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

This paper proposes DKT-STDRL, a deep learning model that combines CNNs and BiLSTMs to predict student learning performance by extracting spatial and temporal features from student exercise sequences in intelligent tutoring systems. The model is evaluated on educational datasets (ASSISTments, Statics2011) to predict whether students will answer questions correctly.

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' le

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