Design and Evaluation
think of an IV idea (that fulfils a need)
preliminary investigation
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Iterative design
(re) design the visualisation tool/ method
create (a physical representation of) the tool/ method
identify appropriate changes to be made to the design
define tasks for participants to do
ask participants to perform the tasks collect and analyse data
recommendations for improvement (formative)
proof-of-worth (summative) design rationale (summative and formative)
Formative vs Summative Evaluation
Improvement
Proof of worth
Participants
A few each iteration
Participants
Typical users
Typical users
Suggestions (weighted or prioritised)
Evidence of worth
Data and Analysis
Qualitative, thematic
Quantitative, statistics
Formative Evaluation Purpose: to identify improvements
“What can I do to make it better?” (nothing to prove, everything to learn!)
think of an IV idea (that fulfils a need)
preliminary investigation
Formative Evaluation
(re) design the visualisation tool/ method
create (a physical representation of) the tool/ method
identify appropriate changes to be made to the design
define tasks for participants to do
ask participants to perform the tasks collect and analyse data
recommendations for improvement (substantial or minor)
design rationale (justification)
Example Formative Evaluation output
recommendation
creating the initial visualisation
the interface does not clearly indicate the steps to be taken
redesign the menu and the interaction process required for this task
loading data
any data set of more than 100 cases takes over 10s to load
review and redesign the data structure
saving data
any data set of more than 100 cases takes over 10s to save
review and redesign the data structure
reading data values
difficult to see the differences between clustered bars
reconsider colour palette used
reading data values
mis-interpretation of pie charts
consider the situations (with respect to form of data) where pie charts are essential, and only allow use of them where essential
Summative Evaluation Purpose: to present proof-of-worth
“How good is it?”
Summative Evaluation
think of an IV idea (that fulfils a need)
preliminary investigation
(re) design the visualisation tool/ method
create (a physical representation of) the tool/ method
define tasks for participants to do
ask participants to perform the tasks collect and analyse data
proof-of-worth
(with respect to specified criteria)
design rationale (justification)
Summative Evaluation
select a visualisation tool/ method created by someone else
define tasks for participants to do
ask participants to perform the tasks collect and analyse data
proof-of-worth
(with respect to specified criteria)
Example Summative Evaluation output
Quantitative data
Qualitative data
ease of use
75% of participants thought the tool was easy to use
“Rather clumsy”; “Quite usable if you know what you are doing”
90% of participants completed the data interpretation tasks in 2 minutes
“Got through the tasks quite quickly – did I get it right?”
40% of participants took an incorrect navigation path to the ‘create a multivariate visualisation’ page
“Not sure where I was”; “wasn’t’ sure of the importance of the word ‘multivariate’”
85% of participants said they would like to use the tool again
“That was great!”; “Can I do it again, please?”
72% of participants paid the required £1.50 for the extra data analysis features
“Not sure why I had to pay, but I did anyway”; “very cheap”
supports task
No participants made any errors
“The tasks were really easy to complete”
Example Design Rationale output
design choice
colour palette used for map visualisation
As suggested by Boren and Smit (2013), the palette was chosen so as to emphasise values both above and below the mean
icons used to depict the various animals
A Design Space exercise was conducted using dimensions of colour, shape, size, texture so as to identify appropriate icons that show both similarity and differences between the animals
icons used to depict the various animals
10 participants were asked to identify the animals from their icons, with 65% success. Suggestions from the participants were incorporated into redesign, and validated with the participants afterwards
visual variables used for representing quantitative data
A QOC exercise was conducted based on the criteria of pre- attentive perception, so that the most important information ‘pops out’
Design and Evaluation
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