用反事实解释帮学生找到可操作的学习改进路径。
KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education
- 基于知识关联生成个性化学习建议。
- 在多个指标上提升5.7%至34%。
- 适合教育者与学生快速理解改进方向。
利用人工智能提升教学与学习的适应性与可扩展性。知识追踪（KT）因其优异性能和教育应用潜力，被广泛用于学生建模任务。本文将可解释人工智能（XAI）中的反事实解释引入KT，以实现教育场景下的可操作建议。反事实解释具备因果性、局部性且易于非专家理解。我们提出KTCF方法，通过建模知识概念间的关系生成反事实解释，并设计后处理方案将其转化为具体学习指令序列。在大规模教育数据集上的实验表明，KTCF在多项指标上优于现有方法，提升幅度达5.7%至34%。此外，定性评估显示，生成的学习指令能有效减轻学生的学习负担。结果表明，反事实解释有潜力推动人工智能在教育中负责任且实用的应用。未来工作应基于教育实际，发展以利益相关者为中心的XAI方法。
原文摘要 · Abstract (English)
Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose KTCF, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our KTCF method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help in reducing large study burden. We show that counterfactuals have the potential to advance the responsible and practical use of AI in education. Future works on XAI for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.
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