Integrated Data-driven Technologies for Individualized Instruction in STEM Learning Environments

该项目旨在开发智能学习技术，旨在对个别学生的成绩数据做出反应，从而实现个性化教学。这种技术提供了一种低成本的方式，使学习环境适应个别学生的需要，并为人类学习的先进研究提供信息，从而具有改变美国教育体系的巨大潜力。该项目将为新一代数据驱动的智能辅导系统（ITSs）创造技术，使其能够快速创建个性化教学，支持科学、技术、工程和数学（STEM）的学习。这项工作的最终结果将是一个教育数据挖掘方法的模块化框架，该框架可在多个粒度上提供适合学生的个性化支持，该框架已被实施，迭代改进，并经过经验验证，以实现跨系统的学习影响和鲁棒性。该项目将开发分层数据驱动、可解释和健壮的模型，以优化学生的学习。此外，它将研究将分层数据驱动的代理决策与用户发起的决策相结合是否可以帮助学生学习为他们的学习做出更好的决策。教学生做出有效的决策可以从根本上改变教育评估：重点不应该只放在学生学到了什么，而应该放在学生是否能够在未来的情况下以富有成效的方式学习和适应。通过使用数据提供个性化指导，它有可能为广大受众提供个性化学习支持，包括传统上在STEM领域代表性不足的学生。这些努力通过加强国家发展和多样化STEM劳动力的能力来服务于国家利益。该项目的目标是开发和实证评估一个通用的分层数据驱动框架，该框架将在三个STEM领域（包括逻辑、概率和编程）中引出分层提示和自适应分层教学决策政策，在这些领域中，构建传统的信息技术系统极具挑战性。更具体地说，该项目将1)推进数据驱动方法的研究，使其适应于类似于人类专家的子目标提示和分层决策；2)通过将我们的一般分层数据驱动框架与扁平数据驱动方法进行比较，不仅在每个单独的ITS上，而且在整个ITS上，评估其鲁棒性；3)通过使用数据驱动的政策来实现闭环，提高学生的决策能力和长期解决问题的能力。拟议的工作将通过提高信息技术系统的效率，提高学生在STEM领域的表现，并教学生做出有效的教学决策，从而产生重大影响。如果成功，它将通过使用数据驱动的政策来支持学生决策，并最终通过混合人机交互决策在体内实验提高他们长期解决问题的能力。

This project aims to develop intelligent learning technology designed to react to individual student performance data, so as to personalize instruction. Such technology has significant potential to transform the American educational system by providing a low-cost way to adapt learning environments to individual students' needs and by informing advanced research on human learning. This project will create the technology for a new generation of data-driven Intelligent Tutoring Systems (ITSs), enabling the rapid creation of individualized instruction that supports learning in science, technology, engineering, and mathematics (STEM). The net result of this work will be a modular framework of educational data mining methods that offer student-adaptive, individualized support at multiple granularities, that have been implemented, iteratively refined, and empirically validated for learning impact and robustness across systems. This project will develop hierarchical data-driven, interpretable, and robust models that optimize student learning. Moreover, it will investigate whether integrating hierarchical data-driven agent decision-making with user-initiated decisions can help students learn to make better decisions for their learning. Teaching students to make effective decisions can fundamentally transform educational assessment: the emphasis should not be just on what students have learned, but on whether students can learn and adapt in productive ways in future situations. By providing individualized instruction using data, it has the potential to make individualized learning support accessible to a broad audience, including students that are traditionally underrepresented in STEM fields. These efforts serve the national interests by strengthening the nation's ability to develop and diversify the STEM workforce.The goal of this project is to develop and empirically evaluate a general hierarchical data-driven framework that would induce hierarchical hints and adaptive hierarchical pedagogical decision making policies across three STEM domains, including logic, probability, and programming, where building traditional ITSs is extremely challenging. More specifically, this project will 1) advance research on data-driven approaches to ITSs by adapting them to make subgoal hints and hierarchical decisions similar to those of human experts; 2) evaluate the robustness of our general hierarchical data-driven framework by comparing it to flat data-driven approaches not only on each individual ITS but also across ITSs; and 3) close the loop by using data-driven policies to improve students' decision-making and their long-term problem-solving abilities. The proposed work is poised to have a significant impact by making ITSs more effective, by improving student performance in STEM domains, and by teaching students to make effective pedagogical decisions. If successful, it will 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.