User Models
description
Transcript of User Models
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 = log2(A/W + 1)
MT = a + b*ID
ID = Index of difficultyMT = movement time (to move hand to a target)A = amplitude (distance to target)W = width of target
Which is faster on average?
Linear menu Pie / 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 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
Example: Bus fare boxes
• List the steps needed to:– Pay your fare by coins– Validate an existing transfer
• Estimate how long each willtake, on average
Example: Bus Fare BoxesFare 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 BoxesFare 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
HandednessSide of tablePosition & orientation of input device (pen)
Neural network
Mark Hancock - 2003
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
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.