Alleviating 4 Million Cold Starts in Adaptive Fact Learning

Adaptive learning systems enable any learner to study at a level that is appropriately challenging to them. The cold start problem occurs whenever an adaptive system has not yet had the opportunity to adapt to its user or content. Using learning data from 140 thousand students, we evaluate several methods for alleviating the cold start problem in an adaptive fact learning system. We show that data-driven prediction of the learning system's adaptive parameter leads to more accurate estimates of learning at the start of a session, particularly when the prediction involves fact-specific difficulty information. The observed improvements are similar in magnitude to those in an earlier lab study, where using the predicted values as starting estimates in a learning session significantly increased posttest retention. We expect that comparable retention gains can be achieved in real-world educational practice.