Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks
This work addresses the need for trustworthy and interpretable knowledge structures in adaptive learning and intelligent tutoring systems, representing an incremental advancement in educational technology.
The authors tackled the problem of constructing explainable knowledge structures for adaptive learning systems by proposing a method to build causal knowledge networks using Bayesian networks and causal relationship analysis, resulting in a dependable knowledge-learning path recommendation technique that improves teaching and learning quality with transparency.
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.