Ocean Observatories Initiative
OOI EPE Release 2 Initial Operating
Capability Review
Educational Visualization (EV) Service
Sage Lichtenwalner (Lead Developer)
R2 IOC Review
Rutgers University, August 7, 2012
EPE Release 2 IOC Review
August 7, 2012
EV Code Development
• SLOC
• Changesets / Tool Versions
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EV Tool Design Specifications
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EV Use Cases
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EV Tool Verification
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EV Demos
• Web Site
• Tools
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EV Documentation
• Inline help
• Deployment instructions
• Developer documentation and examples
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EV Usability Questions
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EV Philosophy
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EPE Educational Visualization
• Our Design Philosophy
• Current Prototype Tools
• Frameworks for using data in education
EPE Release 2 IOC Review
August 7, 2012
What is “Visualization”?
Info Aesthetics
Data Visualization
Visual Analysis
Information Design
Visual Analytics
Explorative Visualization
Visualization
Information Visualization
(InfoVis)
Bioinformatics
Pragmatic Visualization
Charting
Cartography
Graphing
Infographics
Data Art
Mapping
Scientific Illustration
Information Art
Infoporn
Chart Junk
Artistic Visualization
“Visual analysis is not primarily about the pictures, but about finding ways to use our
powerful visual systems to analyze data. It's analysis done in a visual way. It's visual
exploration, visual data analysis, and visual presentation of results.”
Robert Kosara, eagereyes.org
EPE Release 2 IOC Review
August 7, 2012
What Do Educators Want?
NERRS Real-Time Data Needs Assessment 2006
COSEE NOW Educator Survey 2008
Top requested features:
• Data visualization tools (ability to graph, map, chart data)
• Inquiry-based lessons/activities
• Lesson plans for teaching science concepts with RTD
• Locally relevant data sets
• Map interfaces
• Stories or case studies that show how scientists use real-time data.
Middle-school teachers were more likely to have students use:
• Computers at school as part of their lessons
• The Internet/websites at school as part of their lessons
• Real-time data (mostly student-collected data) as part of their
lessons
EPE Release 2 IOC Review
August 7, 2012
A Framework for Data Visualization
Cognitive Levels of Different Audiences
Raw Data
Researchers
Graduate
Students
Visualization
Tools
CDM
HS / Undergrad
Students
Canned
Images
Data
Complexity
Public
End Users
Public
Outreach
Middle School
Students
Directed Inquiry
Scientific
Aptitude
EPE Release 2 IOC Review
August 7, 2012
Policy Makers
Guided Inquiry
Full Inquiry
EPE Design Philosophy
EPE Visualization Tools are designed to be:
•
•
Customizable
– Adapt them to fit your learning goals
Embeddable
– Place them in context within your lessons
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EPE Design Philosophy
EPE Visualization Tools are designed to be:
•
•
•
•
Customizable
– Adapt them to fit your learning goals
Embeddable
– Place them in context within your lessons
Focused
– Learning outcome driven not data driven (I.e. these are not your
typical comprehensive tool)
Simple
– Intuitive & easy to use
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A Scientist’s Glider Visualization Tool
Audience Needs: Raw data, Advanced viz & processing tools
Challenges: Storage, Processing power, Metadata, Flexibility
EPE Release 2 IOC Review
August 7, 2012
And Educator’s Visualization Tool
Driving Questions:
This tool will allow students
to analyze single glider
profiles (also called casts)
to investigate the following
questions:
• How do measurements
vary over the depth of
the water column?
• How does the shape of a
particular
measurement's profile
vary over time and/or
location?
EPE Release 2 IOC Review
August 7, 2012
EPE Design Philosophy
EPE Visualization Tools are designed to be:
•
•
•
•
•
Customizable
– Adapt them to fit your learning goals
Embeddable
– Place them in context within your lessons
Focused
– Learning outcome driven not data driven (I.e. these are not your
typical comprehensive tool)
Simple
– Intuitive & easy to use
Educational
– Primary goal is to aid analysis and understanding of scientific
processes, not dealing with data formats and graphing
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EPE Release 2 IOC Review
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Data lesson: Find your water mass
Students use two different properties measured by Slocum gliders to
find a water mass with a particular “fingerprint”.
Ross Sea, Antarctica “fingerprint:”
Temperature = 1.3°C
Oxygen concentration = 6.5ml/L
Students are guided through the process:
1) Identify where each “fingerprint” property is on the associated data visualization.
2) Identify where the two properties overlap (200-300m depth, ~600km along segment distance).
3) Use the map of the glider track (not shown) to find the latitude and longitude of that point along the segment.
EPE Release 2 IOC Review
August 7, 2012
LSC: Lessons Learned
Most students are lacking data skills:
• Data orientation (using titles, axes, scales, labels)
• Identifying trends
• Map reading; latitude and longitude
• Including units
Strategies:
• Model data orientation skills
• Break analysis into steps
• Clearly label/bold relevant information
• Simplify data to make trends stand out
Version 1: raw glider data
Students had difficulty
• -choosing the correct color from the scale
• -outlining the appropriate color on the data plot.
EPE Release 2 IOC Review
August 7, 2012
Version 2: simplified data
•
•
Reduced number of shades on the color scale
-Cearly defined boundaries between shades, for both
scale bar and data plot
PROTOTYPES
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Visualization Tool #1
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Visualization Tool #2
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Visualization Tool #4
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Visualization Tool #5
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Visualization Design Theory
Ben Fry’s seven stages of visualizing data:
• Acquire
• Parse
• Filter
• Mine
• Represent
• Refine
• Interact
EPE Release 2 IOC Review
August 7, 2012
Learning Sciences Theory
Visualization for Learners
Inquiry-based learning units must
include:
• Motivating context
• Learner-appropriate activities
• Appropriate datasets
• Scaffolding interfaces
• Support for learning
(Edelson & Gordin 1998)
Learning-for-Use
• Motivation
– Experience demand
– Experience curiosity
• Knowledge Construction
– Observe
– Receive communication
• Knowledge Refinement
– Apply
– Reflect
(Edelson 2001)
EPE Release 2 IOC Review
August 7, 2012
Are we stuck on the left?
EPE Release 2 IOC Review
August 7, 2012
http://flowingdata.com/2010/09/21/human-centric-analysis/
Examples
High Adventure Science
Concord Consortium
iBooks Author
Apple
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Interactive Glider Profile Tool
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Educational Visualization Service
Capabilities
• Provides a collection of interactive
visualization tools to explore and analyze OOI
Datasets
• R2: Focus on profile and time series tools
• Allows users to save customize tools (called
“custom instances”)
• R2: Beta version of this functionality
• Allows users to embed custom instances on
third-party web pages
• R2: Test capability. Implement in R3
• Provides a library of visualizations
• R2: List only, search and categorization in R3
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Educational Visualization in R2
Architecture and Technologies
Data Layer
Service Layer
Client Layer
• Science Data
• CI and EPE Web Services
• Visualization Tool Scripts
• file system
• Customized Visualization Tool Data
• MySQL
• Web Interface
• Prototyped in
CakePHP
• Eventually
envisioned as a
Drupal module
• Web browser
based visualization
tools
• D3.js (JavaScript)
• Google Maps/
OpenLayers API
Components
1. Tool Management (index, create, edit, delete)
2. Instance Management (personal index, create, view,
edit options, edit metadata, publish, delete, copy)
3. Instance Builder
4. Instance Directory (index, search, filter)
5. Instance View (view, embed)
6. Glider Tools (profile, map, transect)
7. Mooring Tools (time series)
d3 demo
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Educational Visualization in R2
Initial Content Model
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