GOMS Analysis & Automating Usability Assessment

Melody Y. Ivory

SIMS 213, UI Design & DevelopmentMarch 19, 2002

Why Automated Usability Assessment Methods?

GOMS Analysis Outline

GOMS at a glanceModel Human ProcessorOriginal GOMS (CMN-GOMS)Variants of GOMSGOMS in practiceSummary

GOMS at a glance

Proposed by Card, Moran & Newell in 1983– apply psychology to CS

• employ user model (MHP) to predict performance of tasks in UI– task completion time, short-term memory requirements

– applicable to • user interface design and evaluation• training and documentation

– example of• automating usability assessment

Model Human Processor (MHP)

Card, Moran & Newell (1983)– most influential model of user

interaction • used in GOMS analysis

– 3 interacting subsystems• cognitive, perceptual & motor• each with processor & memory

– described by parameters» e.g., capacity, cycle time

• serial & parallel processing

Adapted from slide by Dan Glaser

MHP (continued)

Card, Moran & Newell (1983)– principles of operation

• subsystem behavior under certain conditions

– e.g., Fitts’s Law, Power Law of Practice

• ten principles

MHP Subsystems

Perceptual processor– sensory input (audio & visual)– code info symbolically– output into audio & visual image

storage (WM buffers)

MHP Subsystems

Cognitive processor– input from sensory buffers– access LTM to determine

response • previously stored info

– output response into WM

MHP Subsystems

Motor processor– input response from WM– carry out response

MHP Subsystem Interactions

Input/outputProcessing– serial action

• pressing key in response to light– parallel perception

• driving, reading signs & hearing

MHP Parameters

Based on empirical data– word processing in the ‘70s

Processors have – cycle time ()

Memories have – storage capacity ()– decay time of an item ()– info code type ()

• physical, acoustic, visual & semantic

Perceptual Subsystem Parameters

Processor– cycle time () = 100 msec

Visual Image Store– storage capacity () = 17 letters– decay time of an item () = 200

msec– info code type () = physical

• physical properties of visual stimulus

– e.g., intensity, color, curvature, length

Auditory Image Store– similar parameters

VIS = 17 [7-17] letters

VIS = 200 [70-1000] msecVIS = Physical

p = 100 [50-200]msec

One Principle of Operation

Power Law of Practice– task time on the nth trial follows a power law

• Tn = T1 n-a, where a = .4• i.e., you get faster the more times you do it!• applies to skilled behavior (perceptual & motor)• does not apply to knowledge acquisition or quality

Original GOMS (CMN-GOMS)

Card, Moran & Newell (1983)Engineering model of user interaction– task analysis (“how to” knowledge)

• Goals - user’s intentions (tasks)– e.g., delete a file, edit text, assist a customer

• Operators - actions to complete task– cognitive, perceptual & motor (MHP)– low-level (e.g., move the mouse to menu)

CMN-GOMS

Engineering model of user interaction (continued)– task analysis (“how to” knowledge)

• Methods - sequences of actions (operators)– based on error-free expert– may be multiple methods for accomplishing same goal

» e.g., shortcut key or menu selection• Selections - rules for choosing appropriate method

– method predicted based on context

– explicit task structure• hierarchy of goals & sub-goals

Text-Editing Example (CMN-GOMS)

CMN-GOMS Analysis

Analysis of explicit task structure– add parameters for operators

• approximations (MHP) or empirical data• single value or parameterized estimate

– predict user performance• execution time (count statements in task structure)• short-term memory requirements (stacking depth of task structure)

– benefits• apply before implementation (comparing alternative designs)• apply before usability testing (reduce costs)

Limitations of CMN-GOMS

No directions for task analysis– granularity (start & stop)

Serial instead of parallel perceptual processing– contrary to MHP

Only one active goalError-free expert performance– no problem solving or evaluation

• Norman’s Human Action Cycle

Norman’s Human Action Cycle (1988)

Evaluation of interpretations

Interpreting the perception

Perceiving the state of the world

The World

Intention to act

Sequence of actions

Execution of sequence of actions

GOMS

Variants of GOMS

Keystroke-Level Model (KLM)– simpler than CMN-GOMS

• six keystroke-level primitive operators– K - press a key or button– P - point with a mouse– H - home hands– D - draw a line segment– M - mentally prepare to do an action– R - system response time

