Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities.
Ran Liu, Kenneth R. Koedinger · Educational Data Mining · 2015
A growing body of research suggests that accounting for studentspecific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a parameter that individualizes for overall student ability but not for student learning rate. Here, we show that adding a per-student learning rate parameter to AFM overall does not improve predictive accuracy. In contrast, classifying students into three “learning rate” groups using residual error patterns, and adding a per-group learning rate parameter to AFM, substantially and consistently improves predictive accuracy across 8 datasets spanning the domains of Geometry, Algebra, English grammar, and Statistics. In a subset of datasets for which there are preand post-test data, we observe a systematic relationship between learning rate group and pre-topost-test gains. This suggests there is both predictive power and external validity in modeling these distinct learning rate groups.