Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students' global historical performance by progressively predicting student-level prior knowledge that is hidden in students' historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that \emph{AT-DKT} outperforms all sequential models with more than 0.9\% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of \emph{AT-DKT}.

翻译：知识追踪（KT）旨在根据学生与智能辅导系统的历史交互预测其未来表现。近期研究已应用多种深度神经网络解决知识追踪问题。然而，真实教育数据中存在两个重要因素尚未得到良好表征：其一，现有研究多通过问题与知识组件（KC）的共现矩阵增强输入表示，但未能将这些内在关联显式整合到最终响应预测任务中；其二，学生个体化的历史表现尚未被充分捕捉。本文提出AT-DKT模型，通过引入两个辅助学习任务——问题标签（QT）预测任务与个体化先验知识（IK）预测任务——改进原始深度知识追踪模型的预测性能。具体而言，QT任务通过预测问题是否包含特定知识组件来学习更优的问题表示；IK任务则通过渐进预测隐藏于学生历史学习交互中的个体先验知识，捕获学生的全局历史表现。我们在三个真实教育数据集上开展全面实验，并将所提方法与深度序列知识追踪模型及非序列模型进行对比。实验结果表明，AT-DKT在所有数据集上的AUC改进均超过0.9%，优于所有序列模型；在与非序列模型的比较中，其性能几乎达到次优水平。此外，我们通过消融实验和量化分析验证了辅助任务的有效性及AT-DKT的卓越预测效果。