GOMS Analysis & Automated Usability Assessment Melody Y. Ivory (UCB CS) SIMS 213, UI Design & Development March 8, 2001

2 GOMS Analysis Outline GOMS at a glance Model Human Processor Original GOMS (CMN-GOMS) Variants of GOMS GOMS in practice Summary

3 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

4 Model Human Processor (MHP) Card, Moran & Newell (1983) – most influential model of user interaction – 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

5 MHP Card, Moran & Newell (1983) – principles of operation subsystem behavior under certain conditions – e.g. Fitts’s Law, Power Law of Practice ten total

6 MHP Subsystems Perceptual processor – sensory input (audio & visual) – code info symbolically – output into audio & visual image storage (WM buffers)

7 MHP Subsystems Cognitive processor – input from sensory buffers – access LTM to determine response previously stored info – output response into WM

8 MHP Subsystems Motor processor – input response from WM – carry out response

9 MHP Subsystem Interactions Input/output Processing – serial action pressing key in response to light – parallel perception driving, reading signs & hearing

10 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

11 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

12 One Principle of Operation Power Law of Practice – task time on the nth trial follows a power law T n = T 1 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

13 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)

14 CMN-GOMS Engineering model of user interaction – 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

15 Text-Editing Example

16 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) – apply before user testing (reduce costs)

17 Limitations of CMN-GOMS No directions for task analysis – granularity (start & stop) Serial v.s. parallel perceptual processing – contrary to MHP Only one active goal Error-free expert performance – no problem solving or evaluation Norman’s Action Cycle

18 Norman’s Action Cycle Goals Evaluation Evaluation of interpretations Interpreting the perception Perceiving the state of the world Execution Intention to act Sequence of actions Execution of sequence of actions The World GOMS

19 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

20 Text-Editing Example (KLM)

21 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

22 Text-Editing Example (NGOMSL)

23 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

24 Text-Editing Ex. (CPM-GOMS)

25 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

26 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

27 Automated Usability Assessment Outline Automated Usability Assessment? Characterizing Automated Methods Automated Assessment Methods Summary

28 Automated Usability Assessment? What does it mean to automate assessment? How could this be done? What does it require?

29 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.

30 Characterizing Automated Methods: Method Classes Analytical Modeling – an evaluator employs user and interface models to generate usability predictions 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)

31 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

32 Characterizing Automated Methods: Effort Levels Minimal Effort – does not require interface usage or modeling Model Development – requires the evaluator to develop a UI model and/or a user model in order to employ the method Informal Use – requires completion of freely chosen tasks (i.e., unconstrained use by a user or evaluator) Formal Use – requires completion of specially selected tasks (i.e., constrained use by a user or evaluator)

33 Automated Assessment Methods

34 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

35 Automated Assessment Methods: Generating Usage Data Information Scent Modeling – 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)

36 Automated Assessment Methods: Detecting Guideline Conformance Inspection – Automated Analysis – Cannot automatically detect conformance for all guidelines – One study – 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 – Studies showed 10-25% speedup for consistent UIs

37 Automated Assessment Methods: Detecting Guideline Conformance Quantitative Web Measures – Words, links, graphics, page breadth & depth, etc. (Rating Game, HyperAT, TANGO) – Most techniques not empirically-validated Web TANGO uses expert ratings to develop prediction models HTML Analysis (WebSAT) – All images have alt tags, one outgoing link/page, etc.

38 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

39 Automated Assessment Methods: Suggesting Improvements Inspection – Automated Critique Rule-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) – Apply guidelines relevant to each graphical object – Widely applicable to Windows UIs HTML Critique – Syntax, validation, accessibility (Bobby), and others – Although useful, not empirically validated

40 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 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)

41 Automated Assessment Methods: Modeling User Performance Programmable User Models – Cross between GOMS and CTA analyses – Program UI on a psychologically-constrained architecture Constraint violations suggest usability problems Generate quantitative predictions

42 Automated Assessment Methods: Simulating User Behavior Simulation – Automated Analysis Petri 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.

43 Automated Assessment Methods: Simulating User Behavior Web Site Navigation (WebCriteria) – 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 – Used to compare sites (Industry Benchmarks)

44 Summary Characterizing Automated Methods – Method Class, 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 webtango.berkeley.edu – Survey paper on automated methods – Papers on quantitative Web page analysis