This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.

1. How do People Type on Mobile Devices?
Observations from a Study with 37,000 Volunteers
Kseniia Palin, Anna Maria Feit, Sunjun Kim, Per Ola Kristensson, Antti Oulasvirta
userinterfaces.aalto.fi/typing37k/
MobileHCI 2019 @ Taipei, Taiwan

2. 2
“If you want to be fast, make use of
both your thumbs and turn on
autocorrection, even though it
might be annoying at times,” said
Feit. “And then just keep using it.”
The researchers from Aalto
University in Finland and Cambridge
University, will present their work at
the International Conference on
Human-Computer Interaction with
Mobile Devices and Services – itself
something of a typing test – in
Taipei, Taiwan, on Wednesday.
https://www.theguardian.com/science/2019/oct/02/ready-text-go-typing-speeds-mobiles-rival-keyboard-users

3. Typing on mobile device,
known facts:
3
Users control typing speed to compromise between
the accuracy and error.
Banovic et al. 2017, N=20
Typing with one-finger is slower than w/ two thumbs.
Azenkot and Zhai 2012, N=32
Average speed of about 32 WPM; 74% used two thumbs.
Buschek et al. 2018, in-the-wild study, N=30
Texted messages are short: 34 keystrokes per session.
Komninos et al. 2018, in-the-wild study, N=12

4. Intelligent Text Entry (ITE) methods
4
PredictionAutocorrection Gesture
The keyboard
automatically corrects the
errors in the inputted text.
The keyboard provides a list
of predicted words, and the
user selects one.
An entire word is inputted
at once by drawing a shape
on a keyboard.

5. Intelligent Text Entry (ITE) methods
5
PredictionAutocorrection Gesture
The keyboard
automatically correct the
error in inputted text
The keyboard provides a list
of predicted words, and the
user selects one.
An entire word is inputted
at once by drawing a shape
on a keyboard.
Open question:
How they are useful in practice?

6. Method
6

7. Online typing test
Try it: http://typingtest.aalto.fi/
● Collaboration with Typing Master Inc.
http://typingtest.com
● Period: Sep. 2018 – Jan. 2019
● Transcription task
● 15 random phrases
○ Enron mobile email (memorable set), n=400
○ Gigaword Datasets, n=1125
● Logging
○ Keystroke events
○ Browser meta-data
7

8. Performance feedback
● Details on typing performance
○ Speed (in Word per Minute)
○ Error (uncorrected)
○ The percentile among the population.
● Visible only after input their demographics:
○ Gender, age, country
○ English language fluency
○ Fingers used for typing
○ etc.
8
Try it: http://typingtest.aalto.fi/

9. Sample and demographics (after filtering)
9
N=1475N=37,370

10. Dataset and metrics
10
Over 260,000 started the test.
Over 49,000 completed the test.
We conservatively excluded 25% of participants:
● Users who did not use a mobile device
● Age <5 yo, >61 yo (> 2 SD from the mean)
● Typing speed over 200 WPM
● Uncorrected error >25%
● Long break (>5s) within inputting a sentence
⇒ The final dataset: 37,370 participants.
Words per minute
Uncorrected Error Rate
Keystroke per character
# of backspaces
ITE usage
Keystroke duration
Corrected Error Rate
Interkey interval (IKI)

11. Recognition of ITE
Per-ITE changes can be detected with a rule set
11
[A quick brpe]t = i
t = i+1 [A quick brpem]
t = i+2 [A quick brown ]
t = i+3 [A quick brown fox ]
t = i+4 [A quick brown fox j]
t = i+5 [A quick brown fox jumps ]
Autocorrection
Gesture
Prediction
Confusion matrix
False Positive = 0.7 %
False Negative = 9.1 %

12. Selected
results
12

13. Speed: Words per Minute (WPM)
13

14. Speed: Words per Minute (WPM)
14
Avg= 36.2
SD=13.2
75%ile: 44
Fastest:
85 WPM!

15. Speed: Words per Minute (WPM)
15
Higher WPM than previous
Reyal 2015, 31
Buschek 2018, 32
Avg= 36.2
SD=13.2
75%ile: 44
Fastest:
85 WPM!

16. Error rates (uncorrected)
16

17. Error rates (uncorrected)
17
Avg= 2.34%
SD=2.08
75%ile: 3.1%

18. Error rates (uncorrected)
18
● Substitution: 55.6%
● Insertion: 11.1%
● Omission: 33.3%
Avg= 2.34%
SD=2.08
75%ile: 3.1%

19. Usage vs. Age
19
20s spent the most time for
typing on mobile device.
Age group

20. Speed vs. Age
20
Age group
Teenagers are the fastest.
→ 39.6 WPM
<10 yo are slowest
→ 24.3 WPM
(* not shown in graph)
Except <10 yo, typing speed
gets slower as age increses.

