An astonishing regularity in student learning rate.

Clicks: 98
ID: 280987
2023
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Ranked #271 of 292 articles by views in Proceedings of the National Academy of Sciences of the United States of America

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Abstract
Leveraging a scientific infrastructure for exploring how students learn, we have developed cognitive and statistical models of skill acquisition and used them to understand fundamental similarities and differences across learners. Our primary question was why do some students learn faster than others? Or, do they? We model data from student performance on groups of tasks that assess the same skill component and that provide follow-up instruction on student errors. Our models estimate, for both students and skills, initial correctness and learning rate, that is, the increase in correctness after each practice opportunity. We applied our models to 1.3 million observations across 27 datasets of student interactions with online practice systems in the context of elementary to college courses in math, science, and language. Despite the availability of up-front verbal instruction, like lectures and readings, students demonstrate modest initial prepractice performance, at about 65% accuracy. Despite being in the same course, students' initial performance varies substantially from about 55% correct for those in the lower half to 75% for those in the upper half. In contrast, and much to our surprise, we found students to be astonishingly similar in estimated learning rate, typically increasing by about 0.1 log odds or 2.5% in accuracy per opportunity. These findings pose a challenge for theories of learning to explain the odd combination of large variation in student initial performance and striking regularity in student learning rate.
Reference Key
koedinger2023anproceedings Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Koedinger, Kenneth R;Carvalho, Paulo F;Liu, Ran;McLaughlin, Elizabeth A;
Journal Proceedings of the National Academy of Sciences of the United States of America
Year 2023
DOI
e2221311120
URL
Keywords
learning curves deliberate practice learning rate logistic regression growth modeling; educational equity

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