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Main Authors: Wei, Yuang, Zhou, Yizhou, Jiang, Yuan-Hao, Jiang, Bo
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2406.17518
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author Wei, Yuang
Zhou, Yizhou
Jiang, Yuan-Hao
Jiang, Bo
author_facet Wei, Yuang
Zhou, Yizhou
Jiang, Yuan-Hao
Jiang, Bo
contents A reliable knowledge structure is a prerequisite for building effective adaptive learning systems and intelligent tutoring systems. Pursuing an explainable and trustworthy knowledge structure, we propose a method for constructing causal knowledge networks. This approach leverages Bayesian networks as a foundation and incorporates causal relationship analysis to derive a causal network. Additionally, we introduce a dependable knowledge-learning path recommendation technique built upon this framework, improving teaching and learning quality while maintaining transparency in the decision-making process.
format Preprint
id arxiv_https___arxiv_org_abs_2406_17518
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
Wei, Yuang
Zhou, Yizhou
Jiang, Yuan-Hao
Jiang, Bo
Artificial Intelligence
Social and Information Networks
A reliable knowledge structure is a prerequisite for building effective adaptive learning systems and intelligent tutoring systems. Pursuing an explainable and trustworthy knowledge structure, we propose a method for constructing causal knowledge networks. This approach leverages Bayesian networks as a foundation and incorporates causal relationship analysis to derive a causal network. Additionally, we introduce a dependable knowledge-learning path recommendation technique built upon this framework, improving teaching and learning quality while maintaining transparency in the decision-making process.
title Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
topic Artificial Intelligence
Social and Information Networks
url https://arxiv.org/abs/2406.17518