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

该项目旨在开发智能学习技术，旨在对个别学生的表现数据做出反应，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求，并为关于人类学习的高级研究提供了信息，因此具有巨大的潜力来改变美国的教育体系。该项目将为新一代数据驱动的智能教学系统(ITSS)创造技术，使快速创建支持科学、技术、工程和数学(STEM)学习的个性化教学成为可能。这项工作的最终结果将是教育数据挖掘方法的模块化框架，这些方法在多个粒度上提供适合学生的个性化支持，这些支持已经被实施、迭代地改进，并在整个系统中对学习影响和稳健性进行了经验验证。该项目将开发分层的、数据驱动的、可解释的和健壮的模型，以优化学生的学习。此外，它还将调查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做出更好的决策。教学生做出有效的决定可以从根本上改变教育评估：重点不应该仅仅是学生学到了什么，而是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教学，它有可能使广大受众，包括传统上在STEM领域代表性不足的学生，能够获得个性化学习支持。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经验评估一个通用的分层数据驱动框架，该框架将在包括逻辑、概率和编程在内的三个STEM领域诱导分层提示和适应性分层教学决策政策，在这三个领域建立传统的ITSS是极其具有挑战性的。更具体地说，这个项目将1)通过调整数据驱动的方法来促进对ITSS的研究，使其能够做出类似于人类专家的子目标提示和分层决策；2)通过将我们的通用分层数据驱动框架与扁平的数据驱动方法进行比较，评估其稳定性，不仅在每个ITS上，而且在整个ITSS上；以及3)通过使用数据驱动的策略来改善学生的决策和长期解决问题的能力来闭合循环。拟议的工作将产生重大影响，使信息技术支持系统更加有效，提高学生在STEM领域的表现，并教导学生做出有效的教学决策。如果成功，它将通过使用数据驱动的政策支持学生决策来闭合循环，并最终通过混合式人机交互决策体内实验来提高他们的长期问题解决能力。

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.