BIGDATA: F: BCC: Data driven optimization of classroom learning activities

BIGDATA：F：BCC：数据驱动的课堂学习活动优化

• 批准号: 1546510
• 负责人: Zoran Popovic
• 金额: $ 150万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546510&HistoricalAwards=false
• 关键词: BIGDATA; BCC; Data; driven; optimization

One of the most exciting potential benefits of big data is that it will enable better decision making in applications such as teaching software, patient treatment plans, and user personalization. In many complex domains involving people, the number of descriptions of a situation and the number of potential decisions that can be made to improve upon the previous situation is enormous. The sheer size of this problem implies that careful selections of additional data must be made to improve decision making. In health, these decisions could involve a course of treatment; in learning these could involve remediations such as additional practice, in personalization it could involve improved choices for consumers. This proposal will develop new algorithms that are especially effective in coming to close to optimal decisions quickly.This project will aim to develop novel algorithms that specifically address these needs, and thus enable data intensive systems to be efficiently used towards solving the decision making process. Since novel algorithmic methods of this kind are best developed, refined and tested on a real domain, the project will focus on the educational domain of classroom learning activities that optimize learning outcomes. However, the algorithms will be applicable to numerous other decision making domains. Specifically the project will focus on finding algorithmic solutions for:(1) Large-scale deployment and data collection of lesson enactments in thousands of classrooms worldwide.(2) Data-driven methods to efficiently determine the likely most important parameters of the classroom decision making process.(3) The development of novel reinforcement learning algorithms with a focus on efficient use of data to rapidly converge towards beneficial policies.(4) Using data to determine the key model and decision parameter space bottlenecks, which, if removed, could significantlyimprove the outcomes of the Reinforcement Learning process. The efficacy of the optimized policy will be determined by the improvement of classroom learning performance at scale.The methods that will be developed by this project are general enough to be directly applicable to domains of patient treatment, or any other domain that involves unknown model dynamics and decision space that involves people. The Reinforcement Learning methods will similarly find practical use in situations where a decision policy is deployed in a large number of instances asynchronously and in other high-risk settings where reducing over-exploration as much as possible is of high importance.

大数据最令人兴奋的潜在好处之一是，它将在教学软件、患者治疗计划和用户个性化等应用程序中实现更好的决策。在许多涉及人的复杂领域中，对一种情况的描述数量和可以做出的改善先前情况的潜在决策数量是巨大的。这个问题的规模意味着必须仔细选择额外的数据，以改善决策。在健康方面，这些决定可能涉及一个疗程;在学习方面，这些决定可能涉及补救措施，如额外的实践，在个性化方面，它可能涉及改善消费者的选择。该项目将开发新的算法，特别是在接近最佳决策的快速有效。该项目的目标是开发新的算法，专门解决这些需求，从而使数据密集型系统能够有效地用于解决决策过程。由于这种新颖的算法方法是在真实的领域中最好地开发、改进和测试的，因此该项目将侧重于优化学习成果的课堂学习活动的教育领域。然而，该算法将适用于许多其他决策领域。具体而言，该项目将专注于寻找算法解决方案：（1）在全球数千个教室中大规模部署和收集课程制定的数据。(2)数据驱动的方法，以有效地确定课堂决策过程中可能最重要的参数。(3)开发新的强化学习算法，重点是有效利用数据，快速收敛到有益的策略。(4)使用数据来确定关键模型和决策参数空间瓶颈，如果消除这些瓶颈，可以显着改善强化学习过程的结果。优化策略的有效性将取决于课堂学习绩效的大规模改善。该项目将开发的方法足够通用，可以直接应用于患者治疗领域，或涉及未知模型动态和涉及人的决策空间的任何其他领域。 强化学习方法同样适用于在大量实例中异步部署决策策略的情况，以及在其他高风险环境中尽可能减少过度探索非常重要的情况。