Abstract

This paper demonstrates how eye tracking technologies can understand providers to realize a personalized learning. Although curiosity is an important factor for learning, textbooks have been static and constant among various learners. The motivation of our work is to develop a digital textbook which displays contents dynamically based on students' interests. As interest is a positive predictor of learning, we hypothesize that students' learning and understanding will improve when they are presented information which is in line with their current cognitive state. As the first step, we investigate students' reading behaviors with an eye tracker, and propose attention and comprehension prediction approaches. These methods were evaluated on a dataset including eight participants' readings on a learning material in Physics. We classified participants' comprehension levels into three classes, novice, intermediate, and expert, indicating significant differences in reading behavior and solving tasks.

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Augmented Learning on Anticipating Textbooks with Eye Tracking | SpringerLink

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