Human memory has been thoroughly studied and modeled in psychology, but mainly in laboratory setting under simplified conditions. For application in practical adaptive educational systems we need simple and robust models which can cope with aspects like varied prior knowledge or multiple-choice questions. We discuss and evaluate several models of this type. We show that using the extensive data sets collected by online educational systems it is possible to build well calibrated models and get interesting insight, which can be used for improvement of adaptive educational systems. [For complete proceedings, see ED560503.]