A Formal Descriptive Language for Learning Dynamics: A Five-Layer Structural Coordinate System

TL;DR

This paper introduces a multi-layer formal descriptive language for learning dynamics, providing a structured, explicit framework to analyze complex learning processes without relying on specific predictive models.

Contribution

It presents a novel multi-layer symbolic framework that explicitly separates responsibilities and describes learning processes in a unified, extensible manner.

Findings

Defines a five-layer structural coordinate system for learning dynamics

Separates load generation, internal understanding, observation, and evaluation layers

Provides a common language for analyzing and extending learning theories

Abstract

Understanding learning as a dynamic process is challenging due to the interaction of multiple factors, including cognitive load, internal state change, and subjective evaluation. Existing approaches often address these elements in isolation, limiting the ability to describe learning phenomena within a unified and structurally explicit framework. This paper proposes a multi-layer formal descriptive framework for learning dynamics. Rather than offering a predictive or prescriptive model, the framework introduces a symbolic language composed of state variables, mappings, and layer-specific responsibilities, enabling consistent description of learning processes without commitment to specific functional forms or optimization objectives. This descriptive framework is intended to serve as a structural substrate for analyzing learning processes in human learners, and by extension, in adaptive and Al-assisted learning systems. A central design principle is the explicit separation of descriptive responsibilities across layers, distinguishing load generation, internal understanding transformation, observation, and evaluation. Within this structure, cognitive load is treated as a relational quantity arising from interactions between external input and internal organization, while subjective evaluation is modeled as a minimal regulatory interface responding to learning dynamics and environmental conditions. By emphasizing descriptive clarity and extensibility, the framework provides a common language for organizing existing theories and supporting future empirical and theoretical work.