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IAT 334
Experimental Evaluation
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SCHOOL OF INTERACTIVE ARTS + TECHNOLOGY [SIAT] | WWW.SIAT.SFU.CA
March 10, 2011 IAT 334 2
Evaluation
g Evaluation stylesg Subjective data
– Questionnaires, Interviewsg Objective data
– Observing Users• Techniques, Recording
g Usability Specifications– Why, How
March 10, 2011 IAT 334 3
Our goal?
March 10, 2011 IAT 334 4
Evaluation
g Earlier:– Interpretive and Predictive
• Heuristic evaluation, walkthroughs, ethnography…
g Now:– User involved
• Usage observations, experiments, interviews...
March 10, 2011 IAT 334 5
Evaluation Forms
g Summative– After a system has been finished. Make
judgments about final item.
g Formative– As project is forming. All through the
lifecycle. Early, continuous.
March 10, 2011 IAT 334 6
Evaluation Data Gathering
g Design the experiment to collect the data to test the hypothesis to evaluate the interface to refine the design
g Information we gather about an interface can be subjective or objective
g Information also can be qualitative or quantitative– Which are tougher to measure?
March 10, 2011 IAT 334 7
Subjective Data
g Satisfaction is an important factor in performance over time
g Learning what people prefer is valuable data to gather
March 10, 2011 IAT 334 8
Methods
g Ways of gathering subjective data– Questionnaires– Interviews– Booths (eg, trade show)– Call-in product hot-line– Field support workers
March 10, 2011 IAT 334 9
Questionnaires
g Preparation is expensive, but administration is cheap
g Oral vs. written– Oral advs: Can ask follow-up questions– Oral disadvs: Costly, time-consuming
g Forms can provide better quantitative data
March 10, 2011 IAT 334 10
Questionnaires
g Issues– Only as good as questions you ask– Establish purpose of questionnaire– Don’t ask things that you will not use– Who is your audience?– How do you deliver and collect
questionnaire?
March 10, 2011 IAT 334 11
Questionnaire Topic
g Can gather demographic data and data about the interface being studied
g Demographic data:– Age, gender– Task expertise– Motivation– Frequency of use– Education/literacy
March 10, 2011 IAT 334 12
Interface Data
g Can gather data about– screen– graphic design– terminology– capabilities– learning– overall impression– ...
March 10, 2011 IAT 334 13
Question Format
g Closed format– Answer restricted to a set of choices
Characters on screen
hard to read easy to read 1 2 3 4 5 6 7
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Closed Format
g Likert Scale– Typical scale uses 5, 7 or 9 choices– Above that is hard to discern– Doing an odd number gives the neutral
choice in the middle
March 10, 2011 IAT 334 15
Closed Format
g Advantages– Clarify alternatives– Easily quantifiable– Eliminates useless answers
g Disadvantages– Must cover whole range– All should be equally likely– Don’t get interesting, “different”
reactions
March 10, 2011 IAT 334 16
Issues
g Question specificity– “Do you have a computer?”
g Language– Beware terminology, jargon
g Clarityg Leading questions
– Can be phrased either positive or negative
March 10, 2011 IAT 334 17
Issues
g Prestige bias– People answer a certain way because
they want you to think that way about them
g Embarrassing questionsg Hypothetical questionsg “Halo effect”
– When estimate of one feature affects estimate of another (eg, intelligence/looks)
March 10, 2011 IAT 334 18
Deployment
g Steps– Discuss questions among team– Administer verbally/written to a few
people (pilot). Verbally query about thoughts on questions
– Administer final test
March 10, 2011 IAT 334 19
Open-ended Questions
g Asks for unprompted opinionsg Good for general, subjective
information, but difficult to analyze rigorously
g May help with design ideas– “Can you suggest improvements to this
interface?”
March 10, 2011 IAT 334 20
Ethics
g People can be sensitive about this process and issues
g Make sure they know you are testing software, not them
g Attribution theory – Studies why people believe that they
succeeded or failed--themselves or outside factors (gender, age differences)
g Can quit anytime
March 10, 2011 IAT 334 21
Objective Data
g Users interact with interface– You observe, monitor, calculate,
examine, measure, …
g Objective, scientific data gatheringg Comparison to interpretive/predictive
evaluation
March 10, 2011 IAT 334 22
Observing Users
g Not as easy as you think
g One of the best ways to gather feedback about your interface
g Watch, listen and learn as a person interacts with your system
March 10, 2011 IAT 334 23
Observation
g Direct– In same room– Can be intrusive– Users aware of
your presence– Only see it one
time– May use
semitransparent mirror to reduce intrusiveness
g Indirect– Video recording– Reduces intrusiveness, but doesn’t
eliminate it– Cameras focused on screen, face &
keyboard– Gives archival record, but can spend a
lot of time reviewing it
March 10, 2011 IAT 334 24
Location
g Observations may be– In lab - Maybe a specially built usability
lab• Easier to control• Can have user complete set of tasks
– In field• Watch their everyday actions• More realistic• Harder to control other factors
March 10, 2011 IAT 334 25
Challenge
g In simple observation, you observe actions but don’t know what’s going on in their head
g Often utilize some form of verbal protocol where users describe their thoughts
March 10, 2011 IAT 334 26
Verbal Protocol
g One technique: Think-aloud– User describes verbally what s/he is
thinking and doing• What they believe is happening• Why they take an action• What they are trying to do
March 10, 2011 IAT 334 27
Think Aloud
g Very widely used, useful techniqueg Allows you to understand user’s
thought processes better
g Potential problems:– Can be awkward for participant– Thinking aloud can modify way user
performs task
March 10, 2011 IAT 334 28
Teams
g Another technique: Co-discovery learning– Join pairs of participants to work
together– Use think aloud– Perhaps have one person be semi-
expert (coach) and one be novice– More natural (like conversation) so
removes some awkwardness of individual think aloud
March 10, 2011 IAT 334 29
Alternative
g What if thinking aloud during session will be too disruptive?
