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Towards a Statistical Knowledge Network. gov Scientific Approach (beyond user friendly) Specify users and tasks Predict and measure –time to learn –speed of performance –rate of human errors –human retention over time Assess subjective satisfaction (Questionnaire for User Interface Satisfaction) Accommodate individual differences Consider social, organizational & cultural context
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Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland at College Park Gary Marchionini, Stephanie Haas & Carol Hert University of North Carolina at Chapel Hill (NSF Grants EIA 0131824 & EIA 0129978) www.ils.unc.edu/govstat
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Page 1: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

Ben Shneiderman & Catherine PlaisantUniversity of Maryland at College Park

Gary Marchionini, Stephanie Haas & Carol HertUniversity of North Carolina at Chapel Hill

(NSF Grants EIA 0131824 & EIA 0129978)

www.ils.unc.edu/govstat

Page 2: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

.gov

Human-Computer Interaction Laboratory

Interdisciplinary research community - Computer Science & Psychology - Information Studies & Education (www.cs.umd.edu/hcil)

Page 3: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

.gov

Scientific Approach (beyond user friendly)• Specify users and tasks• Predict and measure

– time to learn– speed of performance– rate of human errors– human retention over time

• Assess subjective satisfaction (Questionnaire for User Interface Satisfaction)

• Accommodate individual differences• Consider social, organizational & cultural context

Page 4: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

.gov

U.S. Library of Congress

• Scholars, Journalists, Citizens

• Teachers, Students

Page 5: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

.gov

Visible Human Explorer (NLM)

• Doctors

• Surgeons

• Researchers

• Students

Page 6: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

.gov

NASA Environmental Data

• Scientists

• Farmers

• Land planners

• Students

Page 7: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

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Bureau of the Census

• Economists, Policy makers, Journalists

• Teachers, Students

Page 8: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

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NSF Digital Government Initiative

• Find what you need• Understand what you Find

www.ils.unc.edu/govstat/

Census,NCHS, BLS, EIA,NASS, SSA

Page 9: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Treemap: Stock market, industry clustered

Page 10: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Types of Collaboration• Direct Support (2003-05)

– BLS, SSA, Census, NCHS• Prototype installation on FedStats server• Face to Face Meetings

– Biannual meetings in Washington DC (May, Dec)– Help Symposium in Chapel Hill, NC (Jan 21-22, 2005)– Partner meetings (BLS, Census, NICS)

• Listservs, paper co-authorships, evaluation • Participate in Digital Government Conferences

Page 11: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

Towards a Statistical Knowledge Network

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Research Value of DG Domain• Opportunity to

– See research applied in important domains– Work with large-scale problems

• Number of people affected• Size of data sets and number of systems• Open data sets

– Help agencies shape vision and service– Work in interdisciplinary teams

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Challenges/Barriers• Large number of stakeholders

– Agencies and personnel– Diverse citizen population

• Interagency cultures and perspectives– Federal and state/local agencies– Academic departments

• Emphasis on security diverts attention from other important issues

Page 13: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Scientific Research Objective• Support non-specialist access and understanding of

government statistics– Develop and evaluate

• user interfaces• metadata strategies• online help

– Develop alternative information architectures and strategies

“Find what you need, understand what you find”

Page 14: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Accomplishments• SKN architecture• Relation Browser & Crawler• Layered help model & ShowMe!s• Layered metadata model • Interactive Glossary implementation and

user studies • Audio map interface

Page 15: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Statistical Knowledge Network Architecture

Agencies

SKN RegistryActions

Contribute Find

Display Annotate

UnderstandManipulate Collaborate

…..

………….

ObjectsActions

Private Work Space

ObjectsActions

Private Work Space

ObjectsActions

Private Work Space

Ontology Rules & Constraints

SKN Consortium

…..

Objects Reports metadataTables metadataPeople metadata

GlossaryAnnotations

Page 16: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Relation Browser: EIA pages

Page 17: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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ShowMe! : Recorded Demonstrations

• Narrated demonstrations of procedures• Show users < 1 min demos• Use Screen Capture (e.g. Camtasia)

with audio & highlighting• Better than videos:

sharper & smaller files

Page 18: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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Guidelines: Recorded Demonstrations

• Provide procedural or instructional information rather than conceptual information

• Keep segments short • Ensure that tasks are clear and simple• Coordinate demonstrations with text documentation• Use spoken narration • Be faithful to the actual user interface • Use highlighting to guide attention• Ensure user control• Keep file sizes small• Strive for universal usability

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Empirical Studies: FedStats

1st : alphabetical list of 645 destination links 15 subjects starting on A-Z page/home page

2nd : 16 categories list of 549 destination links 15 subjects starting on categories page

3rd : 16 categories list of 215 portal links15 subjects starting on categories page

Page 20: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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FedStats - original version 645 links

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Scenarios

Scenario 1: “I’m a social activist in the Raleigh-Durham, North Carolina area and have become increasingly concerned about urban sprawl and the loss of rural areas for both farming and recreation. I need statistics to support my claim that significant differences occur when urban development occurs in rural and/or farming areas.”

Scenario 2: “I would like to open a grocery store specializing in organic products in the greater Seattle metropolitan area. What are the trends in production and consumption of organic food products? Would the Seattle area be a good place to locate?”

Scenario 3:” I’m contemplating a move from Seattle to Bozeman, MT. How do they compare?“

Page 22: Towards a Statistical Knowledge Network Ben Shneiderman & Catherine Plaisant University of Maryland…

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FedStats: 16 categories list of 549 destination links

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Towards a Statistical Knowledge Network

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FedStats: 16 categories list of 215 portal links

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Results1st study 2nd study 3rd study

correct answers 15% 24% 42%website usefulness 35% 46% 68%

website ease of use 42% 56% 73%

average frustration(scale 1-10)

6 6 3

spent too much time 58% 55% 32%

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Recommendations

Universal access - accommodate diversity of users: experts, first-time & one-time users

Easy Navigation - structured information Common language - easy to understand terminology Comparative search - allow comparative search. Advanced search - fully functional search Data Tools - support viewing & analyzing statistical data Data Granularity - Allow user choice by geography & time

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Recommendations for Improving DG Program

• Develop formal technology transfer models• Develop sustainability models linked to

technology transfer• Increase levels of funding• Develop mechanisms to involve state and local

government agencies


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