1. WebLenses Bringing 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) Control (paper reference material) Treatment (WebLenses) Reference material available Article 1 Transfer (no reference material) Article 2

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