KLM and GOMS

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KLM and GOMS
213 User Interface Design and Development

• Professor Tapan Parikh (parikh_at_berkeley.edu)
• TA Eun Kyoung Choe (eunky_at_ischool.berkeley.edu)
• Lecture 13 - April 1st, 2008

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Todays Outline

1. Cognitive Modeling
2. Keystroke-Level Model (KLM)
3. Other GOMS Variants

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Cognitive Modeling

• Cognitive approach to HCI attempts to predict
  user performance based on a model of cognition
• Start with a model of how humans act
• Use that model to predict how humans would
  complete tasks using a particular UI
• Provided theoretical foundation of HCI in the
  1970s and 1980s

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Source Stuart Card, Lecture on Human Information
Interaction, 2007
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Cognitive Models are

• Abstract
• Quantitative
• Approximate
• Estimated from experiments
• Based on a theory of cognition

Adapted from Rob Miller
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Advantages

• Dont need to implement / prototype
• Dont need to test with real users
• Theory has explanatory power
• Provides scientific foundation for design, like
  other engineering fields

Adapted from Rob Miller
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Cognitive Theories in HCI

• KLM (Keystroke-Level Model) - Description of user
  tasks based on low-level actions (keystrokes,
  etc.)
• GOMS (Goals, Operators, Methods, Selectors) -
  Higher-level then KLM, with structure and
  hierarchy
• Fitts Law - Predicts how long it will take a
  user to select a target used for evaluating
  device input
• Model Human Processor (MHP) - Model of human
  cognition underlying each of these theories

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(No Transcript)
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Keystroke-Level Model (KLM)
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KLM Operators

• K Press a key or button
• P Point to a target on the display
• H Home hands on input device
• D Draw a line segment
• M Mentally prepare for an action
• R (system response time)

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Example Replacing a Word
Adapted from Lorin Hochstein
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KLM Analysis

• Walk through a task, listing the operators needed
  to complete it
• Use heuristics to insert M operators (for
  example, place Ms in front of all Ps that
  select a command)
• These can be different for different UI styles
• Based on estimates for each operator, calculate
  the amount of time required to complete the task

Adapted from Rob Miller
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Operator Estimates

• Keystroke determined by typing speed
• 0.28s for average typist (40 wpm), 0.08 for fast
  typist (155 wpm), 1.20s for worst typist
• Pointing determined by Fitts Law (or general
  approximation)
• T a b log (d/s 1) OR
• T 1.1s
• Drawing determined by Steering Law
• T a b (d/s)

Adapted from Rob Miller
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Operator Estimates

• Homing estimated by measurement
• T 0.36s (between keyboard and mouse)
• Mental prep estimated by measurement
• T 1.35s
• (estimated by taking the total task time,
  subtracting physical operator time, and dividing
  by the number of chunks of activity)

Adapted from Rob Miller
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Heuristics for adding Ms

• Basic idea Put an M before each step requiring
  access of a chunk from long-term memory
• Insert Ms before each K and P
• K -gt MK P -gt MP
• Delete Ms in the middle of typing a word or
  string
• MKMKMK -gt MKKK
• Delete Ms in the middle of composite actions
  (for example, point and click)
• MPMK -gt MPK

Adapted from Rob Miller
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Example Deleting a Word

• Using Shift-Click
• M
• P start of word
• K click
• M
• P end of word
• K shift
• K click
• H to keyboard
• M
• K Del
• Total 3M 2P 4K
• 7.37 sec

Using Delete M P start of word K
click H M K Del x n length of
word Total 2M P H (n1) K 4.44
0.28n sec
Adapted from Rob Miller
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Using KLM

• KLM can help evaluate UI designs, interaction
  methods and trade-offs, if needed using
  parametric analysis
• If common tasks take too long or consist of too
  many statements, can provide shortcuts

T
Del n times
Shift-click
n
Adapted from Rob Miller
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Empirical Validation of KLM
Source Card, Moran and Newell, The Keystroke
Level Model for User Performance Time with
Interactive Systems
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In-class Exercise

• Generate a KLM model for deleting a file from
  your desktop
• Estimate the time it would take using the
  provided operator times
• Compare the predicted time with the actual time

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Limitations of KLM

• Only applies to expert users doing routine
  (well-learned) tasks
• Only predicts efficiency - not error rate,
  memorizability, learnability, etc.
• Impractical for all but the simplest tasks
• Ignores
• Parallel processing
• Goal interleaving
• Mental workload (working memory limits, fatigue)
• Planning and problem-solving (how to select a
  method?)

