Wearable EEG Sensor Analysis for Cognitive Profiling in Educational Contexts

Electroencephalography (EEG) provides a powerful means of capturing real-time neural activity, enabling the study of cognitive processes during complex learning tasks. This study explores the application of wearable EEG and advanced signal analysis to examine cognitive profiles of 30 sixth-grade students engaged in fraction learning. Using validated estimations alongside interactive digital tools such as Fraction Lab and the Diamond Paper task, EEG recordings were processed to evaluate spectral dynamics across delta, theta, alpha, and beta bands. Results revealed that lower-performing students exhibited elevated delta and theta power under cognitive load, whereas higher-performing students showed more stable beta activity linked to cognitive control. These findings highlight the utility of EEG-based signal analysis for identifying neurocognitive markers associated with conceptual and procedural knowledge (PK) in mathematics. The integration of such methodologies supports the development of precision-oriented educational strategies grounded in objective neural data. Clustering further revealed three learner profiles: Core Support Needed, Developing, and Advanced, while classification analyses confirmed that EEG features, especially gamma and beta oscillations, reliably distinguished among them, underscoring the potential of neurocognitive markers to guide adaptive instruction.