Adaptive and Personalized Exercise Generation for Online Language Learning
This addresses the labor-intensive process of customizing educational activities for individual learners in online language learning, representing an incremental improvement by integrating existing techniques.
The paper tackles the problem of manually creating adaptive exercises for online language learning by proposing a model that combines knowledge tracing and controlled text generation to automatically generate personalized exercises based on student knowledge states and instructor requirements, demonstrating superior performance on Duolingo data and showing potential to improve learning efficiency through simulations.
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.