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

EPE Release 2 IOC ReviewAugust 7, 2012

Ocean Observatories Initiative

OOI EPE Release 2 Initial Operating Capability Review

Educational Visualization (EV) Service

Sage Lichtenwalner (Lead Developer)

R2 IOC ReviewRutgers University, 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


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 VisualizationArtistic Visualization

Visualization

MappingCartography

InfopornChart Junk

Scientific Illustration


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 2006COSEE NOW Educator Survey 2008


Raw

Data

Visualization Tools

Canned Images

Directed Inquiry Guided Inquiry Full Inquiry

ScientificAptitude

Data

Com

plexity

Researchers

Graduate Students

Middle SchoolStudents

HS / UndergradStudents

Public Outreach

Public End Users

Policy Makers

CDM

Cognitive Levels of Different Audiences

A Framework for Data Visualization


EPE Design PhilosophyEPE 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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• 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 toolsChallenges: Storage, Processing power, Metadata, Flexibility


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?





• 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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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


Version 1: raw glider data Version 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


PROTOTYPES

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Visualization Tool #1

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Visualization Design Theory

Ben Fry’s seven stages of visualizing data:• Acquire• Parse• Filter• Mine• Represent• Refine• Interact


Learning Sciences Theory

Visualization for LearnersInquiry-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)


Are we stuck on the left?

http://flowingdata.com/2010/09/21/human-centric-analysis/


Examples

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iBooks AuthorApple

High Adventure ScienceConcord Consortium


Interactive Glider Profile Tool

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Educational Visualization ServiceCapabilities

• 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

Components1. Tool Management (index, create, edit, delete)2. Instance Management (personal index, create, view, edit

options, edit metadata, publish, delete, copy)3. Instance Builder4. Instance Directory (index, search, filter)5. Instance View (view, embed)6. Glider Tools (profile, map, transect)7. Mooring Tools (time series)

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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


Educational Visualization in R2

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Initial Content Model

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

Documents

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