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OOI EPE Release 2 Initial Operating Capability Review Educational Visualization (EV) Service

OOI EPE Release 2 Initial Operating Capability Review Educational Visualization (EV) Service. Sage Lichtenwalner (Lead Developer) R2 IOC Review Rutgers University, August 7, 2012. EV Code Development. SLOC Changesets / Tool Versions. EV Tool Design Specifications. EV Use Cases.

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OOI EPE Release 2 Initial Operating Capability Review Educational Visualization (EV) Service

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  1. OOI EPE Release 2 Initial Operating Capability ReviewEducational Visualization (EV) Service Sage Lichtenwalner (Lead Developer) R2 IOC Review Rutgers University, August 7, 2012

  2. EV Code Development SLOC Changesets / Tool Versions

  3. EV Tool Design Specifications

  4. EV Use Cases

  5. EV Tool Verification

  6. EV Demos Web Site Tools

  7. EV Documentation Inline help Deployment instructions Developer documentation and examples

  8. EV Usability Questions

  9. EV Philosophy

  10. EPE Educational Visualization Our Design Philosophy Current Prototype Tools Frameworks for using data in education

  11. What is “Visualization”? Info Aesthetics Data Visualization Visual Analysis Information Design Visual Analytics Explorative Visualization Visualization Infographics Information Art Information Visualization (InfoVis) Data Art Charting Infoporn Bioinformatics Mapping Chart Junk Cartography Pragmatic Visualization Artistic Visualization Graphing Scientific Illustration “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

  12. 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

  13. A Framework for Data Visualization Cognitive Levels of Different Audiences Researchers Graduate Students CDM HS / Undergrad Students Public End Users Public Outreach Data Complexity Middle School Students Policy Makers Scientific Aptitude

  14. 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

  15. 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 

  16. A Scientist’s Glider Visualization Tool Audience Needs: Raw data, Advanced viz & processing tools Challenges: Storage, Processing power, Metadata, Flexibility

  17. 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?

  18. 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

  19. 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: Identify where each “fingerprint” property is on the associated data visualization. Identify where the two properties overlap (200-300m depth, ~600km along segment distance). Use the map of the glider track (not shown) to find the latitude and longitude of that point along the segment.

  20. 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 Version 2: simplified data Students had difficulty • -choosing the correct color from the scale • -outlining the appropriate color on the data plot. • Reduced number of shades on the color scale • -Cearlydefined boundaries between shades, for both scale bar and data plot

  21. Prototypes

  22. Visualization Tool #1

  23. Visualization Tool #2

  24. Visualization Tool #4

  25. Visualization Tool #5

  26. Visualization Design Theory Ben Fry’s seven stages of visualizing data: • Acquire • Parse • Filter • Mine • Represent • Refine • Interact

  27. 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)

  28. Are we stuck on the left? http://flowingdata.com/2010/09/21/human-centric-analysis/

  29. Examples High Adventure Science Concord Consortium iBooks Author Apple

  30. Interactive Glider Profile Tool

  31. 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 32

  32. Educational Visualization in R2 Architecture and Technologies d3 demo Components Tool Management (index, create, edit, delete) Instance Management (personal index, create, view, edit options, edit metadata, publish, delete, copy) Instance Builder Instance Directory (index, search, filter) Instance View (view, embed) Glider Tools (profile, map, transect) Mooring Tools (time series)

  33. Educational Visualization in R2 Initial Content Model

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