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Knowledge tracing (KT) serves as a primary part of intelligent educationsystems. Most current KTs either rely on expert judgments or only exploit asingle network structure, which affects the full expression of learningfeatures. To adequately mine features of students' learning process, DeepKnowledge Tracing Based on Spatial and Temporal Deep Representation Learningfor 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 hiddeninformation. Specifically, firstly, the DKT-STDRL model uses CNN to extract thespatial feature information of students' exercise sequences. Then, the spatialfeatures are connected with the original students' exercise features as jointlearning features. Then, the joint features are input into the BiLSTM part.Finally, the BiLSTM part extracts the temporal features from the joint learningfeatures to obtain the prediction information of whether the students answercorrectly at the next time step. Experiments on the public education datasetsASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTchall, and Statics2011 provethat DKT-STDRL can achieve better prediction effects than DKT and CKT.

DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction