.png){kind=icon}
返回
Ensemble Knowledge Tracing: Modeling interactions in learning process
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.117680.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) aims to continuously estimate students' evolving knowledge state during their learning process, which has attracted much research attention due to its potential for delivering personalized and optimal experiences to students in intelligent learning systems. The learning process is essentially the pairwise interactions of Students, Concepts, and Questions (S-C, S-Q, C-Q for short). Modeling all these interactions will improve the performance of KT. However, existing KT methods hardly exploit all the interactions in a single model. Specifically, Bayesian Knowledge Tracing (BKT) and most of its variants neglect C-Q; Deep Knowledge Tracing (DKT) and other deep neural network approaches mostly neglect S-Q and C-Q. We propose the Ensemble Knowledge Tracing (EnKT), which models all three types of interactions. The base model of EnKT is a hybrid of BKT and DKT. We also present an ensemble algorithm Recurrent Boosting (RB), which extends AdaBoost to deal with KT sequential data. Inspired by BKT, EnKT represents S-C and S-Q using learning and performance parameters, respectively. Besides, EnKT defines C-Q as the correlation complexity among the concepts involved in a question. Experiments show EnKT significantly outperforms state-of-the-art methods (by up to 6% in AUC in some cases) on four real-world benchmark datasets and illustrate better interpretability by several typical case studies.
Keyword:
KnowledgeTracing
Deepneuralnetwork
Learninginteractions
Usermodeling
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Gastric cancer in patients with gastric atrophy and intestinal metaplasia: A systematic review and meta-analysis.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Trends for the Future of Education Programs for Professional Development专业发展教育计划的未来趋势SUSTAINABILITYOAAI
SUSTAINABILITYIF3.3
OAAI分享收藏SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social mediaSsel-ade: 一种用于从社交媒体中提取药物不良事件的半监督集成学习框架
PREAI
分享收藏Purpura Fulminans and Symmetrical Peripheral Gangrene Caused by Capnocytophaga canimorsus (Formerly DF-2) Septicemia—A Complication of Dog BiteMedicineOAAI
MedicineIF0
OAAI分享收藏
查看更多内容