DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction. (arXiv:2302.11569v1 [cs.LG])

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.