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The Learning Federation. Learning Science & Technology R&D To Catalyze a Revolution in Learning. Henry Kelly Federation of American Scientists Randy Hinrichs Microsoft Research Seattle, WA May 4, 2003. The Learning Federation.

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The Learning Federation

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    1. The Learning Federation Learning Science & Technology R&D To Catalyze a Revolution in Learning Henry Kelly Federation of American Scientists Randy Hinrichs Microsoft Research Seattle, WA May 4, 2003

    2. The Learning Federation • Goal: A sustained, well funded, creatively managed national research program in learning science and technology funded at a level commensurate with its importance to the nation’s future • Steering Committee: Industry, Universities, Government • Target Audience: Post-secondary, SMET (expandable to all ages and fields) • Key Themes: Pre-competitive applied research, spiral development strategies, tool building

    3. Steering Committee Ruzena Bajcsy, UC Berkeley John Bransford, Vanderbilt U Randy Hinrichs, Microsoft Research Ed Lazowska, U Washington Elliott Masie, Masie Center Richard Newton, UC Berkeley Don Norman, Nielsen Norman Group Raj Reddy, CMU Shankar Sastry, UC Berkeley Bill Spencer, Washington Advisory Group Janos Sztipanovits, DARPA, Vanderbilt U Andries van Dam, Brown U Gene Broderson, CPB Ann Wittbrodt, HP Administrative Management Henry Kelly, Federation of American Scientists Marianne Bakia, FAS Kay Howell, FAS Tom Kalil, UC Berkeley Learning Federation Leadership

    4. Technology can make learning: • More productive (quicker mastery, better retention, seamless transfer) • More compelling • More personal • More adaptable to local needs • More accessible • Continuous improvement (no data point left behind)

    5. The final centimeter Terabytes of Data Pointers to All of Human Knowledge Terabytes of Text Digital Libraries Animations simulations Photos, videos 3D objects Teachers, Advisors, counselors Billions of Web pages

    6. Learning Technology Research Cognitive Science Information Science

    7. Cognitive Theory Recommendations: • Acquiring expertise requires lots of practical experience • Experience and practice should continue only as long as it is challenging and reinforces expertise Based on: How People Learn: NAS 2001

    8. Assessment Theory Recommendations: • Focus on identifying the specific strategies used for problem-solving • Make students’ thinking visible both to their teachers and themselves and adjust instruction as appropriate • Provide timely and informative feedback Based on: Knowing what Students Know : NAS 2001

    9. Why a Research Roadmap? • Provide a clear definition of what’s possible • Support a management plan for executing the research • priorities for resource allocation • goals and metrics • manage spiral development • Build constituencies

    10. Archives FAQ Systems Engineering Approach Simulation & Interface tools Student Records Learner Integration Tools User Model Assessment tools Teachers Experts Counselors Q&A tools Pedagogy/Instructional Design Theory

    11. Research Areas: Component Roadmaps • Instructional Design for New Technology-Enabled Approaches to Learning • Learner Modeling and Assessment for Technology-Enabled Learning Systems • Question Generation and Answering Systems • Building Simulations and Virtual Environments • Integration Tools for Building and Maintaining Advanced Learning Systems

    12. 1. Instructional Design: Using Simulations and Games in Learning Understanding how people learn, how experts organize information, and the skills of effective learners • Developing multi-dimensional models of subject-matter mastery and expertise in different subject areas. • Understanding the influence of variables that may affect learning: motivation, prior experience, interest. • Building on emerging information about the biological basis of learning. • Understanding how best to use discovery-based learning, games, and other exploration-based learning? • What are the best roles for teachers, coaches, experts, and other humans supporting the learning process.

    13. 2. Learner Modeling and Assessment What to measure, when to measure and how to use the information • Embedded, multi-dimensional assessments of content mastery • Measuring individual and group skills. • Measuring levels of learner interest and motivation • Defining useful measures of learning styles and measuring how they may be revealed by student performance. • Ensuring security and privacy of assessment information.

    14. 3. Question Generation and Answering Systems How to take advantage of the benefits offered by emerging technologies to facilitate inquiry • Responding to learner inquiries through automated responses and dispatch of questions to instructors and experts. • Stimulating learners to ask questions and helping learners formulate answerable questions. • Diagnosing sources of misunderstanding and proposing new directions (with and without instructor assistance). • Using question sessions to build profiles of learner’s interests, capabilities and learning styles. • Natural language dialogues.

    15. 4. Building Simulations and Virtual Environments How to build complex virtual environments that accurately reflect current understanding of physics, chemistry, biology, and mathematics that permit exploration-based pedagogy • Semantic interoperability within and across disciplines. • Model scalability for use at many levels of resolution and complexity. • Certification & mgmt techniques for validating & updating simulations. • Techniques to navigate simulations and visualizations at different levels of granularity; feature-based navigation; and scene management. • Simulations of full range of instruments interoperable with synthetic environments. • User interfaces for virtual environments. • Noninvasive and accurate tracking to sense and react to the user and the user’s environment. • Encouraging communities of practice that may lead to standards

    16. 5. Integration Tools for Building and Maintaining Advanced Learning Systems Engineering strategies for using learning system tools to build learning systems • Course building tools for designing scenarios, creating assignments, designing response to information gathered from student observer tools, and programming avatar behaviors. • Tools to identify software resources relevant to the task, to combine these tools into a functioning system, and to adapt them to reflect specified objectives. • Tools to establish an open process for worldwide collaboration on building and maintaining learning environments.

    17. Roadmapping Work Plan • Develop and disseminate component roadmaps - OCT 2002 – AUG 2003 • Integrated roadmap workshop - JUL 2003 • Publish and disseminate integrated research roadmap - SEP 2003 • Launch Public Campaign about Needs & Opportunities - SEP 2003 • Constituent Outreach Continuing through DEC 2003

    18. The Challenges • Set priorities in interdisciplinary fields, many of which have no obvious home in traditional academic departments • Build connections between cognitive research and groups with the skills to implement these concepts • Balance basic research, applied research, development, and demonstration • Continuous testing and feedback (spiral development) • Bridge to commercialization/procurement • Appropriate model for research management

    19. Desired Management Features: • A roadmap identifying goals and priorities for achieving them that is regularly updated after consulting with experts in business, universities, and government • A strong team of program managers with a very small staff each assigned a major component of the roadmap • Flexibility in research management (e.g. “other transactions authority”) allowing fast response to new opportunities and an ability to draw on expertise wherever it may be found. • Freedom to establish significant research teams that can focus on a task for at least 3-5 years • The ability to establish a research center (analogous to the NIH campus) if, the Board is convinced that such a capability is needed

    20. Annual US Investment in Education and Training • Total Spending: $900 billion • Technology: $5.5 billion • Basic and applied RD&D to understand how this technology can be used to improve learning: $200 million (most in DoD) • Basic research on learning science and technology: $50 million

    21. Microsoft Hewlett Packard Hewlett Foundation Carnegie Corporation of New York Corporation for Public Broadcasting National Science Foundation DoD (DDR&E) Digital Promise Sponsorship