A novel method to monitoring changes in cognitive load in video-based learning

Abstract

One of the golden rules in instructional design methods is to optimize the use of working memory capacity and avoid cognitive overload. The study of cognitive load has historically relied on one's introspection. However, it is difficult to capture changes in cognitive load levels during learning sensitively. This paper suggests an approach to investigating dynamic changes in cognitive load by using a pupillometry. With the method, this study explores the effects of learners' prior knowledge and task complexity on cognitive load. An experiment was conducted on two groups of students (N = 19) with distinct levels of prior knowledge. In the experimental session, participants watched a video lecture on a mathematics proposition, while being eye-tracked. The lecture consists of sections, which can be either a high task complexity or a low task complexity based on elements they have. Pupil dilations acquired in each section were used to explore the time course of cognitive load. To formulate cognitive load patterns, a time-series clustering was used. The research conducted a chi-square analysis to test differences in cognitive load patterns by prior knowledge and task complexity. Results show that pupil dilation patterns can be applied to monitor changes in cognitive load during learning.