Providing adaptive support to the understanding of instructional material

We present an adaptive interface designed to provide tailored support for the understanding of written instructional material. The interface relies on a user model based on a Bayesian network, that assesses users' understanding as users read the instructional material and try to understand it by generating explanations to themselves. The user model's assessment is used by the interface to generate tailored scaffolding of further user's explanations that can improve the user's comprehension. After illustrating how the Bayesian user model assesses understanding from the user's explanations and from latency data on the user's attention, we discuss initial results on the effectiveness of the interface's adaptive interventions.