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

arXiv: https://arxiv.org/abs/2302.11569 · HTML: https://mlsanity.com/paper/2302.11569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Knowledge Tracing with Sequential Key-Value Memory Networks](https://mlsanity.com/paper/1910.13197/) (2019)
- [Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks](https://mlsanity.com/paper/2207.03648/) (2022)
- [GritNet: Student Performance Prediction with Deep Learning](https://mlsanity.com/paper/1804.07405/) (2018)
- [Toward modern educational IT-ecosystems: from learning management systems to digital platforms](https://mlsanity.com/paper/1806.11154/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining](https://mlsanity.com/paper/2309.04761/) (2023)

## Abstract (arXiv)

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.