2. Objectives Define predictive and descriptive models and explain why they are useful. Describe Fitts’ Law and explain its implications for interface design. Apply Fitts’ Law and other predictive models to evaluate interface designs. Explain Guiard’s model of two-handed interaction. Apply this model to evaluate two- handed interaction techniques.

3. Fitts’ Law ID = log 2 (A/W + 1) MT = a + b*ID MT = a + b*log 2 (A/W + 1) ID = Index of difficulty MT = movement time (to move hand to a target) A = amplitude (distance to target) W = width of target

7. Action Analysis Use mathematical models to predict more complex actions than pointing Example: Keystroke-Level Model List the steps required to complete an operation, and sum up average times for each step

8. Average Times (seconds) Physical movements: Enter one keystroke on a standard keyboard0.28 Use mouse to point at an object on the screen1.1 Click mouse or other device0.2 move hand to pointing device or function key0.4 Visual perception: respond to a brief light0.1 recognize a six letter word0.34 move eyes to a new location on the screen 0.23 Mental Actions retrieve a simple item from long-term memory1.2 learn a single “step” procedure25 execute a mental “step”0.075 Prepare for next step (choose a method)1.35

9. Example: Bus Fare Boxes Old fare box: –Payment by coins: Passenger tells driver how many zones. Coins drop into glass box. Driver glances to see if fare seems approx. correct. Driver tears off transfer (clip is pre-positioned so transfer will tear off with correct time shown). –To validate an existing transfer Passenger holds up for driver to see Driver determines if time is valid

10. Example: Bus Fare Boxes New Fare Box: –Payment by coins: Passenger tells driver how many zones. Driver presses button to indicate. Coins dropped into slot are counted by machine. Machine prints transfer. –To validate a transfer Passenger feeds transfer into slot. Machine reads transfer electronically and prints ok message. Machine returns transfer to user.

11. Key Points Models enable you to predict expert user performance at simple tasks, and consequently design interfaces that will support better performance. Models have limited usefulness (only expert users & frequent operations). They should not replace user testing of end products.