用概率嵌入和逻辑推理提升知识追踪的可解释性。
Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
- 用贝塔分布的概率嵌入替代确定性向量表示学生知识状态。
- 通过显式逻辑运算构建可追溯的推理路径，准确率超越现有方法。
- 适合需要理解学生学习行为影响机制的教育研究者使用。
知识追踪（KT）模型基于学习交互预测学生表现，但主流深度学习方法依赖确定性向量嵌入和黑箱状态转移，难以解释具体历史行为如何影响预测。为此，我们提出概率逻辑知识追踪（PLKT），将预测建模为基于历史学习行为的目标条件证据推理过程。不同于传统确定性嵌入，PLKT采用鲁棒的贝塔分布概率嵌入表示学生知识状态，能够建模历史行为的不确定性，并执行显式逻辑操作（如合取），构建透明的推理路径，揭示特定过往互动对预测的贡献。大量实验表明，PLKT在保持高预测精度的同时显著提升可解释性。代码已公开于 https://anonymous.4open.science/r/PLKT-D3CE/。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) models students' knowledge states based on learning interactions to predict performance. While deep learning-based KT models have boosted predictive accuracy, most models rely on deterministic vector embeddings and opaque latent state transitions, limiting interpretability regarding how specific past behaviors influence predictions. To address this limitation, we propose Probabilistic Logical Knowledge Tracing (PLKT), an interpretable KT framework that formulates prediction as a goal-conditioned evidence reasoning process over historical learning behaviors. Instead of representing knowledge states as deterministic vector embeddings, PLKT employs robust Beta-distributed probabilistic embeddings to represent student knowledge states. This probabilistic foundation allows us to model the uncertainty of historical behaviors and perform explicit logical operations (e.g., conjunction), constructing transparent reasoning paths that reveal how specific past interactions contribute to the prediction. Extensive experiments show that PLKT outperforms state-of-the-art KT methods while achieving superior interpretability. Our code is available at https://anonymous.4open.science/r/PLKT-D3CE/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。