Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing

2026-08-24

AI Summary

本文针对多领域学习中认知负荷和知识迁移问题，提出了一种结合认知负荷与知识迁移的多领域知识追踪方法LT-MKT，以提高学生知识状态评估准确性。

Abstract

Knowledge Tracing (KT) aims to assess students' dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable success, real-world learning scenarios often involve multiple domains simultaneously, introducing two critical factors: 1) Cognitive load, arising from managing learning across domains in both temporal and knowledge dimensions. 2) Knowledge transfer, where knowledge states in one domain influence related states both within and across domains. In this paper, we focus on exploring these factors to improve students' knowledge state assessment in multi-domain learning scenarios and propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT). Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large language models (LLMs). Then, cross-domain features in both the temporal and knowledge dimensions are explicitly modeled to capture the effects of cognitive load. Additionally, a knowledge transfer module is designed to model the propagation of knowledge states within and across domains. By jointly modeling these factors, LT-MKT enables more accurate prediction of students' future performance. Finally, extensive experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance.

Problem

• Cognitive Load
• Knowledge Transfer
• Multi-domain Learning

Innovation

• Cognitive Load
• Knowledge Transfer
• Multi-domain Hierarchical Graph
• Large Language Models (LLMs)