instruction activities. Totally, 83 and 65 Grade 7 students were randomly assigned to the experimental and control groups that respectively underwent online synchronous instruction with and without AMAM support. Analytical results reveal that the experimental group of learners exhibited significantly better learning performance and sustained attention than those in the control group, verifying that the AMAM efficiently promotes the learning performance and sustained attention of learners. Moreover, the proposed AMAM was more helpful in improving the learning performance of female learners than those of male learners and improved the sustained attention of both male and female learners. Furthermore, the sustained attention, frequency of attention alarms, and learning performance of the learners in the experimental group were strongly correlated, and the sustained attention and frequency of attention alarms strongly predicted learning performance.