Presentation Inputlog 6.0: Eye tracking. Defining reading during writing. Reference: De Smet, M., Leijten, M., & Van Waes, L. (2014). Inputlog 6.0: Eye tracking. Defining reading during writing. Paper presented at the Keystroke logging training school, Antwerp. 15/04/2014
Reading during writing
Defining reading during writing
Combining keystroke logging and eye tracking
 Reading the emerging text on the screen
 During pauses and production
 Different purposes
 Combine keystroke logging with eye tracking
 Explore and interpret characteristics and the function of
reading during writing
 Define a pattern of reading during writing
Milou de Smet
Mariëlle Leijten
Luuk Van Waes
Training School Antwerp - April 2014
2
Schematical overview
Fluent vs non-fluent writing
Reading during fluent and non-fluent writing
Finding a definition
 Traditional pause treshold (2000ms)
 Cognitive activities between 200ms and 2000ms
 Take personal typing skill into account
3
Hunt and Peck
4
Monitor gazer
5
6
1
15/04/2014
Set up experiment
Set up experiment
 Participants (university students)
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1.
2.
3.
4.
N = 16
Mean age = 21,9
Typing course
Monitor gazers
Used 10 fingers when typing
Dutch L1
Reading test
Descriptive writing task
Typing test
Sentence production tasks
1.
2.
3.
4.
Fluent writing
Non-fluent writing (spelling difficulty)
Non-fluent writing (revision at word level)
Non-fluent writing (revision at sentence level)
7
Set up experiment
Set up experiment
 All sentences were controlled for
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8
 Individual session of 40 minutes
 Inputlog 5
 Tobii TX300 Eyetracker (anhulab Antwerp)
Number of words;
Number of characters;
Word length (M & SD);
Grammatical characteristics;
Word frequency.
 Sentences are dictated in an audio fragment
9
Personal pause treshold
Pause treshold
300
 Based on typing test (2min)
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10
Mean interkey pause time (+1sd)
Median interkey pause time (+1sd)
Mean interkey pause time within words (+1sd)
Median interkey pause time within words (+1sd)
Task optimum (fluency test inputlog)(+1sd)
250
200
150
100
50
0
Mean interkey
11
Median interkey
Mean interkey
(ww)
Median interkey
(ww)
Task optimum
12
2
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Percentage non-fluent writing
Mean production time
In ms per sentence
7200
45
Fluent sentences
40
Non-fluent sentences
7000
35
6800
30
25
6600
20
6400
15
10
6200
5
6000
0
Mean interkey
Median
interkey
p = .001
5800
Mean interkey Median
Task optimum
(ww)
interkey (ww)
Fluent sentences
Non-fluent sentences
13
Number of fixations
14
Percentage of fixations
25
145
23
135
21
125
19
17
115
15
105
13
95
11
9
85
7
p = .050
75
Fluent sentences
p = .062
5
Non-fluent sentences
Fluent sentences
Non-fluent sentences
15
Mean fixation duration
16
Reading during writing
600
 Start from traditional reading measures
 Develop new measures
550
500
merged fixations
450
regressions
400
15
350
17
14
300
1
250
2
3
4 5
6
7
8
9
10
11
12
13
Ik pak iedere ochtend de fiets richting werk
200
150
p = .182
100
Fluent sentences
fixations
Non-fluent sentences
17
saccades
16
(average) distance X-axis
18
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Example 1
Example 2
1
Produced text:
1 2
3
4
5
6
7
8
9
10
11
13
1 2
12
Het egeltje heeft de hele winter al gesm [back] lapen
3
4
5
6 7
89
11
12
Vandaag ga ik een taart ak[back2]bakken voor vrienden
10
ca. 1000 ms
ca. 750 ms
ca. 500 ms
200 ms and less
19
20
Example 3
Example 3
Produced text:
Produced text:
De reisgids leidt ons dor d[back4 ]oor de smalle sta[back]raten
De reisgids leidt ons dor d[back4 ]oor de smalle sta[back]raten
1
484
1747
437
733 406
452
2
3
4
5
6
7
8
9
10
11
546
ca. 1000 ms
Blue = fluent written text
Orange = non-fluent written text
ca. 750 ms
ca. 500 ms
21
22
200 ms and less
Example 3
Produced text:
3
2
1 4
De reisgids leidt ons dor d[back4 ]oor de smalle sta[back]raten
23
24
4
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Example 4
Further explorations
 Not only look at individual fixations
 Are there regular patterns?
 How can we define these patterns?
15
17
14
1
2
3
4 5
6
7
8
9
10
11
12
 Data and process mining?
 Presentation by Jochen De Weerdt
13
Ik pak iedere ochtend de fiets richting werk
16
25
26
5