Adapted from Rob Miller
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GOMS
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GOMS

• GOMS provides a higher-level language for task
  analysis and UI modeling
• Generates a set of quantitative and qualitative
  measures based on description of the task and
  user interface
• Provides a hierarchy of goals and methods to
  achieve them
• Different GOMS variants use different terms,
  operate at various levels of abstraction, and
  make different simplifying assumptions

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GOMS

• Goals - What the user wants to achieve can be
  broken down into subgoals
• Operators - An action performed in service of a
  goal can be perceptual, cognitive or motor acts
• Methods - Sequences of operators and subgoal
  invocations that accomplish a specified goal
• Selection Rules - Represent the users knowledge
  of which method should be applied

Source John and Kieras, The GOMS Family of User
Interface Analysis Techniques
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Example

• Goal delete text (n chars long)
• Select method 1 if n gt 10 method 2 if n
  lt 10
• Method 1 Goal highlight text delete
• Goal highlight text
• Point
• Click
• Point
• Shift
• Click
• Verify
• Click
• Method 2 Goal delete n chars

Adapted from Rob Miller
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Goals vs. Operators

• The difference between goals and operators is
  simply the level of detail chosen by the analyzer
• Goals are usually important end-user intentions
• Operators usually represent primitive user
  actions, that have a fixed execution time
  regardless of the context (or that is a constant
  function of some parameter), that can be
  estimated empirically
• As a result, operators usually stop at the
  command or keystroke level

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GOMS Variants

• KLM - Simplest version (Card, Moran, Newell 1983)
• CMN-GOMS - Original formulation includes methods
  and selection rules (Card, Moran, Newell 1983)
• NGOMSL - GOMS using natural language also can
  model memory usage and learning times (Kieras
  1988)
• CPM-GOMS - Models parallel processing by
  cognitive, perceptual and motor systems (John
  1990)

Source John and Kieras, The GOMS Family of User
Interface Analysis Techniques
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CMN-GOMS

• Provides for methods to achieve explicit goals
  and subgoals
• Selection of methods are predicted by the system
  - based on users rational decision-making
• Mental operations are modeled either as
• VERIFY operators
• Subgoal invocations
• Selection rules

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Building a CMN-GOMS Model

• Generate task description
• Pick high-level user Goal
• Write Method for accomplishing Goal (may invoke
  subgoals)
• Write Methods for subgoals (recursive)
• Stop when Operators are reached
• Evaluate description of task
• Apply results to UI
• Iterate

Adapted from Chris Long, Marti Hearst
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NGOMSL

• Structured natural language notation for
  representing GOMS models
• Can predict learning time based on the number of
  NGOMSL statements in a method (plus loading any
  additional static data in memory)
• Can be used for developing training materials,
  help modules, etc.
• Models the operation of working memory by
  including Retain and Recall statements
• More specific notation for mental operations
• Still does not address parallel processing, goal
  interleaving, fatigue, problem-solving, etc.

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Example
Source John and Kieras, The GOMS Family of User
Interface Analysis Techniques
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CPM-GOMS

• Cognitive-Preceptual-Motor or Critical-Path-Method
  
• Works at an even lower level of detail then KLM
• Primitive operators are very simple perceptual,
  cognitive or motor acts
• Explicitly models parallelism by considering
  three mental processors and storage systems that
  can work in parallel (based on Model Human
  Processor - to be discussed in next lecture)
• Execution time predicted using critical path -
  the longest path through the task based on
  cognitive limitations and information flow
  dependencies

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Critical Path
0
perceivetarget
perceivecursor
PP
100
100
CP
start eye move
start mouse move
start Shift press
verifytarget
50
50
50
50
move mouse
MPright
480
MPleft
pressShift
100
MP eye
move eye to target
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Adapted from Rob Miller
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Critical Path
0
perceivetarget
perceivecursor
PP
100
100
CP
start eye move
start mouse move
start Shift press
verifytarget
50
50
50
50
move mouse
MPright
480
MPleft
pressShift
100
MP eye
move eye to target
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Adapted from Rob Miller
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CPM-GOMS Success Story

• Phone company considering redesign of a
  workstation for telephone operators
• Reduced keystrokes needed for common tasks
• Put frequently-used keys closer to users fingers
• New design was 4 slower than old design
• 1 sec/call 3 million/year
• Keystroke-level model has no explanation
• But CPM-GOMS explained why
• Keystrokes removed were not on the critical path
• Used during slack time, while greeting customer
• A keystroke was moved from the beginning of call
  (during slack time) to later (putting it on the
  critical path)

Adapted from Rob Miller
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In-class Exercise

• Generate GOMS models (CNM-GOMS or NGOMSL) for
  deleting a file from your desktop using two
  methods
• Drag-and-drop
• Using terminal window
• Compare the two models for deciding which should
  be used, and in which contexts

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Limitations of GOMS

• Assumes extreme expert behavior
• Tasks must be goal-directed
• Does not model problem-solving, exploration, etc.
• Difficult to model behavior at this level of
  detail
• Still hard to model anything but the simplest
  tasks
• Not as easy as heuristic analysis, guidelines, or
  cognitive walkthrough
• Conclusion GOMS is an interesting theoretical
  model, but is hardly ever used in practice

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For Next Time

• We will talk about the conceptual theories
  underlying KLM and GOMS
• Fitts Law
• Model Human Processor
• Interactive Prototype 2 and Experiment Design
  due on April 15th!