.png){kind=icon}
返回
A Genetic Causal Explainer for Deep Knowledge Tracing
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2023.3286666.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based KT (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this article, we propose a causal explanation method based on the genetic algorithm (GA), named genetic causal explainer (GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multistrategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.
Keyword:
Predictive models
Neural networks
Analytical models
Deep learning
Correlation
Data models
Knowledge engineering
Cause-effect
explanation method
genetic algorithm (GA)
knowledge tracing (KT)
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
分享收藏Laser-Induced Breakdown Spectroscopy (LIBS) Analysis of Calcium Ions Dissolved in Water Using Filter Paper Substrates: An Ideal Internal Standard for Precision Improvement使用滤纸基底对溶解在水中的钙离子进行激光诱导击穿光谱 (LIBS) 分析: 提高精度的理想内标
PREAI
分享收藏
Intercellular Adhesion Molecule 1 (ICAM-1) Gene Variant is Associated with Coronary Artery Calcification Independent of Soluble ICAM-1 Levels细胞间粘附分子1 (ICAM-1) 基因变异与冠状动脉钙化相关，与可溶性ICAM-1水平无关
PREAI
分享收藏The response of rectangular piezoelectric sensors to Rayleigh and Lamb ultrasonic waves矩形压电传感器对瑞利和兰姆超声波的响应
OAAI
OAAI分享收藏
查看更多内容