Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition

2026-08-12

AI Summary

This study addresses the limitations of traditional questionnaires and behavioral logs in learning style identification—namely, their subjectivity or reliance on prolonged data collection—by introducing functional brain connectivity for cross-subject learning style recognition. Using EEG-derived phase-locking values (PLV) to construct inter-regional brain connectivity, the authors employ support vector machines with leave-one-subject-out cross-validation to classify learners along the active–reflective and verbal–visual dimensions of the Felder–Silverman model. Results reveal that fronto-occipital polarization features are discriminative for the verbal–visual dimension, achieving 70.00% subject-level accuracy, whereas performance on the active–reflective dimension remains modest at 55.56%, likely due to overlapping executive network engagement and systematic inter-individual differences in neural connectivity. Notably, the findings uncover a “systematic neural inversion” phenomenon, challenging the assumption of a universal classifier across subjects.

Abstract

Identifying individual learning styles optimizes pedagogical efficacy. While traditional questionnaires are structured, behavioral tracking methods require prolonged interaction log accumulation. To overcome these temporal constraints, this paper proposes an objective Electroencephalography (EEG) approach evaluating Phase Locking Value (PLV) connectivity against localized features across the Active-Reflective (AR) and Verbal-Visual (VV) Felder-Silverman dimensions. EEG signals were recorded from 28 participants during Raven's Advanced Progressive Matrices tasks. Support Vector Machine classification used Leave-One-Subject-Out Cross-Validation (LOSO-CV) alongside a 70:30 intra-subject split. The VV dimension achieved 70.00% subject-level accuracy driven by distinct fronto-occipital polarization. Conversely, the AR dimension yielded lower cross-subject generalizability (55.56%) due to overlapping executive networks and a "Systematic Neural Inversion" phenomenon, where stable individual connectivity signatures operated diametrically opposed to global boundaries (up to 20-0 voting margins). Ultimately, these outcomes demonstrate that rigid "one-size-fits-all" classifiers are bounded by biological diversity, emphasizing the need for future adaptive feature transformation techniques to bridge the cross-subject generalization gap.

Problem

• learning style recognition
• subject-independent classification
• cross-subject generalization
• EEG functional connectivity
• neural diversity

Innovation

• Phase Locking Value
• functional connectivity
• subject-independent classification
• learning style recognition
• systematic neural inversion