Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
arXiv:2406.17518
Abstract
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.
8 pages, 3 figures, Educational Data Mining 2024, Human-Centric eXplainable AI in Education