Web-Based Learning Platform Architecture and Mathematical Modeling of Adaptive Learning Mechanism

Abstract

This paper presents a multi-layered architecture of a web-based learning platform and a mathematical model of an adaptive learning mechanism. The system is designed using a modular structure consisting of presentation, application, service, and data layers. The adaptive learning mechanism models the learner’s knowledge level as a latent variable and enables dynamic question selection. Item Response Theory (IRT) and Bayesian updating are applied to ensure accurate estimation of learner ability. The proposed system is evaluated through theoretical analysis and simulation experiments, demonstrating improved learning outcomes and system efficiency