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WebLenses Bringing Data into Focus

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  1. WebLensesBringing Data into Focus Haggai Mark Learning, Design & Technology Stanford University 2009

  2. What’s the problem?(Why WebLenses) • When reading web content • Terminology, assumptions, background unfamiliar, unclear • Data presentation hard to digest • Static content, or dynamic but “canned” • Availability of learning/support resources • External to the content (“task switching”) • Not content/context-sensitive • Long-term learning/support • Up to you (memory, paper notes, e-notes…)

  3. Improving the Experience • Imagine you could: • Look up terminology, assumptions, as needed • See context-specific examples relevant to the content • Visualize specific content data in various ways • Simulate/explore in context, on demand • Long-term learning/support • Take notes and highlight in-context,within the content • Share and publish observations and learning • Link and associate across content • WebLenses can help!

  4. Learning Theories & Principles • The WebLenses Portal environment: • Reduces “Cognitive Gulfs” (Norman) • Execution, Evaluation • Enables “Guided Noticing” (Pea) • Look, Notice, Comment • Supports development of “Professional Vision” (Goodwin) • Enables refinement of “Perceptual Differentiation” (Gibson)

  5. What is WebLenses Portal • Environment for • displaying web content, applying “lenses” to interact with the content in meaningful ways • Implemented a narrow content slice in a single area – Statistics applied to academic research papers (social sciences) • Open architecture and design • A human performance support, learning support environment

  6. Solution • WebLenses Demo

  7. Assessment - Design • A 2 x 2 design, learning + transfer • 4 subjects in each group • Test (which technique, why, data sensitivity)

  8. Assessment - Results • Initial Learning/Performance • Subsequent Retention/Transfer

  9. Closing • Comments • LDT MA student • SUSE PhD student • Enhancements • Adding content (lenses, notes, content seeding) • Learning sharing (analysis sheets, threads) • Analysis to synthesis

  10. Q & A Thank you.

  11. Learning Problem & Goals • Audience: high school and college students • Problem: • Lack of in-context, just-in-time tools to critically analyze/assess complex statistics-based academic content • Goals: • Identify gaps in statistic data within the content • Reason about sensitivity of findings to changes in conditions/data

  12. Design Process • Inspiration – Data Analysis of research papers • Metaphor • “glass table”, transparent layer on top of the Web • “drafter’s table”, pulling tools for engagement • Started narrow – Statistics • Expanded architecture – Performance Support • Implemented a domain “slice” • Identified next steps, iterations