CAREER: Improving Adaptive Decision Making in Interactive Learning Environments

互动学习环境（ILEs）对提高学生在STEM教育中的表现大有希望。虽然传统上，这样的系统侧重于教授学生学科知识，但同样重要的是教会他们如何成为更好的学习者。该项目的目标是制定一个综合研究和教育计划，调查如何改善ILE中的决策，以及将ILE决策与用户发起的决策相结合的影响。主要的研究目标是使用最先进的机器学习技术，直接从数据中创建和改进ILEs。主要的教育目标在于培养学生在他们不知道如何采取行动的新情况下独立行动并做出正确选择。这项工作将有助于通过提高学生在STEM领域的表现，教学生做出有效的教学决策，以及使这些系统做出的决策对教师和领域专家更加透明，从而使ILEs更加有效。该项目将开发和实证评估跨三个ILE或STEM领域的一般决策框架。该项目自然地将研究和教育目标结合起来。更具体地说，该项目将1)通过调整强化学习来做出类似于人类专家的分层决策，从而推进强化学习应用的研究；2)通过诱导紧凑的策略来提高对ILEs和强化学习算法的理解，这些策略突出了关键决策，并可以为一个人提供信息？对教育领域的理解；3)利用数据驱动的政策闭环支持学生决策，最终通过混合人机交互决策在体内实验提高学生长期解决问题的能力。

Interactive Learning Environments (ILEs) hold great promise for improving student performance in STEM education. While, traditionally, such systems have focused on teaching students subject matter, an equally important facet is to teach them how become better learners. The objective of this project is to develop an integrated research and education program that investigates the how to improve decision-making in ILEs and the impact of integrating ILE decisions with user-initiated decisions. The primary research goal lies in creating and improving ILEs directly from data, using state-of-the-art machine-learning techniques. The primary educational goal lies in preparing students to act independently and make good choices in new situations for which they do not immediately know how to act. The work will contribute in enabling ILEs to be more effective by improving student performance in STEM domains, by teaching students to make effective pedagogical decisions, and by making the decisions made by such systems more transparent to both teachers and domain experts.The project will develop and empirically evaluate a general decision-making framework across three ILE or STEM domains. The project naturally integrates both the research and education goals. More specifically, the project will 1) advance research on the application of Reinforcement Learning by adapting it to make hierarchical decisions similar to those of human experts; 2) advance the understanding of ILEs and Reinforcement Learning algorithms by inducing compact policies that highlight key decisions and that can inform one?s understanding of the educational domain; and 3) close the loop by using data-driven policies to support student decision-making and eventually improve their long-term problem-solving abilities through hybrid human-machine interactive decision making in vivo experimentation.