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

• URL: http://arxiv.org/abs/2608.24005v1
• Date: Tue, 25 Aug 2026 02:50:58 GMT
• Title: Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing
• Abstract summary: We propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT)<n>Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph.<n>By jointly modeling these factors, LT-MKT enables more accurate prediction of students' future performance.
• Score: 47.61633089692492
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph [15.450478774839324]
  We propose DyMRL, a Dynamic Multispace Representation Learning approach to efficiently acquire and fuse multimodal temporal knowledge.<n>We show that DyMRL outperforms state-of-the-art dynamic unimodal and static multimodal baseline methods.
  arXiv  Detail & Related papers  (2026-03-25T09:45:16Z)
• Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process [16.472568558398482]
  The Knowledge Tracing task focuses on predicting a learner's future performance based on the historical interactions.<n>We propose a new method named Learning State Enhanced Knowledge Tracing (LSKT)<n> Experimental results on four real-world datasets show that our LSKT method outperforms the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-12-27T09:41:25Z)
• Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention [10.95112067894146]
  We propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains.<n>We also present a concept aggregation approach designed to reduce conceptual disparities within sequences of student interactions from diverse domains.<n>To fully leverage exercise information, we propose a new knowledge tracing model tailored for the domain generalization KT task, named Domain-General Relation-based Knowledge Tracing (DGRKT)
  arXiv  Detail & Related papers  (2024-07-02T13:13:44Z)
• Generative Multi-Modal Knowledge Retrieval with Large Language Models [75.70313858231833]
  We propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases. We demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
  arXiv  Detail & Related papers  (2024-01-16T08:44:29Z)
• Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations [50.81844184210381]
  We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE. This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
  arXiv  Detail & Related papers  (2023-11-16T07:09:38Z)
• Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation [68.13453771001522]
  We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings. We conduct extensive experiments and evaluate our model on large-scale real-world data.
  arXiv  Detail & Related papers  (2023-06-14T13:07:48Z)
• A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training [73.7507857547549]
  We propose to unify knowledge discovery and multi-modal pre-training in a continuous learning framework. For knowledge discovery, a pre-trained model is used to identify cross-modal links on a graph. For model pre-training, the knowledge graph is used as the external knowledge to guide the model updating.
  arXiv  Detail & Related papers  (2022-06-11T16:05:06Z)
• A Quantitative Perspective on Values of Domain Knowledge for Machine Learning [27.84415856657607]
  Domain knowledge in various forms has been playing a crucial role in improving the learning performance. We study the problem of quantifying the values of domain knowledge in terms of its contribution to the learning performance.
  arXiv  Detail & Related papers  (2020-11-17T06:12:23Z)
• Domain Adaption for Knowledge Tracing [65.86619804954283]
  We propose a novel adaptable framework, namely knowledge tracing (AKT) to address the DAKT problem. For the first aspect, we incorporate the educational characteristics (e.g., slip, guess, question texts) based on the deep knowledge tracing (DKT) to obtain a good performed knowledge tracing model. For the second aspect, we propose and adopt three domain adaptation processes. First, we pre-train an auto-encoder to select useful source instances for target model training.
  arXiv  Detail & Related papers  (2020-01-14T15:04:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.