  Recommending personalized learning materials for online language learning is
challenging because we typically lack data about the student's ability and the
relative difficulty of learning materials. This makes it hard to recommend
appropriate content that matches the student's prior knowledge. In this paper,
we propose a refined hierarchical knowledge structure to model vocabulary
knowledge, which enables us to automatically organize the authentic and
up-to-date learning materials collected from the internet. Based on this
knowledge structure, we then introduce a hybrid approach to recommend learning
materials that adapts to a student's language level. We evaluate our work with
an online Japanese learning tool and the results suggest adding adaptivity into
material recommendation significantly increases student engagement.
