Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing
cs.AI
Submitted: 2026-08-25
Updated: 2026-08-25
Comments: Accepted as a CIKM 2026 Oral
Code: https://github.com/sosweetzhang/LT-MKT
License: http://creativecommons.org/licenses/by/4.0/
The gist: Knowledge Tracing (KT) aims to assess students' dynamic knowledge states from their learning histories.
Terminology
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.
Sources
- GPT-4 Technical Report
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction
- Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation
- PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- A Self-Attentive model for Knowledge Tracing
- HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
- Large Language Models for Education: A Survey and Outlook
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