Tuna Anglers in the Tuna Anglers in the Online OOS World:Online OOS World:

A pilot study of how usability testing can A pilot study of how usability testing can guide the development of OOS data products guide the development of OOS data products

and web portalsand web portals

Conducted by members of the COSEE NOW team for MACOORA

September 15, 2008

Project GoalsProject Goals

• To gather input from fishermen to understand how ocean observatory data can best be communicated via Web-based visualization displays, such as maps, charts, etc.

• To use the results to inform the design of data displays for the COSEE NOW website, as well as MACOORA’s own data and model forecast displays.

• To train COSEE NOW team members on the fundamentals of usability testing.

Method: Usability TestingMethod: Usability Testing

An digital media evaluation method that measures the effectiveness of your digital [Web] product with members of your target audience.

Why this Approach?• Verify appeal of current design• Verify effectiveness of current design• Modernize existing design• Determine usefulness of content• Determine how best to display data so that

it’s useful to the data users

Usability TestingUsability Testing

• A typical usability test involves – recruiting a group of users representative

of your target audience– meeting with them at a neutral location – observing and interviewing them

individually as each performs common tasks on a Web site

– audio & video recording the conversation and tracking the viewing of the Web site in real time.

Test SubjectsTest Subjects

• Fishermen/woman: n = 7

• Ages: all 45+

• Professions: varied

• Fish at least weekly, mostly offshore for tuna and shark

• Use of Internet: 5 of 7 daily

Participants’ Use of Participants’ Use of Online DataOnline Data

• Do you use the Internet to search for information before going fishing? Yes (5 of 7), Sometimes (1/7), No (1/7)

• What type of information do you look for? weather (3), water temperature (4), water conditions (2), tides (2), wind, turbidity, chlorophyll, solunar tables (1 each)

• Role of online ocean data: to locate conditions where fish are likely to be

Test ObjectsTest Objects

1. Website Interfaces– theCOOLroom.org web site– Draft redesign of the

COOLroom web site– Rutgers SST data web site

2. Data Visualizations– Sea Surface Temperature

Map– Underwater Profiles from a

Glider– CODAR Velocity Map– CODAR Velocity Animation

Finding Temperature DataFinding Temperature Data

• Subjects start with location: the general area where they plan to fish

• When find sea temperature images, they look for the most recent image with the most data (least cloud cover) for their fishing location

During this task, users were asked to find water temperatures that would help them fish today.

Finding Temperature with SSTFinding Temperature with SST

• Then they look for water temperature breaks: areas where there are dramatic side-by-side temperature differences

• Then they want to locate those break areas

• They’d also like to see the bottom depth and topography at break locales

Data Visualization FindingsData Visualization Findings

• Location: can be via a list (4) or map (3)

• Map: would like zoom feature, plus grab and move feature

• Water temperature images: preferably in Fahrenheit

• Date and time: preferably local• Water temperature breaks:

indicated by colors or well-defined lines

• Break area locations: longitude/latitude

• Bottom depth and topography: need major features and detailed (5 degree) lines

Subsurface Glider DataSubsurface Glider Data • 6/7 recognized the display as temperature and knew how to interpret the colors (preferred Fahrenheit to Celsius)

• 2/7 recognized the thermocline being represented in the temperature graph and expressed it’s importance in offshore fishing

• None of the participants matched up the transect plots with their location on the map

•2/7 expressed concern that if the data was not in the Hudson Canyon area it was not useful to them

Real-time Surface CurrentsReal-time Surface Currents• 5/7 users recognized the

arrows as the flow of water, but many were not sure. [3] did not realize this until the animation.

• 3/7 users identified the colors as temperatures, even after noting the colorbar axis showing velocity

• Some users wanted to click on the image to zoom in to see smaller regions

• Users did not like the units (cm/s). Because of the range of values (0-50), one user mistook them as wind speeds (i.e. mi/hr).

Expressed interest in seeing where the hotspots they identify in the temperature fields will move. Especially useful if the

clouds cover the more recent SST data or as a forecast for future movements.

Surface Current AnimationSurface Current Animation• 6/7 were able to recognize the

depiction of currents and how they change over time

• 4/7 users said they thought the colors were showing temperatures and assumed the animation was showing “directions of temperatures” or “currents of temperatures”

• Because of the usefulness of combining temp and current data, many users were interested in exploring further.

• 2/7 users wanted to determine if the animation was showing hourly or seasonal changes but did not recognize the timeframe of the animation

Several users wanted additional information about the animation, including: - What does the map show?

- How frequently it was updated? - What time period does it cover?

- an example animation with a description of the features it depicts

RecommendationsRecommendations

• Provide detailed SST imagery

• Provide links to other relevant data sources on the web

• Develop new data displays and tutorials to explain their use

• Improve data legends and displays

AcknowledgementsAcknowledgements

We would like to thank David Chapman for the funding to conduct this study. We are grateful for the partnership of MACOORA partners. A special thanks to Cia Romano and Kyle Kulakowski at Interface Guru for their knowledge and guidance and Jeff Yapalater, from the Freeport Tuna Club for recruiting the participants and his gracious assistance in logistical planning for the test. In addition, we’re grateful to the COSEE NOW team: Chris Parsons (Word Craft) for her assistance in data tabulation, Janice McDonnell and Dr. Rebecca Jordan (Rutgers University) for moderating the tests, Stephen Gray (graduate student at Rutgers University) for assistance with working with the test subjects, Sage Lichtenwalner (Rutgers University) for his technical expertise, Corinne Dalelio (graduate student Rutgers University) for assistance with data tabulation, and Igor Heifetz and Lisa Ojanen (Rutgers University) for attending the training.