Abstract:

As the technology for physiological data collection is advancing at a great speed, eye tracking data has become one of the valuable cues for learners’ mind. Eye movement, which reflects one’s conscious thought as well as unintended perception, is gaining attention from scholars and corporations as a significant indicator of learning experiences and achievements. Moreover, the advance of measuring pupillary response for cognitive load facilitated new approaches for investigating natures and fundamental of cognitive load mechanism during actual learning process. The purpose of this study, therefore, is to understand of the relationship between pupillary measured cognitive load of learners with diverse individual characteristics, such as prior knowledge, and achievement. To maximize the use of gaze data, this study specifically focuses on the video learning context. After basic survey, all participants were asked to watch two learning videos of proposition in mathematics, each playing about 12 minutes. Participants had to take post-test after watching videos for checking learning achievements. Pupillary response was collected with Tobii, the eye-tracking device during entire video learning. We adopt APCPS for calculating pupillary responses. Groups are divided by post-test score, High achievement groups(HAG) and Low achievement groups(LAG). We performed 2 types of t-tests one was independent t-test of APCPS between achievements groups and the other was paired t-test as APCPS within achievements groups. There was a significant difference of cognitive load only in the within HAG between learning 1, 2. There was no significant differences of cognitive load between achievement groups. Considering total cognitive load in learning process is combined with three types of cognitive load, extrinsic, intrinsic, and germane load, the result can be explained that even though there are no differences in total cognitive load, it is possible the interaction among three cognitive load types to affect the quality of cognitive load in learning process.

Keywords:

cognitive load, pupillary response, eye-movements, achievement, online learning, video-based learning.