• no selections• five heuristic rules (mental operators)

– still one goal activation

Text-Editing Example (KLM)

Variants of GOMS

Natural GOMS Language (NGOMSL)– more rigorous than CMN-GOMS

• uses cognitive complexity theory (CCT)– user and system models

» mapping between user’s goals & system model– user style rules (novice support)

• task-analysis methodology• learning time predictions• flatten CMN-GOMS goal hierarchy

– high-level notation (proceduralized actions) v.s. low-level operators

– still one goal activation

Text-Editing Example (NGOMSL)

Variants of GOMS

Cognitive-Perceptual-Motor GOMS (CPM-GOMS)– activation of several goals

• uses schedule chart (PERT chart) to represent operators & dependencies

• critical path method for predictions– no selections

Text-Editing Example (CPM-GOMS)

GOMS in Practice

Mouse-driven text editor (KLM)CAD system (KLM)Television control system (NGOMSL)Minimalist documentation (NGOMSL)Telephone assistance operator workstation (CMP-GOMS)– saved about $2 million a year

Activity

GOMS analysis of using a search engine– Search for “free food”, explore 2 retrieved pages and

find what you are looking for

Summary

GOMS in general– “The analysis of knowledge of how to do a task in terms of the components of goals, operators,

methods & selection rules.” (John & Kieras 94)

• CMN-GOMS, KLM, NGOMSL, CPM-GOMS

Analysis entails• task-analysis• parameterization of operators• predictions

– execution time, learning time (NGOMSL), short-term memory requirements

Application to other types of interfaces (e.g., Web or information retrieval)– Limitations?

Automating Usability Assessment Outline

Automated Usability Assessment?Characterizing Automated MethodsAutomated Assessment MethodsSummary

Automated Usability Assessment?

What does it mean to automate assessment?How could this be done?What does it require?

Characterizing Automated Methods: Method Classes

Testing– an evaluator observes users interacting with an interface (i.e.,

completing tasks) to determine usability problems

Inspection– an evaluator uses a set of criteria or heuristics to identify

potential usability problems in an interface

Inquiry– users provide feedback on an interface via interviews,

surveys, etc.

Characterizing Automated Methods: Method Classes

Analytical Modeling– an evaluator employs user and interface models to generate

usability predictions– GOMS is one example

Simulation– an evaluator employs user and interface models to mimic a

user interacting with an interface and report the results of this interaction (e.g., simulated activities, errors and other quantitative measures)

Characterizing Automated Methods: Automation Types

None– no level of automation supported (i.e.,evaluator performs all

aspects of the evaluation method)Capture– software automatically records usability data (e.g., logging

interface usage)Analysis– software automatically identifies potential usability problems

Critique– software automates analysis and suggests improvements

Characterizing Automated Methods: Effort Levels

Minimal Effort– does not require interface usage or modeling

Model Development (M)– requires the evaluator to develop a UI model and/or a user model

in order to employ the methodInformal Use (I)– requires completion of freely chosen tasks (i.e., unconstrained use

by a user or evaluator)Formal Use (F)– requires completion of specially selected tasks (i.e., constrained

use by a user or evaluator)

Automated Assessment Methods

Automated Assessment Methods:Generating Usage Data

Simulation – Automated Capture– Mimic user and record activities for subsequent analysis

Genetic Algorithm Modeling– Script interacts with running interface (Motif-based UI)– Deviation points in script behavior determined by genetic

algorithm• Mimic novice user learning by exploration

– Inexpensively generate a large number of usage traces• Find weak spots, failures, usability problems, etc.

