面向在线语言学习的自适应个性化练习生成
Adaptive and Personalized Exercise Generation for Online Language Learning
AI总结：
本文提出融合知识追踪与受控文本生成的语言学习练习生成框架，依据学生知识状态和教师要求生成个性化练习，并在 Duolingo 数据上验证其有效性。
摘要：
自适应学习旨在提供定制化的教育活动（例如练习），以满足个体学习需求。然而，人工构建和交付此类活动是一个费力的过程。因此，本文研究了一项面向在线语言学习的自适应个性化练习生成新任务。为此，我们将一个知识追踪模型与一个受控文本生成模型相结合：前者根据学生的学习历史估计其不断变化的知识状态，后者则依据学生当前估计的知识状态以及教师对所需属性（如领域知识和难度）的要求生成练习句子。我们在来自 Duolingo 的真实学习者交互数据上训练并评估了模型，证明由学生状态引导的语言模型（LMs）能够生成更优质的练习。随后，我们通过多种模拟讨论了模型在教育应用中的潜在用途。这些模拟表明，我们的模型能够适应学生的个体能力，并可通过个性化学习序列提升其学习效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
