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EPE Release 2 IOC Review August 7, 2012 Ocean Observatories Initiative OOI EPE Release 2 Initial Operating Capability Review Educational Visualization.

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Presentation on theme: "EPE Release 2 IOC Review August 7, 2012 Ocean Observatories Initiative OOI EPE Release 2 Initial Operating Capability Review Educational Visualization."— Presentation transcript:

1 EPE Release 2 IOC Review August 7, 2012 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

2 EPE Release 2 IOC Review August 7, 2012 EV Code Development SLOC Changesets / Tool Versions 1

3 EPE Release 2 IOC Review August 7, 2012 EV Tool Design Specifications 2

4 EPE Release 2 IOC Review August 7, 2012 EV Use Cases 3

5 EPE Release 2 IOC Review August 7, 2012 EV Tool Verification 4

6 EPE Release 2 IOC Review August 7, 2012 EV Demos Web Site Tools 5

7 EPE Release 2 IOC Review August 7, 2012 EV Documentation Inline help Deployment instructions Developer documentation and examples 6

8 EPE Release 2 IOC Review August 7, 2012 EV Usability Questions 7

9 EPE Release 2 IOC Review August 7, 2012 EV Philosophy 8

10 EPE Release 2 IOC Review August 7, 2012 9

11 EPE Release 2 IOC Review August 7, 2012 EPE Educational Visualization Our Design Philosophy Current Prototype Tools Frameworks for using data in education

12 EPE Release 2 IOC Review August 7, 2012 What is “Visualization”? Visual Analysis Bioinformatics Information Visualization (InfoVis) Charting Graphing Data Visualization Data Art Visual Analytics “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 Explorative Visualization Information Art Information Design Infographics Info Aesthetics Pragmatic Visualization Artistic Visualization Visualization Mapping Cartography Infoporn Chart Junk Scientific Illustration

13 EPE Release 2 IOC Review August 7, 2012 What Do Educators Want? 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 NERRS Real-Time Data Needs Assessment 2006 COSEE NOW Educator Survey 2008

14 EPE Release 2 IOC Review August 7, 2012 Raw Data Visualization Tools Canned Images Directed InquiryGuided InquiryFull Inquiry Scientific Aptitude Data Complexity Researchers Graduate Students Middle School Students HS / Undergrad Students Public Outreach Public End Users Policy Makers CDM Cognitive Levels of Different Audiences A Framework for Data Visualization

15 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 14

16 EPE Release 2 IOC Review August 7, 2012 15

17 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 16

18 EPE Release 2 IOC Review August 7, 2012 A Scientist’s Glider Visualization Tool Audience Needs: Raw data, Advanced viz & processing tools Challenges: Storage, Processing power, Metadata, Flexibility

19 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?

20 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 19

21 EPE Release 2 IOC Review August 7, 2012 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. Data lesson: Find your water mass

22 EPE Release 2 IOC Review August 7, 2012 Version 1: raw glider dataVersion 2: simplified data Most students are lacking data skills: Data orientation (using titles, axes, scales, labels) Identifying trends Map reading; latitude and longitude Including units 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 -Cearly defined boundaries between shades, for both scale bar and data plot Strategies: Model data orientation skills Break analysis into steps Clearly label/bold relevant information Simplify data to make trends stand out LSC: Lessons Learned

23 EPE Release 2 IOC Review August 7, 2012 PROTOTYPES 22

24 EPE Release 2 IOC Review August 7, 2012 Visualization Tool #1 23

25 EPE Release 2 IOC Review August 7, 2012 Visualization Tool #2 24

26 EPE Release 2 IOC Review August 7, 2012 Visualization Tool #4 25

27 EPE Release 2 IOC Review August 7, 2012 Visualization Tool #5 26

28 EPE Release 2 IOC Review August 7, 2012 Visualization Design Theory Ben Fry’s seven stages of visualizing data: Acquire Parse Filter Mine Represent Refine Interact

29 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)

30 EPE Release 2 IOC Review August 7, 2012 Are we stuck on the left? http://flowingdata.com/2010/09/21/human-centric-analysis/

31 EPE Release 2 IOC Review August 7, 2012 Examples 30 iBooks Author Apple High Adventure Science Concord Consortium

32 EPE Release 2 IOC Review August 7, 2012 Interactive Glider Profile Tool 31

33 EPE Release 2 IOC Review August 7, 2012 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

34 EPE Release 2 IOC Review August 7, 2012 Educational Visualization in R2 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) 33 Data Layer Science Data CI and EPE Web Services Visualization Tool Scripts file system Customized Visualization Tool Data MySQL Service Layer Web Interface Prototyped in CakePHP Eventually envisioned as a Drupal module Client Layer Web browser based visualization tools D3.js (JavaScript) Google Maps/ OpenLayers API Architecture and Technologies d3 demo

35 EPE Release 2 IOC Review August 7, 2012 Educational Visualization in R2 34 Initial Content Model

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