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User Modeling

Predicting thoughts and actionsGOMS

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Agenda

g User modeling– Fitt’s Law

– GOMS

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User Modeling

g Idea: If we can build a model of how a user works, then we can predict how s/he will interact with the interface– Predictive modeling

g Many different modeling techniques exist

User Modeling – 2 typesg Stimulus-Response

– Hick’s law– Practice law– Fitt’s law

g Cognitive – human as interperter/predictor – based on Model Human Processor (MHP)– Key-stroke Level Model

• Low-level, simple– GOMS (and similar) Models

• Higher-level (Goals, Operations, Methods, Selections)• Not discussed here

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Power Law of Practice

g Tn = T1n-a

– Tn to complete the nth trial is T1 on the first trial times n to the power -a; a is about .4, between .2 and .6

– Skilled behavior - Stimulus-Response and routine cognitive actions• Typing speed improvement• Learning to use mouse• Pushing buttons in response to stimuli• NOT learning

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Power Law of Practice

g How to use it?– Use measured T1 on the first trial

• Predict whether usability criteria will be met• How many trials?

–Predict how many practice iterations needed to reach usability criteria

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Hick’s Law

g Decision time to choose among n equally likely alternatives– T = Ic log2(n+1)

– Ic ~ 150 msec

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Hick’s Law

g How to use it?– Menu selection– Choose among 64 choices:

• Single 64-item menu• 2-level menu: 8 choices at each level• 2-level menu: 4 choices then 16 choices

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Fitts’ Law

g Models movement times for selection (reaching) tasks in one dimension

g Basic idea: Movement time for a selection task– Increases as distance to target

increases– Decreases as size of target increases

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Fitts Experiment: 1D

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d w

Fitts: Index of Difficulty

g ID - Index of difficulty

g ID is an information theoretic quantity– Based on work of Shannon – larger target =>

more information (less uncertainty)

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ID = log2 (d/w + 1.0)

bits result

width (tolerance)of target

distance to move

Fitts formula

g MT - Movement time

g MT is a linear function of ID k1 and k2 are experimental constants

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MT = k1 + k2*IDMT = k1 + k2 *log2 (d/w + 1.0)

g Run empirical tests to determine k1 and k2 in MT = k1 + k2* ID

g Will get different ones for different input devices and device uses

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MT

ID = log2(d/w = 1.0)

What about 2D

g h x w rect:one way is ID = log2(d/min(h, w) + 1)– Should take into account direction of

approach

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Design implications

g Menu item sizeg Icon sizeg Put frequenlty used icons togetherg Scroll bar target size and placement

– Up / down scroll arrows together or at top and bottom of scroll bar

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GOMS

g One of the most widely known

g Assumptions– Know sequence of operations for a task– Expert will be carrying them out

g Goals, Operators, Methods, Selection

Rules

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GOMS Procedure

g Walk through sequence of steps g Assign each an approximate time

duration

-> Know overall performance time

g (Can be tedious)

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Limitations

g GOMS is not for– Tasks where steps are not well

understood– Inexperienced users

g Why?

g Good example: Move a sentence in a document to previous paragraph

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Goal

g End state trying to achieveg Then decompose into subgoals

Moved sentence

Select sentence

Cut sentence

Paste sentenceMove to new spot

Place it

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Operators

g Basic actions available for performing a task (lowest level actions)

g Examples: move mouse pointer, drag, press key, read dialog box, …

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Methods

g Sequence of operators (procedures) for accomplishing a goal (may be multiple)

g Example: Select sentence– Move mouse pointer to first word– Depress button– Drag to last word– Release

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Selection Rules

g Invoked when there is a choice of a method

g Example: Could cut sentence either by menu pulldown or by ctrl-x

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Further Analysis

g GOMS is often combined with a keystroke level analysis– Assigns times to different operators– Plus: Rules for adding M’s (mental

preparations) in certain spots

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Example

1. Select sentence Reach for mouse H 0.40 Point to first word P 1.10 Click button down K 0.60 Drag to last word P 1.20 Release K 0.60

3.90 secs

2. Cut sentence Press, hold ^ Point to menu Press and release ‘x’or Press and hold mouse Release ^ Move to “cut”

Release

3. ...

Move Sentence

Keystroke-Level Model

g Simplified GOMSg KSLM - developed by Card, Moran &

Newell, see their book– The Psychology of Human-Computer

Interaction, Card, Moran and Newell, Erlbaum, 1983

g Skilled users performing routine tasksg Assigns times to basic human operations -

experimentally verifiedg Based on MHP - Model Human ProcessorFeb 24, 2011 IAT 334 25

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User Profiles

g Attributes:– attitude, motivation, reading level,

typing skill, education, system experience, task experience, computer literacy, frequency of use, training, color-blindness, handedness, gender,…

g Novice, intermediate, expert

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Motivation

g User– Low motivation, discretionary use– Low motivation, mandatory– High motivation, due to fear– High motivation, due to interest

g Design goal– Ease of learning

– Control, power

– Ease of learning, robustness, control– Power, ease of use

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Knowledge & Experience

g Experienceg task system

– low low

– high high

– low high

– high low

g Design goals– Many syntactic and semantic prompts– Efficient commands, concise syntax– Semantic help facilities

– Lots of syntactic prompting

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Job & Task Implicationsg Frequency of use

– High - Ease of use– Low - Ease of learning & remembering

g Task implications– High - Ease of use– Low - Ease of learning

g System use– Mandatory - Ease of using– Discretionary - Ease of learning

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Modeling Problems

g 1. Terminology - example– High frequency use experts - cmd

language– Infrequent novices - menus

g What’s “frequent”, “novice”?

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Modeling Problems (contd.)

g 2. Dependent on “grain of analysis” employed– Can break down getting a cup of coffee

into 7, 20, or 50 tasks– That affects number of rules and their

types

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Modeling Problems (contd.)

g 3. Does not involve user per se– Don’t inform designer of what user

wants

g 4. Time-consuming and lengthy