Proceedings of the Annual Meeting of the Cognitive Science Society

Human learning has been characterized by three robust effects (i.e.power law of learning, power law of decay, and spacing), whichhave been validated across multiple domains and time intervals. Toaccount for these different effects mathematical model of learningand retention have been developed. These models hold a great dealof potential for application a wide range of educational and trainingscenarios. However, many models are not validated according fortheir ability to make accurate predictions of human performance.The predictive demand of these models is made increasinglycomplex by the needs of training domain, needing both to predictboth skill decay and reacquisition from little historical data. In thispaper, we examine the predictive capability of the PredictivePerformance Equation (PPE) implemented in a Bayesianhierarchical model. Through a comparison of two Bayesianhierarchical models we show how hierarchical model fit to aparticipant’s performance across a set of items compared to only asingle item improves PPE’s predictive accuracy of both skill decayand reacquisition over multiple learning schedules