This document discusses user models and predictive modeling techniques for interface design. It covers Fitts' law for predicting pointing and selection times based on target size and distance. The keystroke-level model is presented for estimating times of complex tasks as the sum of basic operations. Guiard's model of bimanual control and neural networks are described as ways to predict more complex interactions like menu placement. Predictive models allow mathematical performance predictions while descriptive models provide frameworks for understanding problems. Both have limitations and should be used alongside user testing.

1. User Models
Predicting a user’s behaviour

2. Fitts’ Law

3. 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

5. Trackpad
Mouse

6. Fitts’ Law
ID = log2(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

8. Which is faster on average?
Linear menu Pie / marking menu

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

10. Where are the fastest places to access?

11. Which is faster?

12. Why is this menu slow to use?

13. 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

14. Average Times (seconds)
Physical movements:
• Enter one keystroke on a standard keyboard 0.28
• Use mouse to point at an object on the screen 1.1
• Click mouse or other device 0.2
• move hand to pointing device or function key 0.4
Visual perception:
• respond to a brief light 0.1
• recognize a six letter word 0.34
• move eyes to a new location on the screen 0.23
Mental Actions
• retrieve a simple item from long-term memory 1.2
• learn a single “step” procedure 25
• execute a mental “step” 0.075
• Prepare for next step (choose a method) 1.35

15. 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

16. 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.

17. 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.

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

19. 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

20. Guiard’s Model of Bimanual
Control
From Scott Mackenzie

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

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

23. 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

24. Solution: neural network
Step 1: Training
Handedness
Side of table
Position & orientation
of input device (pen)
Neural network
Mark Hancock - 2003

25. 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 - 2003
Handedness
Side of table

26. 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.