A Strategy for Enhancing English Learning Achievement, Based on the Eye-Tracking Technology with Self-Regulated Learning

Abstract

Owing to the global promotion of e-learning, combining recognition technology to facilitate learning has become a popular research topic. This study uses eye-tracking to analyze students’ actual learning situations by examining their attention during the learning process and to provide timely support to enhance their learning performance. Using cognitive technology, this study can analyze students’ real-time learning status, which can be utilized to provide timely learning reminders that help them achieve their self-defined learning goals and to effectively enhance their interest and performance. Accordingly, we designed a self-regulated learning (SRL) mechanism, based on eye-tracking technology, combined with online marking and note-taking functions. The mechanism can aid students in maintaining a better reading state, thereby enhancing their learning performance. This study explores students’ learning outcomes, motivation, self-efficacy, learning anxiety, and performance. The experimental results show that students who used the SRL mechanism exhibited a greater learning performance than those who did not use it. Similarly, SRL mechanisms could potentially improve students’ learning motivation and self-efficacy, as well as increase their learning attention. Moreover, SRL mechanisms reduce students’ perplexities and learning anxieties, thereby enhancing their reading-learning performance to achieve an educational sustainability by providing a better e-learning environment.