论文标题
通过自适应课程和上下文匪徒提高学生完成率
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits
论文作者
论文摘要
我们提出了一种自适应学习智能辅导系统，该系统使用基于模型的强化学习以上下文匪徒的形式为学生分配学习活动。该模型经过数千名学生的轨迹培训，以最大程度地提高其运动完成率并继续在线学习，并自动适应新活动。与学生进行的随机对照试验表明，与其他方法相比，我们的模型可提高较高的完成率，并显着改善学生的参与度。我们的方法是完全自动解锁学习经验个性化的新机会。
We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajectories of thousands of students in order to maximize their exercise completion rates and continues to learn online, automatically adjusting itself to new activities. A randomized controlled trial with students shows that our model leads to superior completion rates and significantly improved student engagement when compared to other approaches. Our approach is fully-automated unlocking new opportunities for learning experience personalization.
