Improving Student Problem Solving in Narrative-Centered Learning Environments: a Modular Reinforcement Learning Framework
Improving Student Problem Solving in Narrative-Centered Learning Environments: a Modular Reinforcement Learning Framework
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提高学生在以叙事为中心的学习环境中解决问题的能力：模块化强化学习框架
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发表时间：
2015
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通讯作者：
James C. Lester
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作者：
Jonathan P. Rowe;James C. Lester
以叙事为中心的学习环境包括一类基于游戏的学习环境，这种学习环境将问题解决嵌入到互动故事中。以叙事为中心的学习带来的一个关键挑战是动态地定制故事事件以促进学生的学习。在这篇文章中，我们调查了在以叙事为中心的学习环境水晶岛中，数据驱动的辅导计划器对学生学习过程的影响。我们采用模块化强化学习，这是经典强化学习的一种多目标扩展，从而诱导出教程计划器。为了训练策划者，我们收集了453名中学生的语料库，这些学生在课堂上使用水晶岛。之后，我们对另外75名学生进行了后续实验，调查了诱导计划者的影响。研究表明，与对照条件相比，诱导计划者改善了学生的问题解决过程--包括假设检验和信息收集行为--这表明模块化强化学习是在以叙事为中心的学习环境中进行辅导计划的一种有效方法。
Narrative-centered learning environments comprise a class of game-based learning environments that embed problem solving in interactive stories. A key challenge posed by narrative-centered learning is dynamically tailoring story events to enhance student learning. In this paper, we investigate the impact of a data-driven tutorial planner on students’ learning processes in a narrative-centered learning environment, Crystal Island. We induce the tutorial planner by employing modular reinforcement learning, a multi-goal extension of classical reinforcement learning. To train the planner, we collected a corpus from 453 middle school students who used Crystal Island in their classrooms. Afterward, we investigated the induced planner’s impact in a follow-up experiment with another 75 students. The study revealed that the induced planner improved students’ problem-solving processes—including hypothesis testing and information gathering behaviors—compared to a control condition, suggesting that modular reinforcement learning is an effective approach for tutorial planning in narrative-centered learning environments.