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

Adaptive Learning Material Recommendation in Online Language Education