1. ___________________________________________________________________________________________________ SCHOOL OF INTERACTIVE ARTS + TECHNOLOGY [SIAT] | WWW.SIAT.SFU.CA User Modeling Predicting thoughts and actions GOMS

2. Feb 24, 2011IAT 3342 Agenda User modeling –Fitts Law –GOMS

3. Feb 24, 2011IAT 3343 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

4. User Modeling – 2 types Stimulus-Response –Hicks law –Practice law –Fitts 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 Feb 24, 2011IAT 3344

5. Power Law of Practice T n = T 1 n -a –T n to complete the nth trial is T 1 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 Feb 24, 2011IAT 3345

6. Power Law of Practice How to use it? –Use measured T 1 on the first trial Predict whether usability criteria will be met How many trials? –Predict how many practice iterations needed to reach usability criteria Feb 24, 2011IAT 3346

7. Hicks Law Decision time to choose among n equally likely alternatives –T = I c log 2 (n+1) –I c ~ 150 msec Feb 24, 2011IAT 3347

8. Hicks 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 Feb 24, 2011IAT 3348

9. 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 Feb 24, 2011IAT 3349

10. Fitts Experiment: 1D Feb 24, 2011IAT 33410 dw

11. 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) Feb 24, 2011IAT 33411 ID = log 2 (d/w + 1.0) bits result width (tolerance) of target distance to move

12. Fitts formula MT - Movement time MT is a linear function of ID k 1 and k 2 are experimental constants Feb 24, 2011IAT 33412 MT = k 1 + k 2 *ID MT = k 1 + k 2 *log 2 (d/w + 1.0)

13. Run empirical tests to determine k 1 and k 2 in MT = k 1 + k 2 * ID Will get different ones for different input devices and device uses Feb 24, 2011IAT 33413 MT ID = log 2 (d/w = 1.0)

14. What about 2D h x w rect: one way is ID = log 2 (d/min(h, w) + 1) –Should take into account direction of approach Feb 24, 2011IAT 33414

15. 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 Feb 24, 2011IAT 33415

16. Feb 24, 2011IAT 33416 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

17. Feb 24, 2011IAT 33417 GOMS Procedure Walk through sequence of steps Assign each an approximate time duration -> Know overall performance time (Can be tedious)

18. Feb 24, 2011IAT 33418 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

19. Feb 24, 2011IAT 33419 Goal End state trying to achieve Then decompose into subgoals Moved sentence Select sentence Cut sentence Paste sentence Move to new spot Place it

20. Feb 24, 2011IAT 33420 Operators Basic actions available for performing a task (lowest level actions) Examples: move mouse pointer, drag, press key, read dialog box, …

21. Feb 24, 2011IAT 33421 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

22. Feb 24, 2011IAT 33422 Selection Rules Invoked when there is a choice of a method Example: Could cut sentence either by menu pulldown or by ctrl-x

23. Feb 24, 2011IAT 33423 Further Analysis GOMS is often combined with a keystroke level analysis –Assigns times to different operators –Plus: Rules for adding Ms (mental preparations) in certain spots

24. Feb 24, 2011IAT 33424 Example 1. Select sentence Reach for mouseH0.40 Point to first wordP1.10 Click button downK0.60 Drag to last wordP1.20 ReleaseK0.60 3.90 secs 2. Cut sentence Press, hold ^Point to menu Press and release xorPress and hold mouse Release ^Move to cut Release 3.... Move Sentence

25. 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 Feb 24, 2011IAT 33425

26. Feb 24, 2011IAT 33426 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

27. Feb 24, 2011IAT 33427 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

28. Feb 24, 2011IAT 33428 Knowledge & Experience Experience tasksystem –lowlow –highhigh –lowhigh –highlow Design goals –Many syntactic and semantic prompts –Efficient commands, concise syntax –Semantic help facilities –Lots of syntactic prompting

29. Feb 24, 2011IAT 33429 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

30. Feb 24, 2011IAT 33430 Modeling Problems 1. Terminology - example –High frequency use experts - cmd language –Infrequent novices - menus Whats frequent, novice?

31. Feb 24, 2011IAT 33431 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

32. Feb 24, 2011IAT 33432 Modeling Problems (contd.) 3. Does not involve user per se –Dont inform designer of what user wants 4. Time-consuming and lengthy