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Award ID contains: 1657176

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  1. ; ; ; ( , ArXiv.org)
    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. 
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  2. ; ; ; ( , Work in Progress, Learning at Scale 2018)
    Learning at scale (LAS) systems like Massive Open Online Classes (MOOCs) have hugely expanded access to high quality educational materials however, such materials are frequently time and resource expensive to create. In this work we propose a new approach for automatically and adaptively sequencing practice activities for a particular learner and explore its application for foreign language learning. We evaluate our system through simulation and are in the process of running an experiment. Our simulation results suggest that such an approach may be significantly better than an expert system when there is high variability in the rate of learning among the students and if mastering prerequisites before advancing is important. They also suggest it is likely to be no worse than an expert system if our generated curriculum approximately describes the necessary structure of learning in students. 
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