Directory UMM :Networking Manual:computer_network_books:

(1)

Manual Text Entry: Experiments,

Models, and Systems

Poika Isokoski

Tampere Unit for Computer-Human Interaction

(2)

Writing

(3)

At School

• I did not understand why the inferior

(4)

Multiple writing technologies

• Pen and paper and computers are

(5)

Structure of the Work

I: Device Independent Text Input: Rationale and an Example

II: Comparison of two Toucpad-Based Methods for Numeric Entry

III: Evaluation of Multi-device Extension of Quikwriting

IV: Performance of Menu-augmented Soft Keyboards

VI: Combined Model for

Text Entry Rate Development

V: Model for Unistroke writing time

(6)

Paper IV: Introduction

• Performance of Menu-Augmented Soft

Keyboards

(7)

Paper IV: Model

• Simulator:

– Fitts’ law for pointing ( t = a + b log(A/W+1) ).

– Constant 160 ms for selection (McQueen et al., GI 1995).

– Given a text string the simulator computes how long entering it takes.

• _{Used the 500 phrase set published by}

MacKenzie and Soukoreff (CHI2003).

(8)

Paper IV: Model: results

• _{QWERTY: 26% faster}

0

Dvorak QWERTY OPTI II ATOMIK FITALY

W

(9)

Paper IV: Model: discussion 1

• _{Why does it seem to be faster?}

• _{Because it saves stylus travel:}

– Without menu:

(10)

Paper IV: Model: discussion 2

• Is the model realistic and reliable?

– Maybe.

– It could reflect user’s motor performance fairly accurately.

– It does not know anything about cognitive factors.

(11)

Paper IV: Experiment I: task

• Measured the motor performance.

– 12 participants entered repeating patterns:

(12)

Paper IV: Experiment I: results

• When the patterns are simple (left),

using the menu is faster

• When the patterns are more complex

(right), the difference is smaller

(13)

Paper IV: Experiment I: discussion

• _{Atomic menu selection is faster than}

pointing (on average).

• Deciding whether to point or select

takes time.

• _{It could be that in real writing situation}

the cognitive planning dominates

making using the menu slower than not using it.

__{Another experiment was needed.}

(14)

Paper IV: Experiment II: task

• _{Text transcription task:}

– The same software

– 6 participants

– 20 sessions (15 min with the menu and 15 min without it per session)

(15)

Paper IV: Experiment II: results

• Text entry rate:

(16)

Paper IV: Conclusions

• Using the menu may allow faster text

entry rates, but

– only after a lot of training (>>5 hours) – at the cost of added cognitive load

– the increase in text entry rate may not be that great (model says max 26%).

• The menu does not disturb those users

(17)

Summary

• _{The thesis contributes in four ways:}

– Device independent text entry

– Data on text entry method performance – Models of user performance

– A software architecture for text entry