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User Modeling

User Modeling. Predicting thoughts and actions GOMS. Agenda. User modeling Fitt’s Law GOMS. 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.

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User Modeling

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  1. User Modeling Predicting thoughts and actions GOMS

  2. Agenda • User modeling • Fitt’s Law • GOMS IAT 334

  3. 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 IAT 334

  4. User Modeling – 2 types • Stimulus-Response • Hick’s law • Practice law • Fitt’s 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 IAT 334

  5. Power Law of Practice • Tn = T1n-a • Tn to complete the nth trial is T1 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 IAT 334

  6. Power Law of Practice • How to use it? • Use measured T1 on the first trial • Predict whether usability criteria will be met • How many trials? • Predict how many practice iterations needed to reach usability criteria IAT 334

  7. Hick’s Law • Decision time to choose among n equally likely alternatives • T = Ic log2(n+1) • Ic ~ 150 msec IAT 334

  8. Hick’s 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 IAT 334

  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 IAT 334

  10. Fitts Experiment: 1D d w IAT 334

  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) ID = log2 (d/w + 1.0) width (tolerance) of target bits result distance to move IAT 334

  12. Fitts formula • MT - Movement time • MT is a linear function of ID k1 and k2 are experimental constants MT = k1 + k2*ID MT = k1 + k2 *log2 (d/w + 1.0) IAT 334

  13. Run empirical tests to determine k1 and k2 in MT = k1 + k2* ID • Will get different ones for different input devices and device uses MT ID = log2(d/w = 1.0) IAT 334

  14. What about 2D • h x w rect:one way is ID = log2(d/min(h, w) + 1) • Should take into account direction of approach IAT 334

  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 IAT 334

  16. 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 IAT 334

  17. GOMS Procedure • Walk through sequence of steps • Assign each an approximate time duration -> Know overall performance time • (Can be tedious) IAT 334

  18. 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 IAT 334

  19. Goal • End state trying to achieve • Then decompose into subgoals Select sentence Moved sentence Cut sentence Move to new spot Paste sentence Place it IAT 334

  20. Operators • Basic actions available for performing a task (lowest level actions) • Examples: move mouse pointer, drag, press key, read dialog box, … IAT 334

  21. 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 IAT 334

  22. Selection Rules • Invoked when there is a choice of a method • Example: Could cut sentence either by menu pulldown or by ctrl-x IAT 334

  23. Further Analysis • GOMS is often combined with a keystroke level analysis • Assigns times to different operators • Plus: Rules for adding M’s (mental preparations) in certain spots IAT 334

  24. Example Move Sentence 1. Select sentence Reach for mouse H 0.40 Point to first word P 1.10 Click button down K 0.60 Drag to last word P 1.20 Release K 0.60 3.90 secs 2. Cut sentence Press, hold ^ Point to menu Press and release ‘x’ or Press and hold mouse Release ^ Move to “cut” Release 3. ... IAT 334

  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 IAT 334

  26. 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 IAT 334

  27. 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 Motivation IAT 334

  28. Experience task system low low high high low high high low Design goals Many syntactic and semantic prompts Efficient commands, concise syntax Semantic help facilities Lots of syntactic prompting Knowledge & Experience IAT 334

  29. 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 IAT 334

  30. Modeling Problems • 1. Terminology - example • High frequency use experts - cmd language • Infrequent novices - menus • What’s “frequent”, “novice”? IAT 334

  31. 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 IAT 334

  32. Modeling Problems (contd.) • 3. Does not involve user per se • Don’t inform designer of what user wants • 4. Time-consuming and lengthy IAT 334

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