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.
