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)是智能教育系统的一个主要部分。大多数当前的KT公司要么依靠专家判断,要么只是利用单一的网络结构,影响学习功能的完整表达。为了充分挖掘学生学习过程的特点,本文件提出了以空间和时空深层演示学习为学习表现预测(DKT-STDRL)为基础的深层次知识追踪(DKT-STDRL)的建议。DKT-STDRL从学生学习历史序列中提取空间特征,然后进一步提取时间特征以提取更深层的隐藏信息。具体地说,DKT-STDRL模型首先利用CNN来提取学生练习序列的空间特征信息。然后,空间特征与最初的学生练习特征作为联合学习特征连接起来。随后,联合功能成为BILSTM部分的投入。最后,BILSTM部分从联合学习特征中提取时间特征,以获取学生在下一个时间步骤是否正确回答的预测信息。对公共教育数据集2009、ASSIT2015、Synthistical-5、ASSIST-C和SDRADRADRADRADRADLKLADryADRADRADRADRAsADRADRADYADYDYDYDYTADYDYTADYTAST和SDST和SDST和SDKKKKKADYADYADYADYADYADYADVADVADVADVADVADVDVDYADYDYDKK)的预测的更好效果。