WebLensesBringing Data into Focus

Haggai MarkLearning, Design & Technology

Stanford University2009

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…)

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!

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)

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

Solution

• WebLenses Demo

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 Article 1

Transfer (no

reference material)

Article 2 Article 2

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

Closing

• Comments– LDT MA student– SUSE PhD student

• Enhancements– Adding content (lenses, notes, content seeding)– Learning sharing (analysis sheets, threads)– Analysis to synthesis

Q & A

Thank you.

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

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