[论文解读] Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills

Jinseok Lee, Dit‐Yan Yeung|arXiv (Cornell University)|Mar 4, 2019

Intelligent Tutoring Systems and Adaptive Learning参考文献 21被引用 20

一句话总结

本文提出知识查询网络（KQN），一种新颖的知识追踪模型，通过将学生学习活动编码为知识状态和技能向量，并利用点积建模其交互，实现可解释的预测。KQN在四个公开数据集上实现了最先进水平的准确率，同时引入了概率性技能相似度，以实现直观的解释和技能领域的聚类。

ABSTRACT

Knowledge Tracing (KT) is to trace the knowledge of students as they solve a sequence of problems represented by their related skills. This involves abstract concepts of students' states of knowledge and the interactions between those states and skills. Therefore, a KT model is designed to predict whether students will give correct answers and to describe such abstract concepts. However, existing methods either give relatively low prediction accuracy or fail to explain those concepts intuitively. In this paper, we propose a new model called Knowledge Query Network (KQN) to solve these problems. KQN uses neural networks to encode student learning activities into knowledge state and skill vectors, and models the interactions between the two types of vectors with the dot product. Through this, we introduce a novel concept called probabilistic skill similarity that relates the pairwise cosine and Euclidean distances between skill vectors to the odds ratios of the corresponding skills, which makes KQN interpretable and intuitive. On four public datasets, we have carried out experiments to show the following: 1. KQN outperforms all the existing KT models based on prediction accuracy. 2. The interaction between the knowledge state and skills can be visualized for interpretation. 3. Based on probabilistic skill similarity, a skill domain can be analyzed with clustering using the distances between the skill vectors of KQN. 4. For different values of the vector space dimensionality, KQN consistently exhibits high prediction accuracy and a strong positive correlation between the distance matrices of the skill vectors.

研究动机与目标

• 在现有模型的基础上进一步提升知识追踪的预测准确率。
• 为学生学习过程中知识与技能的交互关系提供可解释且直观的说明。
• 通过学习到的向量表示实现技能关系的可视化与分析。
• 基于向量距离构建概率性技能相似度度量，以支持聚类与领域分析。

提出的方法

• KQN使用神经网络将学生学习序列嵌入到共享向量空间中的知识状态和技能向量中。
• 通过知识状态向量与技能向量之间的点积来建模知识-技能交互，以预测答题正确性。
• 引入概率性技能相似度，将技能向量之间的余弦距离与欧几里得距离关联到技能对的几率比上。
• 模型学习到的向量表示在不同维度下均保持距离矩阵与预测性能之间的强正相关性。
• 支持对知识-技能交互进行可视化，以增强可解释性，并基于学习到的向量距离实现技能的聚类。

实验结果

研究问题

• RQ1 知识追踪模型能否在预测准确率上超越现有方法？
• RQ2 知识状态与技能之间的交互能否被有意义地可视化和解释？
• RQ3 该模型的技能向量表示是否能基于相似性实现有意义的技能聚类？
• RQ4 在向量空间维度变化的情况下，该模型是否仍能保持高性能？

主要发现

• KQN在四个公开数据集上的预测准确率全面超越所有现有知识追踪模型。
• 知识状态与技能之间的交互可被可视化，从而直观地解释学生学习动态。
• 基于KQN的技能向量推导出的概率性技能相似度，能够有效实现基于语义与结构关系的技能领域聚类。
• KQN在不同向量空间维度设置下均保持高预测准确率，且技能向量距离矩阵与模型性能之间存在强正相关性。