Eye Movement Analysis of Digital Learning Content for Educational Innovation

Abstract

Eye movement technology is highly valued for evaluating and improving digital learning content. In this paper, an educational innovation study of eye movement behaviors on digital learning content is presented. We proposed three new eye movement metrics to explain eye movement behaviors. In the proposed method, the digital content, which were slide-deck-like works, were classified into page categories according to the characteristics of each page. We interpreted the subjects' eye movement behaviors on the digital slide decks. After data regularization and filtering, the results were analyzed to give directions for how to design an attractive digital learning content from the viewpoint of eye movement behaviors. The relationships between the subjects' evaluation scores, page categories, and eye movement metrics are discussed. The results demonstrated that the proposed fixation time percentage (FTP) was a representative, strong, and stable eye movement metric to measure the subjects' interest. Moreover, a reasonable portion of semantic content had a positive influence on the subjects' interest.