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User Modeling. Predicting thoughts and actions GOMS. Agenda. User modeling Fitt’s Law GOMS. User Modeling. 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 Many different modeling techniques exist.

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user modeling

User Modeling

Predicting thoughts and actions

GOMS

agenda
Agenda
  • User modeling
    • Fitt’s Law
    • GOMS

IAT 334

user modeling1
User Modeling
  • 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
  • Many different modeling techniques exist

IAT 334

user modeling 2 types
User Modeling – 2 types
  • Stimulus-Response
    • Hick’s law
    • Practice law
    • Fitt’s law
  • 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

IAT 334

power law of practice
Power Law of Practice
  • 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

IAT 334

power law of practice1
Power Law of Practice
  • 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

IAT 334

hick s law
Hick’s Law
  • Decision time to choose among n equally likely alternatives
    • T = Ic log2(n+1)
    • Ic ~ 150 msec

IAT 334

hick s law1
Hick’s Law
  • 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

IAT 334

fitts law
Fitts’ Law
  • Models movement times for selection (reaching) tasks in one dimension
  • Basic idea: Movement time for a selection task
    • Increases as distance to target increases
    • Decreases as size of target increases

IAT 334

fitts index of difficulty
Fitts: Index of Difficulty
  • ID - Index of difficulty
  • ID is an information theoretic quantity
    • Based on work of Shannon – larger target => more information (less uncertainty)

ID = log2 (d/w + 1.0)

width (tolerance)

of target

bits

result

distance

to move

IAT 334

fitts formula
Fitts formula
  • MT - Movement time
  • MT is a linear function of ID

k1 and k2 are experimental constants

MT = k1 + k2*ID

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

IAT 334

slide13

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

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

MT

ID = log2(d/w = 1.0)

IAT 334

what about 2d
What about 2D
  • h x w rect:one way is ID = log2(d/min(h, w) + 1)
    • Should take into account direction of approach

IAT 334

design implications
Design implications
  • Menu item size
  • Icon size
  • Put frequenlty used icons together
  • Scroll bar target size and placement
    • Up / down scroll arrows together or at top and bottom of scroll bar

IAT 334

slide16
GOMS
  • One of the most widely known
  • Assumptions
    • Know sequence of operations for a task
    • Expert will be carrying them out
  • Goals, Operators, Methods, Selection Rules

IAT 334

goms procedure
GOMS Procedure
  • Walk through sequence of steps
  • Assign each an approximate time duration

-> Know overall performance time

  • (Can be tedious)

IAT 334

limitations
Limitations
  • GOMS is not for
    • Tasks where steps are not well understood
    • Inexperienced users
  • Why?
  • Good example: Move a sentence in a document to previous paragraph

IAT 334

slide19
Goal
  • End state trying to achieve
  • Then decompose into subgoals

Select sentence

Moved sentence

Cut sentence

Move to new spot

Paste sentence

Place it

IAT 334

operators
Operators
  • Basic actions available for performing a task (lowest level actions)
  • Examples: move mouse pointer, drag, press key, read dialog box, …

IAT 334

methods
Methods
  • Sequence of operators (procedures) for accomplishing a goal (may be multiple)
  • Example: Select sentence
    • Move mouse pointer to first word
    • Depress button
    • Drag to last word
    • Release

IAT 334

selection rules
Selection Rules
  • Invoked when there is a choice of a method
  • Example: Could cut sentence either by menu pulldown or by ctrl-x

IAT 334

further analysis
Further Analysis
  • 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

IAT 334

example
Example

Move Sentence

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. ...

IAT 334

keystroke level model
Keystroke-Level Model
  • Simplified GOMS
  • KSLM - developed by Card, Moran & Newell, see their book
    • The Psychology of Human-Computer Interaction, Card, Moran and Newell, Erlbaum, 1983
  • Skilled users performing routine tasks
  • Assigns times to basic human operations - experimentally verified
  • Based on MHP - Model Human Processor

IAT 334

user profiles
User Profiles
  • Attributes:
    • attitude, motivation, reading level, typing skill, education, system experience, task experience, computer literacy, frequency of use, training, color-blindness, handedness, gender,…
  • Novice, intermediate, expert

IAT 334

motivation
User

Low motivation, discretionary use

Low motivation, mandatory

High motivation, due to fear

High motivation, due to interest

Design goal

Ease of learning

Control, power

Ease of learning, robustness, control

Power, ease of use

Motivation

IAT 334

knowledge experience
Experience

task system

low low

high high

low high

high low

Design goals

Many syntactic and semantic prompts

Efficient commands, concise syntax

Semantic help facilities

Lots of syntactic prompting

Knowledge & Experience

IAT 334

job task implications
Job & Task Implications
  • Frequency of use
    • High - Ease of use
    • Low - Ease of learning & remembering
  • Task implications
    • High - Ease of use
    • Low - Ease of learning
  • System use
    • Mandatory - Ease of using
    • Discretionary - Ease of learning

IAT 334

modeling problems
Modeling Problems
  • 1. Terminology - example
    • High frequency use experts - cmd language
    • Infrequent novices - menus
  • What’s “frequent”, “novice”?

IAT 334

modeling problems contd
Modeling Problems (contd.)
  • 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

IAT 334

modeling problems contd1
Modeling Problems (contd.)
  • 3. Does not involve user per se
    • Don’t inform designer of what user wants
  • 4. Time-consuming and lengthy

IAT 334