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

学习与智能系统：用于统计推理的下一代智能学习环境

• 批准号: 9720354
• 负责人: Marsha Lovett
• 金额: $ 69.84万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-01-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9720354&HistoricalAwards=false
• 关键词: Learning; Intelligent; Systems; Next; Generation

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.

9720354洛维特该项目由学习和智能系统(LIS)倡议资助，包括数学和物理科学局多学科活动办公室的支持。该项目将开发用于教学统计推理的创新、智能学习环境的三个核心组成部分。它的目的是直接促进学生将所学知识转移到原始学习环境之外的情景中去。这三个部分是(1)一个帮助学生发展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说明，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一般性，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。该项目将在几个方面取得根本性进展。首先，该界面提供了一种新的学习工具，将被卡内基梅隆大学的每一名人文和社会科学学生使用，并将传播到其他学院。其次，由于界面的设计是为了应用最近认知心理学研究揭示的原则，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发生的，何时发生的，以及为什么发生的。统计推理是这个项目的领域，因为(A)有效的迁移是关键--学生必须将他们学到的技能应用于广泛的问题和内容领域，并且(B)学生通常很难迁移这些技能。