| Title | Convolutional Recurrent Neural Networks for Knowledge Tracing |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Wang, Wei, Liu, Tieyuan, Chang, Liang, Gu, Tianlong, Zhao, Xuemei |
| Conference Name | 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) |
| Date Published | oct |
| Keywords | Computational modeling, convolutional neural networks, Cyber-physical systems, Deep Learning, distributed computing, Knowledge discovery, Knowledge Tracing, Learning systems, Metrics, Neural Network Security, policy-based governance, pubcrawl, Recurrent neural networks, Resiliency, Student Modeling, Task Analysis |
| Abstract | Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian Knowledge Tracing methods. Most existing deep learning-based methods utilized either Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). However, it is worth noticing that these two sorts of models are complementary in modeling abilities. Thus, in this paper, we propose a novel knowledge tracing model by taking advantage of both two models via combining them into a single integrated model, named Convolutional Recurrent Knowledge Tracing (CRKT). Extensive experiments show that our model outperforms the state-of-the-art models in multiple KT datasets. |
| DOI | 10.1109/CyberC49757.2020.00054 |
| Citation Key | wang_convolutional_2020 |
Convolutional Recurrent Neural Networks for Knowledge Tracing