– Requires manual evaluation of trace execution

Automated Assessment Methods:Generating Usage Data

Information Scent Modeling (Bloodhound, CoLiDeS)– Mimic users navigating a Web site and record paths

• Web site model – linking structure, usage data, and content similarity

• Considers information scent (common keywords between user goals and link text) in choosing links

– Percentage of agents follow higher- and lower-scent links• Does not consider impact of page elements, such as images,

reading complexity, etc.• Stopping criteria

– Reach target pages or some threshold (e.g., maximum navigation time)

– Requires manual evaluation of navigation paths• Log file visualization tool (Dome-Tree Visualization)

CoLiDeS

Automated Assessment Methods:Detecting Guideline Conformance

Inspection – Automated Analysis– Cannot automatically detect conformance for all guidelines– One study [Farenc et al.99]: 78% best case, 44% worst case

Quantitative Screen Measures– Size of screen elements, alignment, balance, etc.– Possibly generate initial layouts (AIDE)

Interface Consistency (Sherlock)– Same widget placement and terminology (Visual Basic UIs)– Studies showed 10-25% speedup for consistent UIs

Automated Assessment Methods:Detecting Guideline Conformance

Quantitative Web Measures– Words, links, graphics, page breadth & depth,

etc. (Rating Game, HyperAT, WebTango)– Most techniques not empirically-validated

• WebTANGO uses empirical data & expert ratings to develop prediction models

HTML Analysis (WebSAT)– All images have alt tags, one outgoing

link/page, download speed, etc.

Automated Assessment Methods:Detecting Guideline Conformance

Web Scanning Path (Design Advisor)– Determine how users will

scan a page based on attentional effects of elements

• motion, size, images, color, text style, and position

– Derived from studies of multimedia presentations vs. Web designs

Automated Assessment Methods:Suggesting Improvements

Inspection – Automated CritiqueRule-based critique systems– Typically done within a user interface management system

• Very limited application– X Window UIs (KRI/AG), control systems (SYNOP), space systems (CHIMES)

Object-based critique systems (Ergoval & WebEval)– Apply guidelines relevant to each graphical object– Widely applicable to Windows UIs

HTML Critique (Bobby, Lift)– Syntax, validation, accessibility (Bobby), and others– Embed into popular authoring tool (Lift & Macromedia)– Although useful, not empirically validated

Automated Assessment Methods:Modeling User Performance

Analytical Modeling – Automated Analysis– Predict user behavior, mainly execution time– No methods for Web interfaces

GOMS Analysis (previously discussed)– Generate predictions for GOMS models (CATHCI,

QGOMS)– Generate model and predictions (USAGE, CRITIQUE)

• UIs developed within user interface development environment

Automated Assessment Methods:Modeling User Performance

Cognitive Task Analysis– Input interface parameters to an underlying theoretical model (expert

system)• Do not construct new model for each task

– Generate predictions based on parameters as well as theoretical basis for predictions

– Similar to cognitive walkthrough (supportive evaluation)

Programmable User Models– Cross between GOMS and CTA analyses– Program UI on a psychologically-constrained architecture

• Constraint violations suggest usability problems• Generate quantitative predictions

Automated Assessment Methods:Simulating User Behavior

Simulation – Automated AnalysisPetri Net Modeling (AMME)– Construct petri net from logged interface usage– Simulates problem solving process (learning, decisions, and

task completion)– Outputs measure of behavior complexity

Information Processor Modeling (ACT-R, SOAR, CCT,…)– Methods employ sophisticated cognitive architecture with

varying features• Modeled tasks and components, predictions, etc.

Automated Assessment Methods:Simulating User Behavior

Web Site Navigation (WebCriteria)– Claimed to be similar to GOMS Analysis

• Constructs model of site and predicts navigation time for a specified path– Based on idealized Web user (Max)– Navigation time only for shortest path between endpoints

» Does not consider impact of page elements (e.g., colors,reading complexity, etc.)

– Reports on page freshness and composition of pages (text, image, applets, etc.)

– Supports only a small fraction of analysis possible with guideline review approaches

• Pirolli Critique,March 2000 issue of Internetworking– Used to compare sites (Industry Benchmarks)

Activity

Brainstorm about other ways to automate usability assessment– What about new technology?

Summary

Characterizing Automated Methods– Method Classes, Automation Types, Effort Levels

Automated Methods– Mainly automated capture and analysis– Guideline review enables automated critique– Represented only 33% of 132 surveyed approaches– Most require formal or informal interface usage

More Information– webtango.berkeley.edu– Survey paper on automated methods– Papers on quantitative Web page analysis