A Token-Based Model for Structural Analysis and Quantification of Personal Learning Weight Patterns (TELOWAQ)
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| Sprache: | Englisch |
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2026
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| _version_ | 1866902134310567936 |
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| author | Apophis |
| author_facet | Apophis |
| contents | <p>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.</p> <p><br>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.</p> <p><br>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.</p> <p><br>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.</p> <p><br>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18237804 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Token-Based Model for Structural Analysis and Quantification of Personal Learning Weight Patterns (TELOWAQ) Apophis token-based modeling learning weight cognitive modeling structural analysis quantification framework human learning AI-assisted learning interpretability explainable AI learning analytics human-AI interaction decision-making models conceptual framework analytical framework <p>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.</p> <p><br>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.</p> <p><br>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.</p> <p><br>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.</p> <p><br>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.</p> |
| title | A Token-Based Model for Structural Analysis and Quantification of Personal Learning Weight Patterns (TELOWAQ) |
| topic | token-based modeling learning weight cognitive modeling structural analysis quantification framework human learning AI-assisted learning interpretability explainable AI learning analytics human-AI interaction decision-making models conceptual framework analytical framework |
| url | https://doi.org/10.5281/zenodo.18237804 |