A Token-Based Model for Structural Analysis and Quantification of Personal Learning Weight Patterns (TELOWAQ)

This paper proposes TELOWAQ, a token-based model that provides a structural framework for analyzing and quantifying personal learning weight patterns in human cognitive processes. Existing approaches to learning analysis often rely on qualitative indicators such as time spent, task completion counts, or surface-level behavioral observations, while failing to capture the underlying structural and weighted characteristics of individual cognition and decision-making. TELOWAQ addresses this gap by representing learning as a weighted interaction between discrete informational tokens—originating from text-based interaction with large language models—and internal cognitive states. This representation enables structural interpretation of learning dynamics without reducing cognition to outcome-based performance metrics. Rather than functioning as a predictive model or a closed algorithmic system, TELOWAQ is presented as an open analytical framework that supports comparative analysis, structural mapping, and cross-domain interpretation of learning behaviors. The framework emphasizes adaptability, interpretability, and extensibility, allowing it to be applied across educational, artificial intelligence, and human–machine interaction contexts. By reframing learning as a structurally quantifiable process without imposing normative optimization objectives, this work aims to provide a conceptual bridge between human cognition and machine-based learning systems, offering a foundation for further theoretical expansion and applied research.