Considerable attention has been given to methods for knowledge estimation, a category of methods for automatic assessment of a student's degree of skill mastery or knowledge at a specific time. Knowledge estimation is frequently used to make decisions about when a student has reached mastery and is ready to advance to new material, but there has been little work to forecast how far a student is from mastery or predict how much more practice the student will need before he or she will reach mastery. This article presents a method for predicting the point at which a student will reach skill mastery within an adaptive learning system, based on current approaches to estimating student knowledge. We apply this technique to two popular methods of modeling student learning -- Bayesian knowledge tracing and performance factors analysis -- and compare prediction correctness. Potential applications and future steps for improving the method are discussed.