  How can a learner systematically prepare for reading a book they are
interested in? In this paper,we explore how computational linguistic methods
such as distributional semantics, morphological clustering, and exercise
generation can be combined with graph-based learner models to answer this
question both conceptually and in practice. Based on the highly structured
learner model and concepts from network analysis, the learner is guided to
efficiently explore the targeted lexical space. They practice using multi-gap
learning activities generated from the book focused on words that are central
to the targeted lexical space. As such the approach offers a unique combination
of computational linguistic methods with concepts from network analysis and the
tutoring system domain to support learners in achieving their individual,
reading task-based learning goals.