g Can use post-event protocol– User performs session, then watches
video afterwards and describes what s/he was thinking
– Sometimes difficult to recall
March 10, 2011 IAT 334 30
Historical Record
g In observing users, how do you capture events in the session for later analysis?
March 10, 2011 IAT 334 31
Capturing a Session
g 1. Paper & pencil– Can be slow– May miss things– Is definitely cheap and easy
Time 10:00 10:03 10:08 10:22
Task 1 Task 2 Task 3 …
Se
Se
March 10, 2011 IAT 334 32
Capturing a Session
g 2. Audio tape– Good for talk-aloud– Hard to tie to interface
g 3. Video tape– Multiple cameras probably needed– Good record– Can be intrusive
March 10, 2011 IAT 334 33
Capturing a Session
g 4. Software logging– Modify software to log user actions– Can give time-stamped key press or
mouse event– Two problems:
• Too low-level, want higher level events• Massive amount of data, need analysis tools
March 10, 2011 IAT 334 34
Assessing Usability
g Usability Specifications– Quantitative usability goals, used a
guide for knowing when interface is “good enough”
– Should be established as early as possible in development process
March 10, 2011 IAT 334 35
Measurement Process
g “If you can’t measure it, you can’t manage it”
g Need to keep gathering data on each iterative refinement
March 10, 2011 IAT 334 36
What to Measure?
g Usability attributes– Initial performance– Long-term performance– Learnability– Retainability– Advanced feature usage– First impression– Long-term user satisfaction
March 10, 2011 IAT 334 37
How to Measure?g Benchmark Task
– Specific, clearly stated task for users to carry out
g Example: Calendar manager– “Schedule an appointment with Prof.
Smith for next Thursday at 3pm.”
g Users perform these under a variety of conditions and you measure performance
March 10, 2011 IAT 334 38
Assessment Technique
Usability Measure Value to Current Worst Planned Best poss Observattribute instrument be measured level acc level target level level results
Initial Benchmk Length of 15 secs 30 secs 20 secs 10 secs perf task time to (manual) success add appt on first trial
First Quest -2..2 ?? 0 0.75 1.5impression
March 10, 2011 IAT 334 39
Summary
g Measuring Instrument– Questionnaires– Benchmark tasks
March 10, 2011 IAT 334 40
Summary
g Value to be measured– Time to complete task– Number of percentage of errors– Percent of task completed in given time– Ratio of successes to failures– Number of commands used– Frequency of help usage
March 10, 2011 IAT 334 41
Summary
g Target level– Often established by comparison with
competing system or non-computer based task
Ethics
g Testing can be arduousg Each participant should consent to
be in experiment (informal or formal)– Know what experiment involves, what to
expect, what the potential risks are g Must be able to stop without danger
or penaltyg All participants to be treated with
respectNov 2, 2009 IAT 334 42
Consentg Why important?
– People can be sensitive about this process and issues
– Errors will likely be made, participant may feel inadequate
– May be mentally or physically strenuousg What are the potential risks (there are
always risks)?– Examples?
g “Vulnerable” populations need special care & consideration (& IRB review)– Children; disabled; pregnant; students (why?)Nov 2, 2009 IAT 334 43
Before Studyg Be well prepared so participant’s time is
not wastedg Make sure they know you are testing
software, not them– (Usability testing, not User testing)
g Maintain privacyg Explain procedures without compromising
resultsg Can quit anytimeg Administer signed consent formNov 2, 2009 IAT 334 44
During Study
Make sure participant is comfortable Session should not be too long Maintain relaxed atmosphere Never indicate displeasure or anger
Nov 2, 2009 IAT 334 45
After Study
State how session will help you improve system
Show participant how to perform failed tasks
Don’t compromise privacy (never identify people, only show videos with explicit permission)
Data to be stored anonymously, securely, and/or destroyed
Nov 2, 2009 IAT 334 46
March 10, 2011 IAT 334 47
One Model