Learning and Intelligent Systems: A Next-Generation Intelligent Learning Environment for Statistical Reasoning

该项目由学习和智能系统（LIS）计划资助，包括数学和物理科学理事会多学科活动办公室的支持。该项目将为统计推理教学开发创新、智能学习环境的三个核心组成部分。它旨在直接促进学生将所学知识转移到原始学习情境之外的能力。这三个组成部分是：(1)一个帮助学生形成一般理解的计算机界面，(2)有效应用统计推理所需知识的详细规范，以及(3)新的计算和统计技术，用于评估学生知识的准确性和普遍性，然后产生适当的补救措施。这个项目需要认知心理学家、统计学家和计算机科学家之间的独特合作。这个项目将在几个方面带来根本性的进步。首先，这个界面提供了一个新的学习工具，卡内基梅隆大学的每个人文和社会科学专业的学生都将使用它，并将传播到其他学院。其次，由于该界面的设计是为了应用最近认知心理学研究揭示的原则，因此它提供了对这些原则在实践中的有效性的测试。第三，制定统计推理所需知识的详细规范将产生新的见解，可以为统计教学和认知理论提供信息。第四，评估学生知识的技术开发了使用计算机化学习环境记录的信息的新方法。第五，在整个项目中收集的关于学生迁移的丰富数据将有助于更深入地了解迁移是如何、何时以及为什么发生的。统计推理是这个项目的领域，因为(a)有效的转移在这里是至关重要的——学生必须将他们所学的技能应用于广泛的问题和内容领域，(b)学生通常很难转移这些技能。

9720354 Lovett This project is being funded by the Learning and Intelligent Systems (LIS) Initiative, including support from the Office of Multidisciplinary Activities of the Directorate for Mathematics and Physical Sciences. This project will develop the three core components of an innovative, intelligent learning environment for teaching statistical reasoning. It is aimed at directly facilitating students' ability to transfer what they have learned to situations outside the original learning context. The three components are (1) a computer interface that helps students develop a general understanding, (2) a detailed specification of the knowledge required to apply statistical reasoning effectively, and (3) new computational and statistical techniques for assessing the accuracy and generality of students' knowledge and then generating appropriate remediation. This project entails a unique collaboration among cognitive psychologists, statisticians, and computer scientists. This project will lead to fundamental advances on several fronts. First, the interface provides a new learning tool that will be used by every humanities and social sciences student at Carnegie Mellon University and will be disseminated to other colleges. Second, because the interface is designed to apply the principles revealed by recent cognitive psychology research, it offers a test of these principles' effectiveness in practice. Third, developing a detailed specification of the knowledge required for statistical reasoning will yield new insights that can inform statistics instruction and cognitive theories. Fourth, the techniques for assessing students' knowledge develop new ways of using the information recorded by computerized learning environments. Fifth, the rich data collected on students' transfer throughout this project will lead to a deeper understanding of how, when, and why transfer occurs. Statistical reasoning is the domain for this project because (a) effective transfer is critical here--stude nts must apply the skills they have learned across a wide range of issues and content areas, and (b) students often have great difficulty transferring these skills.