Predicting Performance During Tutoring with Models of Recent Performance
  In educational technology and learning sciences, there are multiple uses for
a predictive model of whether a student will perform a task correctly or not.
For example, an intelligent tutoring system may use such a model to estimate
whether or not a student has mastered a skill. We analyze the significance of
data recency in making such predictions, i.e., asking whether relatively more
recent observations of a student's performance matter more than relatively
older observations. We develop a new Recent-Performance Factors Analysis model
that takes data recency into account. The new model significantly improves
predictive accuracy over both existing logistic-regression performance models
and over novel baseline models in evaluations on real-world and synthetic
datasets. As a secondary contribution, we demonstrate how the widely used
cross-validation with 0-1 loss is inferior to AIC and to cross-validation with
L1 prediction error loss as a measure of model performance.
