i213: User Interface Design & Development

Marti HearstTues, April 17, 2007

Today

Evaluation based on Cognitive Modeling Keystroke-Level Model

low-level description of what users must do to perform a task.

GOMS structured, multi-level description of what

users must do to perform a task Fitts’ Law

Used to predict time needed to select a target

Keystroke-level Model

Another “discount” usability methodMain idea:– Walk through the interface, counting how many

operations it would take an expert user to perform– Look for ways to optimize– Look for potential sources of error

KLM is very low-level (tiny operations)

Slide adapted from Chris Long

Keystroke-Level Model

How to make a KLM– List specific actions user does to perform task

• Keystrokes and button presses• Mouse movements• Hand movements between keyboard & mouse• System response time (if it makes user wait)

– Add Mental operators– Assign execution times to steps– Add up execution times

Only provides execution time and operator sequence

KLM ExampleReplace all instances of a 4-letter word.

(example from Hochstein)

Slide adapted from Chris Long

What is GOMS?

A family of user interface modeling techniquesGoals, Operators, Methods, and Selection rules– Higher-level than KLM– Input: detailed description of UI and task(s)– Output: various qualitative and quantitative measures

Slide adapted from Chris Long

Applications of GOMS analysis

Comparing UI designsProfilingBuilding a help system– GOMS modelling makes user tasks and goals

explicit– Can suggest questions users will ask and the

answers

Slide adapted from Chris Long

What can GOMS model?

Task must be goal-directed– Some activities are more goal-directed than others– Even creative activities contain goal-directed tasks

Task must be a routine cognitive skillCan include serial and parallel tasks

Slide adapted from Chris Long

GOMS Output

Functionality coverage and consistency– Does UI contain needed functions?– Are similar tasks performed similarly? (NGOMSL only)

Operator sequence– In what order are individual operations done?– Abstraction of operations may vary among models

Slide adapted from Chris Long

GOMS Output (cont’d)

Execution time– By expert

Error recoveryProcedure learning time (NGOMSL only)– Useful for relative comparison only– Does not include time for learning domain knowledge

Slide adapted from Chris Long

How to do (CMN-)GOMS Analysis

Generate task description– Pick high-level user Goal– Write Method for accomplishing Goal - may invoke subgoals– Write Methods for subgoals

• This is recursive• Stops when Operators are reached

Evaluate description of taskApply results to UIIterate

Slide adapted from Chris Long

Operators vs. Methods

Operator: the most primitive actionMethod: requires several Operators or subgoal invocations to accomplishLevel of detail determined by– KLM level - keypress, mouse press– Higher level - select-Close-from-File-menu– Different parts of model can be at different levels of

detail

Slide adapted from Chris Long

GOMS Example 1: PDA Text Entry

goal: enter-text-PDA– move-pen-to-text-start– goal: enter-word-PDA– ...repeat until no more words

• write-letter ...repeat until no more letters• [select: goal: correct-misrecognized-word] ...if incorrect

expansion of correct-misrecognized-word goal:– move-pen-to-incorrect-letter– write-letter

GOMS Example

Move text in a word processor– (example from Hochstein)

GOMS Example 2

Move text in a word processor(example from Hochstein)

GOMS Example 2

Move text in a word processor(example from Hochstein)

Slide adapted from Chris Long

Members of GOMS Family

Keystroke-Level Model (KLM) – – Card, Moran, Newell (1983)

CMN-GOMS – Card, Moran, Newell GOMS

Natural GOMS Language (NGOMSL) – -Kieras (1988+)

Critical Path Method or Cognitive, Perceptual, and Motor GOMS (CPM-GOMS) – John (1990+)

Slide adapted from Chris Long

Other GOMS techniques

NGOMSL– Regularized level of detail– Formal syntax, so computer interpretable– Gives learning times

CPM-GOMS– Closer to level of Model Human Processor– Much more time consuming to generate– Can model parallel activities

Slide adapted from Chris Long

Real-world Applications of GOMS

KLM– Mouse-based text editor– Mechanical CAD system

NGOMSL– TV control system– Nuclear power plant operator’s associate

CPM-GOMS– Telephone operator workstation

Slide adapted from Chris Long

Advantages of GOMS

Gives several qualitative and quantitative measuresModel explains why the results are what they areLess work (?) than usability studyEasy (?) to modify when interface is revisedResearch ongoing for tools to aid modeling process

Slide adapted from Chris Long

Disadvantages of GOMS

Not as easy as heuristic analysis, guidelines, or cognitive walkthroughOnly works for goal-directed tasksAssumes tasks are performed by expert usersEvaluator must pick users’ tasks/goalsDoes not address several important UI issues, such as– readability of text– memorability of icons, commands

Does not address social or organizational impact

Slide adapted from Chris Long

GOMS Summary

Provides info about many important UI propertiesBut does not tell you most of what you want to know about a UISubstantial effort to do initial model, but still (potentially) easier than user testingChanging later is much less work than initial generation

Slide adapted from Newstetter & Martin, Georgia Tech

Fitts’ Law

Models movement time for selection tasks

The movement time for a well-rehearsed selection task: • increases as the distance to the target increases• decreases as the size of the target increases

Slide adapted from Newstetter & Martin, Georgia Tech

Fitts’ Law

Time (in msec) = a + b log2(D/S+1)where a, b = constants (empirically derived) D = distance S = size

ID is Index of Difficulty = log2(D/S+1)

Slide adapted from Pourang Irani

Fitts’ Law

Same ID → Same Difficulty

Target 1 Target 2

Time = a + b log2(D/S+1)

Slide adapted from Pourang Irani

Fitts’ Law

Smaller ID → Easier

Target 2Target 1

Time = a + b log2(D/S+1)

Slide adapted from Pourang Irani

Fitts’ Law

Larger ID → Harder

Target 2Target 1

Time = a + b log2(D/S+1)

Slide adapted from Pourang Irani

Determining Constants for Fitts’ Law

To determine a and b– design a set of tasks with varying values for D and S (conditions)

For each task condition – multiple trials conducted and the time to execute each is

recorded and stored electronically for statistical analysis

Accuracy is also recorded– either through the x-y coordinates of selection or – through the error rate — the percentage of trials selected with the

cursor outside the target

Slide adapted from Pourang Irani

A Quiz Designed to Give You Fitts

http://www.asktog.com/columns/022DesignedToGiveFitts.html

Microsoft Toolbars offer the user the option of displaying a label below each tool. Name at least one reason why labeled tools can be accessed faster. (Assume, for this, that the user knows the tool.)

Slide adapted from Pourang Irani

A Quiz Designed to Give You Fitts

1. The label becomes part of the target. The target is therefore bigger. Bigger targets, all else being equal, can always be acccessed faster, by Fitt's Law.

2. When labels are not used, the tool icons crowd together.

Slide adapted from Pourang Irani

A Quiz Designed to Give You Fitts

You have a palette of tools in a graphics application that consists of a matrix of 16x16-pixel icons laid out as a 2x8 array that lies along the left-hand edge of the screen. Without moving the array from the left-hand side of the screen or changing the size of the icons, what steps can you take to decrease the time necessary to access the average tool?

Slide adapted from Pourang Irani

A Quiz Designed to Give You Fitts

1. Change the array to 1X16, so all the tools lie along the edge of the screen.

2. Ensure that the user can click on the very first row of pixels along the edge of the screen to select a tool. There should be no buffer zone.

Summary

We can use Cognitive Modeling to make predictions about interface usabilityComplementary to Usability StudiesIn practice:– GOMS not used often– Fitts’ law often used for determining best case for

new kinds of input methods