Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students' learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students' learning history sequence, and then further extracts temporal features to extract deeper hidden information. Specifically, firstly, the DKT-STDRL model uses CNN to extract the spatial feature information of students' exercise sequences. Then, the spatial features are connected with the original students' exercise features as joint learning features. Then, the joint features are input into the BiLSTM part. Finally, the BiLSTM part extracts the temporal features from the joint learning features to obtain the prediction information of whether the students answer correctly at the next time step. Experiments on the public education datasets ASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTchall, and Statics2011 prove that DKT-STDRL can achieve better prediction effects than DKT and CKT.

翻译：知识追踪（KT）是智能教育系统的核心组成部分。当前多数知识追踪方法或依赖专家判断，或仅采用单一网络结构，这影响了学习特征的充分表达。为深入挖掘学生学习过程中的特征，本文提出了一种基于时空深度表示学习的深度知识追踪方法（DKT-STDRL）用于学习表现预测。DKT-STDRL首先从学生学习历史序列中提取空间特征，进而提取时间特征以挖掘更深层的隐藏信息。具体而言，DKT-STDRL模型首先利用CNN提取学生习题序列的空间特征信息；随后将空间特征与原始习题特征进行拼接，形成联合学习特征；接着将联合特征输入BiLSTM部分；最后BiLSTM部分从联合学习特征中提取时间特征，从而预测学生在下一时间步的作答正确性。在公开教育数据集ASSISTment2009、ASSISTment2015、Synthetic-5、ASSISTchall及Statics2011上的实验证明，DKT-STDRL能够取得优于DKT和CKT的预测效果。