arXiv：cs.LG（机器学习，全量分类）· Xianghui Meng, Yujing Zhang, Jionghao Lin·· 14 小时前AI 评分34
一项掌握阈值并不适用于所有知识追踪模型
One Mastery Threshold Does Not Fit All Knowledge Tracing Models
AI 导读
研究考察六种知识追踪（KT）模型在四个公开教育数据集上的表现，用12个从0.50到0.99的阈值评估进阶后表现、进阶覆盖率、练习负担及不同先前表现群体的差距。BKT对阈值变化相对不敏感，神经模型随阈值升高则明显更具选择性，部分原因是BKT估计潜在掌握概率而神经模型估计下一次作答正确的概率。
正文
Abstract:Tutoring systems use mastery thresholds to decide when students can stop practicing and advance, but the same numerical threshold can lead to very different decisions when the underlying knowledge tracing (KT) model changes. We examine six KT models across four public educational datasets and evaluate 12 thresholds from 0.50 to 0.99 using post-advancement performance, advancement coverage, practice burden, and disparities across prior-performance groups. We also identify thresholds that balance performance, extra practice, and advancement under 30 predefined instructional settings. Bayesian Knowledge Tracing (BKT) is relatively insensitive to threshold changes, while neural models become much more selective as thresholds increase. This partly reflects different model outputs: BKT estimates latent mastery probability, whereas neural models estimate the probability of a correct next response, so the same cutoff does not represent the same level of mastery. The best-balanced threshold varied substantially across models and settings. In half of the tested settings, neural models and BKT differed by more than 0.10 in their selected thresholds, although this gap became smaller when greater priority was placed on reducing extra practice and allowing more students to advance. Stricter thresholds also did not reliably reduce performance gaps and could disproportionately restrict advancement, with stronger-prior students advancing up to 3.26 times as often as weaker-prior students. These results show that mastery thresholds should be recalibrated when the KT model or instructional priorities change and evaluated by their effects on performance, practice, advancement, and access.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00095 [cs.LG] |
| (or arXiv:2610.00095v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00095 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xianghui Meng [view email]
[v1]
Tue, 8 Sep 2026 14:18:39 UTC (610 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org