An Electroencephalography (EEG) Study of Cognitive Load and Neural Efficiency During Higher-Order Thinking in Secondary Science Learning

Saleh, Salmiza; Fazil, Fazrin; Noh, Nor Azila; Liu, Yufei · 2026 · Crossref

DOI: 10.21315/apjee2025.40.3.21

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Summary

This study investigates the neural correlates of higher-order thinking skills (HOTS) engagement in secondary science education, addressing the gap between curriculum reforms emphasizing HOTS and students’ persistent underperformance in Malaysia. Traditional assessments fail to capture real-time cognitive processes, prompting the use of electroencephalography (EEG) to examine cognitive load and neural efficiency. The research specifically analyzes how students of varying ability levels process complex scientific reasoning during learning and assessment tasks, aiming to identify EEG-based markers for effective engagement. The study employed a within-subject experimental design with 45 Form Two students, stratified into high-, moderate-, and low-ability groups based on prior science performance. Participants completed two 15-minute tasks: a scaffolded HOTS learning task and a structured HOTS assessment task. EEG data were recorded using a 32-channel system following the international 10–20 system, focusing on theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) frequency bands. Data underwent rigorous preprocessing, including band-pass filtering and artifact removal, followed by repeated-measures ANOVA and Pearson correlation analyses to evaluate differences in neural activity and its relationship to performance. Results revealed distinct neural patterns across ability groups. High-ability students exhibited elevated alpha and beta power with low theta activity during learning tasks, indicating high neural efficiency and sustained engagement. In contrast, low-ability students showed significantly increased theta power and reduced alpha/beta activity, signaling cognitive overload and inefficient processing. Assessment tasks elicited stronger beta responses across all groups compared to learning tasks, reflecting higher cognitive demands. Correlation analyses confirmed that alpha power was the strongest positive predictor of HOTS performance (r = 0.62), while theta power was negatively correlated (r = –0.55), serving as a marker for working memory overload. The findings provide empirical support for using EEG to diagnose cognitive load and efficiency in educational settings. The study concludes that high-ability learners benefit from open-ended inquiry, while low-ability students require scaffolded instruction to manage cognitive strain. These insights advocate for neuroscience-informed pedagogical strategies that align instructional complexity with students’ neural processing capacities, thereby reducing overload and promoting deeper engagement with HOTS content in science education.

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• neuro workload indices