Machine learning-driven model construction for automated classification of cognitive styles

Accurate identification of cognitive styles is important for personalized learning environment optimization and human-computer interaction system design. Traditional self-report measures suffer from subjectivity bias, so this study developed a machine learning classification model based on objective physiological data. Focusing on the distinction between verbal and representational cognitive styles, the study collected eye-movement data from 85 participants in a standardized cognitive task via eye-tracking technology. We extracted multidimensional eye-movement features and systematically evaluated the classification performance of six machine learning algorithms: decision tree (DT), K-nearest neighbor algorithm (KNN), plain Bayes (NB), support vector machine (SVM), logistic regression (LR), and integrated learning model (EL). Experimental results show that all algorithms can effectively utilize eye movement features for cognitive style classification, with SVM performing optimally, after optimizing the parameters using the grid optimization method, achieving 82.1% classification accuracy (F1 = 0.715). The method proposed in this study provides a new way for non-invasive assessment of cognitive styles, which can be applied to real-time adaptive learning systems. The research results provide important insights into the development of personalization of educational technology, adaptive design of learning interfaces, and cognitive-perceptual computing systems, and provide valuable references for the fields of educational psychology and human-computer interaction research.