weblenses bringing data into focus
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WebLenses Bringing Data into Focus. Haggai Mark Learning, Design & Technology Stanford University 2009 . What’s the problem? (Why WebLenses). When reading web content Terminology, assumptions, background unfamiliar, unclear Data presentation hard to digest - PowerPoint PPT PresentationTRANSCRIPT
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
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