@inproceedings{cui-sachan-2023-adaptive,
    title = "Adaptive and Personalized Exercise Generation for Online Language Learning",
    author = "Cui, Peng  and
      Sachan, Mrinmaya",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.567",
    doi = "10.18653/v1/2023.acl-long.567",
    pages = "10184--10198",
    abstract = "Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language learning. To this end, we combine a knowledge tracing model that estimates each student{'}s evolving knowledge states from their learning history and a controlled text generation model that generates exercise sentences based on the student{'}s current estimated knowledge state and instructor requirements of desired properties (e.g., domain knowledge and difficulty). We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises. Then, we discuss the potential use of our model in educational applications using various simulations. These simulations show that our model can adapt to students{'} individual abilities and can facilitate their learning efficiency by personalizing learning sequences.",
}

Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language learning. To this end, we combine a knowledge tracing model that estimates each student{'}s evolving knowledge states from their learning history and a controlled text generation model that generates exercise sentences based on the student{'}s current estimated knowledge state and instructor requirements of desired properties (e.g., domain knowledge and difficulty). We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises. Then, we discuss the potential use of our model in educational applications using various simulations. These simulations show that our model can adapt to students{'} individual abilities and can facilitate their learning efficiency by personalizing learning sequences.
Cui, Peng Sachan, Mrinmaya
Adaptive and Personalized Exercise Generation for Online Language Learning