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CAREER: Improving Adaptive Decision Making in Interactive Learning Environments

职业：改善交互式学习环境中的自适应决策

基本信息

批准号：
1651909
负责人：
Min Chi
金额：
$ 54.78万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651909&HistoricalAwards=false
关键词：
CAREER Improving Adaptive Decision Making

项目摘要

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.

交互式学习环境 (ILE) 有望提高学生在 STEM 教育中的表现。虽然传统上，此类系统侧重于教授学生主题，但同样重要的一个方面是教他们如何成为更好的学习者。该项目的目标是开发一个综合研究和教育计划，研究如何改进 ILE 决策以及将 ILE 决策与用户发起的决策相结合的影响。主要研究目标在于使用最先进的机器学习技术直接从数据创建和改进 ILE。主要教育目标在于让学生做好独立行动的准备，并在他们不知道如何行动的新情况下做出正确的选择。这项工作将通过提高学生在 STEM 领域的表现、教导学生做出有效的教学决策以及使此类系统做出的决策对教师和领域专家更加透明，从而使 ILE 更加有效。该项目将开发并实证评估跨三个 ILE 或 STEM 领域的通用决策框架。该项目自然地整合了研究和教育目标。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）通过引入强调关键决策并可以帮助人们理解教育领域的紧凑政策，促进对 ILE 和强化学习算法的理解； 3）通过使用数据驱动的政策来支持学生决策形成闭环，并最终通过体内混合人机交互决策实验提高他们长期解决问题的能力。