21. Speed vs. Language skills
21
Language skill must be
considered when conducting a
text-entry study.
Language experience affect the
typing speed.
(if non-native English users)
Q: How often do you type in English?

22. Posture
22
Most participants (74%) use both
thumbs to type.
= Buschek et al. 2018

23. Speed vs. posture
23
Using two fingers is faster than
one-finger typing.
Azenkot 2013 Ours
Two thumbs 50.0 38.0
One thumb 36.3 29.2
One index 33.8 26
Two-thumbs typing is the fastest.
Azenkot and Zhai 2013, Buschek 2018, + ours

24. Speed vs. ITE
24
A: Autocorrection
P: Prediction
G: Gesture

25. Speed vs. ITE
25
A: Autocorrection
P: Prediction
G: Gesture
Autocorrection-only users are
faster than all the others

26. Speed vs. ITE
26
A: Autocorrection
P: Prediction
G: Gesture
Autocorrection-only users are
faster than all the others
Prediction and Gesture
are no faster than no-ITE

27. Speed vs. ITE
27
A: Autocorrection
P: Prediction
G: Gesture
Some condition is even slower than no-ITE
Autocorrection-only users are
faster than all the others
Prediction and Gesture
are no faster than no-ITE

28. Typing performances vs. ITE usage
28
A: Autocorrection
P: Prediction
G: Gesture
Pearson correlation values

29. Typing performances vs. ITE usage
29
A: Autocorrection
P: Prediction
G: Gesture
Pearson correlation values
With more
autocorrections,
the speed gets faster.

30. Typing performances vs. ITE usage
30
A: Autocorrection
P: Prediction
G: Gesture
Pearson correlation values
With more predictions,
the speed gets slower.
With more
autocorrections,
the speed gets faster.

31. Typing performances vs. ITE usage
31
A: Autocorrection
P: Prediction
G: Gesture
Pearson correlation values
With more predictions,
the speed gets slower.
With more
autocorrections,
the speed gets faster.
ITEs help slower typists
to have less mistakes.

32. Typing performances vs. ITE usage
32
A: Autocorrection
P: Prediction
G: Gesture
Pearson correlation values
With more predictions,
the speed gets slower.
With more
autocorrections,
the speed gets faster.
ITEs help slower typists
to have less mistakes.
Prediction and
gesture reduce
keystroke (KSPC)

33. Conclusion
33

34. Intelligent Text Entry (ITE) methods contribute
to mobile typing differently.
● Correlations
positive: autocorrection and speed
negative: prediction and speed
● All ITE methods help slow users to
reduce errors.
Typing on mobile device is slow and error prone.
Teenagers have the fastest typing speed.
Two-finger typing is significantly faster than one-finger typing.
Main take-aways
34
Confirmed!

35. Limitations
35
Sampling bias
● Self-selection bias
● Population bias: western, young, more technology-affined group
● Low proportion of gesture-only users (1.9%)
Imprecision in web-based logging for mobile keystroke events
● Soft keyboard doesn’t transfer touch events to keystroke events as-is.
○ Usually, a set of key-down & key-up events are sent together when touch-up occurs.
● The usage of ITEs were inferred from input text, not directly from the keyboard.

36. Data
● Raw data (274k participants, 1M sentences, 79M input events).
● Processed data (37k participants, 564k sentences, 27M input events).
Code
● Implementation of the online typing test.
Analysis
● SQL and python scripts used for analyzing and visualizing the data.
● Statistic analysis results.
Public release: The full dataset
36
userinterfaces.aalto.fi/typing37k/

37. 37
How do People Type on Mobile Devices?
Observations from a Study with 37,000 Volunteers
Kseniia Palin, Anna Maria Feit, Sunjun Kim, Per Ola Kristensson, Antti Oulasvirta
New observations
● The first large-scale study with the ITEs.
● Correlations between ITEs and typing speed.
○ Autocorrection: positive.
○ Prediction: negative.
● Novice users get benefits from ITEs for
producing less errors.
Dataset contribution
● 27 million keystrokes from
37k participants.
● Code and analysis scripts
● WPM, error rate, etc.
● All unfiltered raw data from
260k participants.
userinterfaces.aalto.fi/typing37k/