Choosing Sample Size for Knowledge Tracing Models.
Derrick Coetzee · 2014
An important question in the practical application of Bayesian knowledge tracing models is determining how much data is needed to infer parameters accurately. If training data is inadequate, even a perfect inference algorithm will produce parameters with poor predictive power. In this work, we describe an empirical study using synthetic data that pro-vides estimates of the accuracy of inferred parameters based on factors such as the number of students used to train the model, and the values of the underlying generating param-eters. We find that the standard deviation of the error is roughly proportional to 1/ n where n is the sample size, and that model parameters near 0 and 1 are easier to learn accurately.