神经符号知识追踪：将教育知识注入深度学习以实现负责任的学习者建模
Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling
AI总结：
本文提出Responsible-DKT，通过整合教育符号知识提升学习者建模的性能与可解释性，实验表明其在预测准确率和时间可靠性方面优于传统方法。
摘要：
随着人工智能在教育中的广泛应用，特别是大型语言模型，智能辅导系统的需求日益增加。然而，LLMs在适应性和建模学习者知识演变方面表现有限，凸显了需要专门的学习者建模方法。尽管深度知识追踪方法在预测性能上表现强劲，但其不透明性和对偏见的敏感性限制了与教学原则的一致性。为此，我们提出Responsible-DKT，一种神经符号深度知识追踪方法，将符号教育知识（如掌握和非掌握规则）整合到顺序神经模型中，以实现负责任的学习者建模。实验表明，Responsible-DKT在真实世界的学生数学互动数据集上，优于神经符号基线和完全数据驱动的PyTorch DKT模型。该模型在仅使用10%训练数据时达到0.80以上的AUC，最高可达0.90 AUC，性能提升达13%。此外，该方法在时间可靠性方面表现优异，产生较低的早期和中期序列预测误差，并在所有序列长度中具有最低的预测不一致率，表明预测更新始终保持与观察到的学生响应方向一致。进一步地，神经符号方法通过基础计算图提供内在可解释性，揭示每个预测背后的逻辑，使本地和全局解释成为可能。它还允许对教学假设的实证评估，揭示重复错误响应（非掌握）强烈影响预测更新。这些结果表明，神经符号方法在性能和可解释性方面有所提升，缓解了数据限制，并支持更具责任性和以人类为中心的教育人工智能。
英文摘要：
The growing use of artificial intelligence (AI) in education, particularly large language models (LLMs), has increased interest in intelligent tutoring systems. However, LLMs often show limited adaptivity and struggle to model learners' evolving knowledge over time, highlighting the need for dedicated learner modelling approaches. Although deep knowledge tracing methods achieve strong predictive performance, their opacity and susceptibility to bias can limit alignment with pedagogical principles. To address this, we propose Responsible-DKT, a neural-symbolic deep knowledge tracing approach that integrates symbolic educational knowledge (e.g., mastery and non-mastery rules) into sequential neural models for responsible learner modelling. Experiments on a real-world dataset of students' math interactions show that Responsible-DKT outperforms both a neural-symbolic baseline and a fully data-driven PyTorch DKT model across training settings. The model achieves over 0.80 AUC with only 10% of training data and up to 0.90 AUC, improving performance by up to 13%. It also demonstrates improved temporal reliability, producing lower early- and mid-sequence prediction errors and the lowest prediction inconsistency rates across sequence lengths, indicating that prediction updates remain directionally aligned with observed student responses over time. Furthermore, the neural-symbolic approach offers intrinsic interpretability via a grounded computation graph that exposes the logic behind each prediction, enabling both local and global explanations. It also allows empirical evaluation of pedagogical assumptions, revealing that repeated incorrect responses (non-mastery) strongly influence prediction updates. These results indicate that neural-symbolic approaches enhance both performance and interpretability, mitigate data limitations, and support more responsible, human-centered AI in education.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
