Ann Child Neurol.  2024 Apr;32(2):99-104. 10.26815/acn.2023.00360.

Reliability of a Tablet Computer-Based Dyslexia Screening Application Using an Eye-Tracking System

• Park S
• Song J
• Eom TH
• Kim YH

• KMID: 2554106
• DOI: http://doi.org/10.26815/acn.2023.00360

Abstract

Purpose
The early detection and management of dyslexia are crucial for preventing irreversible educational gaps and various negative consequences for affected students. However, diagnosing dyslexia is challenging because it requires a comprehensive assessment. Dyslexia screening tests that utilize fast, automated, computer-based technology can be useful for early identification and management. In this paper, we introduce a tablet computer-based dyslexia screening application that uses an eye-tracking system and verify its reliability.
Methods
The study included 200 participants between 8 and 13 years of age from an elementary school, all of whom underwent dyslexia screening tests twice. The screening was conducted using the VisualCamp SeeSo eye-tracking Android Software Development Kit v3.0.0, implemented on Samsung Galaxy Tab S5e tablets. The eye-tracking system measured reading speed by gaze, mean gaze fixation time, gaze fixation frequency, saccadic length, and regression ratio. To assess the reliability of the two sets of measurements, the intraclass correlation coefficient (ICC) was employed.
Results
Excellent reliability was found for measurements of gaze fixation frequency (ICC=0.83), gaze fixation mean time (ICC=0.82), and reading speed by gaze (ICC=0.76), and good reliability for measurements of regression ratio (ICC=0.75) and saccadic length (ICC=0.72).
Conclusion
This study demonstrated that the tablet computer-based dyslexia screening application reliably measured eye movements in subjects with dyslexia. Furthermore, the application proved to be highly reliable and potentially suitable for use in clinical or school settings, eliminating the need for a laboratory environment and extensive equipment.

Keyword

Dyslexia; Specific learning disorder; Eye-tracking technology; Software