AlignKT通过对齐理想知识状态，提升学习者知识追踪的可解释性与教学支持能力。
AlignKT: Explicitly Modeling Knowledge State for Knowledge Tracing with Ideal State Alignment
- 设计前后端架构，显式建模稳定的知识状态
- 基于教育理论定义理想状态，在三个数据集上超越7个基线模型
- 适合关注可解释性与教学反馈的教育AI研究者
知识追踪（KT）是智能辅导系统（ITS）的核心组件，用于通过建模学习者的知识状态来监控和理解其进步。然而，现有许多KT模型主要关注学习者交互序列的拟合，往往忽略知识状态本身，导致可解释性差且难以提供有效教学支持。为此，我们提出AlignKT，采用前后端架构显式建模稳定的知识状态，并将初步知识状态与一个附加准则对齐。具体而言，我们基于教育理论定义理想知识状态作为对齐标准，为可解释性提供基础。通过五个编码器实现该框架，并引入对比学习模块增强对齐过程的鲁棒性。大量实验表明，AlignKT在三个真实世界数据集上表现优异，超越七个基线模型，其中在两个数据集上达到当前最优性能，在第三个数据集上表现具有竞争力。代码已开源：https://github.com/SCNU203/AlignKT。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) serves as a fundamental component of Intelligent Tutoring Systems (ITS), enabling these systems to monitor and understand learners' progress by modeling their knowledge state. However, many existing KT models primarily focus on fitting the sequences of learners' interactions, and often overlook the knowledge state itself. This limitation leads to reduced interpretability and insufficient instructional support from the ITS. To address this challenge, we propose AlignKT, which employs a frontend-to-backend architecture to explicitly model a stable knowledge state. In this approach, the preliminary knowledge state is aligned with an additional criterion. Specifically, we define an ideal knowledge state based on pedagogical theories as the alignment criterion, providing a foundation for interpretability. We utilize five encoders to implement this set-up, and incorporate a contrastive learning module to enhance the robustness of the alignment process. Through extensive experiments, AlignKT demonstrates superior performance, outperforming seven KT baselines on three real-world datasets. It achieves state-of-the-art results on two of these datasets and exhibits competitive performance on the third. The code of this work is available at https://github.com/SCNU203/AlignKT.
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。