Intelligent behavior of adaptive educational systems is based#N#on student models. Most research in student modeling focuses on#N#student learning (acquisition of skills). We ocus on prior#N#knowledge, which gets much less attention in modeling and yet#N#can be highly varied and have important consequences for the#N#use of educational systems. We describe several models for#N#prior knowledge estimation – the Elo rating system, its#N#Bayesian extension, a hierarchical model, and a networked model#N#(multivariate Elo). We evaluate their performance on data from#N#application for learning geography, which is a typical case#N#with highly varied prior knowledge. The result show that the#N#basic Elo rating system provides good prediction accuracy. More#N#complex models do improve predictions, but only slightly and#N#their main purpose is in additional information about students#N#and a domain.
['Juraj Niznan', 'Radek Pelánek', 'Jirí Rihák']
Student Models for Prior Knowledge Estimation