User Models Predicting a user’s behaviour

Fitts’ Law

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 interfaces Explain Guiard’s model of two-handed interaction. Apply this model to evaluate two- handed interaction techniques

Trackpad Mouse

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

Which is faster on average? Linear menuPie / marking menu

Aside: marking menus Selection is even faster by using a gesture Menu doesn’t need to appear

Where are the fastest places to access?

Which is faster?

Why is this menu slow to use?

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

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

Example: Bus fare boxes List the steps needed to: –Pay your fare by coins –Validate an existing transfer Estimate how long each will take, on average

Example: Bus Fare Boxes Fare box 1: 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). Driver pushes foot pedal to drop money into box Fare Box 2: 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.

Example: Bus Fare Boxes Fare box 1: To validate an existing transfer Passenger holds up for driver to see Driver determines if time is valid Fare Box 2: To validate a transfer Passenger feeds transfer into slot. Machine reads transfer electronically and prints ok message. Machine returns transfer to user.

Expert vs. novice users Fitts’ law and the KLM model only EXPERT performance. Novice performance is much harder to model.

Predictive vs. Descriptive models Predictive – allow a mathematical prediction of performance (usually time) e.g. Fitts’ law, KLM Descriptive – A framework for thinking about a problem e.g. Guiard’s model

Guiard’s Model of Bimanual Control From Scott Mackenzie

Case studies See Mackenzie reading for case studies E.g. Text entry on mobile phones Multi-tap vs. One key + disambiguation

If you assume one-finger entry (e.g. thumb), can model this using Fitts’ law

More complex user modeling: Eg. Correctly placing menus Problem: popup menus can be inconveniently placed on a tabletop display –May be upside down for some users –May be awkward for left-hand users

Solution: neural network Step 1: Training Handedness Side of table Position & orientation of input device (pen) Neural network Mark Hancock

Solution: neural network Step 2: Predict handedness & side of table Use this to position menu correctly Position & orientation of input device (pen) Neural network Mark Hancock Handedness Side of table

Key Points Predictive models enable you to predict expert user performance at simple tasks, and consequently design interfaces that will support better performance. Predictive models have limited usefulness (only expert users & frequent operations). They should not replace user testing. Descriptive models may help you understand a process better.