Improving Effectiveness of Learners' Review of Video Lectures by Using an Attention-Based Video Lecture Review Mechanism Based on Brainwave Signals

Abstract

Online video-based learning has been increasingly used in educational settings. When viewing video content, students may not always maintain a high degree of attention and may neglect or ignore important content critical for effective learning. This study thus presents an attention-based video lecture review mechanism (AVLRM) that can generate video segments for review based on students' sustained attention status, as determined using brainwave signal detection technology. A quasi-experiment nonequivalent control group design was utilized to divide 55 participants from two classes of an elementary school in New Taipei City, Taiwan, into two groups. One class was randomly assigned to the experimental group, and used video lectures with the AVLRM support for learning. The other class was assigned to the control group, and used video lectures with autonomous review for learning. Analytical results indicate that students in the experimental group exhibited significantly better review effectiveness than did the control group, and this difference was especially marked for students who had a low attention level, were field-dependent, or were female. The findings show that AVLRM based on brainwave signal detection technology can precisely identify video segments that are more useful for effective review than those picked by student themselves.