Keywords
Abstract
Adaptive context-aware blocking (ACAC) is a dynamic machine learning framework that optimizes information presentation by adjusting block size and structure based on real-time cognitive load indicators. Traditional static blocking methods fail to account for individual differences and contextual variability in cognitive processing. ACAC integrates multimodal biosensing (eye-tracking, skin conductance response) and environmental data, using a transformer fusion architecture to calculate a composite cognitive load index (CLI). This index drives a dual-path adaptive engine: a Gaussian process regressor predicts optimal block size, while graph neural networks (GNNs) organize information units into hierarchical clusters based on semantic relevance and load constraints. A feedback-driven optimization loop with meta-learning ensures continuous performance across environments. Experimental results show significant improvements in information retention and task performance compared to traditional strategies. ACAC’s modular design enables seamless integration with existing systems, with wide applicability in educational technology and human-computer interaction.
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