This work compares two approaches to provide metacognitive interventions and their impact on preparing students for future learning across Intelligent Tutoring Systems (ITSs). In two consecutive semesters, we conducted two classroom experiments: Exp. 1 used a classic artificial intelligence approach to classify students into different metacognitive groups and provide static interventions based on their classified groups. In Exp. 2, we leveraged Deep Reinforcement Learning (DRL) to provide adaptive interventions that consider the dynamic changes in the student's metacognitive levels. In both experiments, students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that adaptive DRL-based interventions closed the metacognitive skills gap between students. In contrast, static classifier-based interventions only benefited a subset of students who knew how to use BC in advance. Additionally, our DRL agent prepared the experimental students for future learning by significantly surpassing their control peers on both ITSs.

通过比较经典人工智能方法和深度强化学习方法在元认知干预方面的应用，本研究在两个学期内进行两项课堂实验证明，DRL适应性干预对缩小学生元认知技能差距具有显著作用，而静态分类器的干预只有在事先了解如何使用逆向链策略的学生中才有益处。此外，本研究所涉及的DRL代理在两个ITS上令实验学生显着超越其对照组同学。

利用深度加强学习在智能辅导系统中进行元认知干预