Classifying sequential–global cognitive styles is essential for developing adaptive and
personalized learning systems. Existing studies have relied on aggregated gaze statistics from proprietary
eye tracking software, limiting feature diversity and classification accuracy. To address this gap, this study
proposes a classification framework based on the Felder–Silverman Learning Style Model (FSLSM) that
leverages time series eye tracking data and deep learning. Features from x- and y-coordinate gaze data
were extracted using eight temporal window scales. The experimental results show that the proposed
framework accurately distinguishes sequential and global cognitive styles. Among the evaluated methods,
the Temporal Convolutional Network (TCN) combined with Robust scaling achieved the best classification
accuracy of 99.51%. The temporal window of approximately 2.0 seconds (i.e., 121 samples) yielded the
best performance. When combined with the feature set comprising gazeX, gazeY, speed, and direction, the
proposed method achieved the most optimal discriminative capability. Our findings underscore the potential
of time series eye tracking for identification of cognitive styles. This research serves as a crucial initial step
in improving biometric-driven approaches to personalized education and adaptive learning technologies
