自动化学习平台验证了学生学习速率的稳定性，支持大规模个性化教学。
Personalized AI Practice Replicates Learning Rate Regularity at Scale
- 基于180万条交互数据，自动生成知识点与练习题，免去人工建模。
- 学生初始水平差异大，但学习速率稳定（中位数7.22次达80%掌握）。
- 效果媲美专家设计课程，适合教育科技开发者参考。
近期研究发现学生在不同教育情境下学习速率保持一致。本研究利用来自数字平台Campus AI的180万条（经筛选后36.6万条）学生交互数据，进一步验证了这一规律。该平台可自动生成知识点（KCs）和对应练习题，均由人类专家验证。通过一对多映射，采用加性因子模型测量学习参数，无需复杂认知建模。使用混合效应逻辑回归分析，确认了先前核心发现：学生初始知识水平差异显著（四分位距[2.78, 12.18]次练习达80%掌握），但学习速率极为一致（四分位距[7.01, 8.25]次）。使用该全自动系统的学生，中位数仅需7.22次练习即达80%掌握，接近专家设计课程的6.54次。结果表明，基于科学原理的自动化内容生成可实现规模化有效个性化学习。数据与代码已公开。https://github.com/Campus-edu-AI/learning-rate
原文摘要 · Abstract (English)
Recent research demonstrated that students exhibit consistent learning rates across diverse educational contexts. We test these findings using a dataset of 1.8 million (366k post-filtering) student interactions from the digital platform Campus AI providing further evidence to the observation of regularity in learning rate among students. Unlike prior work requiring manual cognitive modeling, Campus AI automatically generates Knowledge Components (KCs) and corresponding exercises, both of which are validated by human experts. This one-to-many mapping facilitates the application of Additive Factors Models to measure learning parameters without complex cognitive modeling. Using mixed-effects logistic regression, we confirmed the core finding of prior work: students displayed substantial variation in initial knowledge ($\text{IQR} = [2.78, 12.18]$ practice opportunities to reach 80% mastery) but remarkably consistent learning rates ($\text{IQR} = [7.01, 8.25]$ opportunities). Furthermore, students using this fully automated system achieved 80% mastery in a median of 7.22 practice opportunities, comparable to the 6.54 reported for expert-designed curricula. These results suggest that automated, science-grounded content generation can support effective personalized learning at scale. Data and code are publicly available. https://github.com/Campus-edu-AI/learning-rate
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