Abstract: Knowledge tracing （KT） is a key technology in the field of educational data mining. It uses students’ historical learning records to predict students’ next answer performance. Aiming at the problem that the deep knowledge tracking model based on time convolution network （TCN） only uses students’ answer sequences and answer results， and ignores other behavior characteristics of students， a deep knowledge tracking model based on multi feature extraction （TKT-PCA） is proposed. The model uses principal component analysis （PCA） method to automatically extract hidden features in a variety of students’ answer behavior and learn their representation. It not only reduces the feature dimension and redundant information， but also fully evaluates students’ knowledge mastery. The experimental results show that the TKT-PCA has the better prediction performance compared with other knowledge tracking baseline models.
Key words: deep learning； knowledge tracking； temporal convolution network； educational data mining； intelligent education

Key words: deep learning, knowledge tracking, temporal convolution network, educational data mining, intelligent education