Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks

TL;DR

This paper presents a method for constructing causal knowledge networks using Bayesian networks and causal analysis, aiming to improve the transparency and effectiveness of adaptive learning systems.

Contribution

It introduces a novel approach combining Bayesian networks with causal analysis to build explainable knowledge structures and learning path recommendations.

Findings

Enhanced transparency in knowledge structures

Improved learning path recommendations

Strengthened trustworthiness of adaptive systems

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.