TL;DR
This study confirms that students have consistent learning rates across different educational contexts using a large-scale automated platform, supporting scalable personalized learning.
Contribution
It demonstrates that automated content generation and analysis can replicate learning rate regularity at scale without manual cognitive modeling.
Findings
Students show substantial variation in initial knowledge but consistent learning rates.
Automated system achieves mastery in comparable practice opportunities to expert-designed curricula.
Data and code are publicly available for further research.
Abstract
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 ( practice opportunities to reach 80%…
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