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ShanghaiTech University Knowledge Management System
| Uncertainty-aware Knowledge Tracing | |
| 2025-01-09 | |
| 状态 | 已发表 |
| 摘要 | Knowledge Tracing (KT) is crucial in education assessment, which focuses on depicting students' learning states and assessing students' mastery of subjects. With the rise of modern online learning platforms, particularly massive open online courses (MOOCs), an abundance of interaction data has greatly advanced the development of the KT technology. Previous research commonly adopts deterministic representation to capture students' knowledge states, which neglects the uncertainty during student interactions and thus fails to model the true knowledge state in learning process. In light of this, we propose an Uncertainty-Aware Knowledge Tracing model (UKT) which employs stochastic distribution embeddings to represent the uncertainty in student interactions, with a Wasserstein self-attention mechanism designed to capture the transition of state distribution in student learning behaviors. Additionally, we introduce the aleatory uncertainty-aware contrastive learning loss, which strengthens the model's robustness towards different types of uncertainties. Extensive experiments on six real-world datasets demonstrate that UKT not only significantly surpasses existing deep learning-based models in KT prediction, but also shows unique advantages in handling the uncertainty of student interactions. |
| 语种 | 英语 |
| DOI | arXiv:2501.05415 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:120431838 |
| WOS类目 | Computer Science, Artificial Intelligence |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/496895 |
| 专题 | 信息科学与技术学院_硕士生 |
| 通讯作者 | Ni, Yongxin |
| 作者单位 | 1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Ohio State Univ, Columbus, OH, USA 3.Univ Sci & Technol China, Hefei, Peoples R China 4.Univ Queensland, Brisbane, Australia |
| 推荐引用方式 GB/T 7714 | Cheng, Weihua,Du, Hanwen,Li, Chunxiao,et al. Uncertainty-aware Knowledge Tracing. 2025. |
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