Enhancing Knowledge Tracing via Adversarial Training
- URL: http://arxiv.org/abs/2108.04430v1
- Date: Tue, 10 Aug 2021 03:35:13 GMT
- Title: Enhancing Knowledge Tracing via Adversarial Training
- Authors: Xiaopeng Guo, Zhijie Huang, Jie Gao, Mingyu Shang, Maojing Shu, Jun
Sun
- Abstract summary: We study the problem of knowledge tracing (KT) where the goal is to trace the students' knowledge mastery over time.
Recent advances on KT have increasingly concentrated on exploring deep neural networks (DNNs) to improve the performance of KT.
We propose an efficient AT based KT method (ATKT) to enhance KT model's generalization and thus push the limit of KT.
- Score: 5.461665809706664
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study the problem of knowledge tracing (KT) where the goal is to trace the
students' knowledge mastery over time so as to make predictions on their future
performance. Owing to the good representation capacity of deep neural networks
(DNNs), recent advances on KT have increasingly concentrated on exploring DNNs
to improve the performance of KT. However, we empirically reveal that the DNNs
based KT models may run the risk of overfitting, especially on small datasets,
leading to limited generalization. In this paper, by leveraging the current
advances in adversarial training (AT), we propose an efficient AT based KT
method (ATKT) to enhance KT model's generalization and thus push the limit of
KT. Specifically, we first construct adversarial perturbations and add them on
the original interaction embeddings as adversarial examples. The original and
adversarial examples are further used to jointly train the KT model, forcing it
is not only to be robust to the adversarial examples, but also to enhance the
generalization over the original ones. To better implement AT, we then present
an efficient attentive-LSTM model as KT backbone, where the key is a proposed
knowledge hidden state attention module that adaptively aggregates information
from previous knowledge hidden states while simultaneously highlighting the
importance of current knowledge hidden state to make a more accurate
prediction. Extensive experiments on four public benchmark datasets demonstrate
that our ATKT achieves new state-of-the-art performance. Code is available at:
\color{blue} {\url{https://github.com/xiaopengguo/ATKT}}.
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