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How do people type on mobile devices? observations from a study with 37,000 volunteers / MobileHCI 2019

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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.

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How do people type on mobile devices? observations from a study with 37,000 volunteers / MobileHCI 2019

  1. 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. 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. 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. 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. 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. 6. Method 6
  7. 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. 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. 9. Sample and demographics (after filtering) 9 N=1475N=37,370
  10. 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. 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. 12. Selected results 12
  13. 13. Speed: Words per Minute (WPM) 13
  14. 14. Speed: Words per Minute (WPM) 14 Avg= 36.2 SD=13.2 75%ile: 44 Fastest: 85 WPM!
  15. 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. 16. Error rates (uncorrected) 16
  17. 17. Error rates (uncorrected) 17 Avg= 2.34% SD=2.08 75%ile: 3.1%
  18. 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. 19. Usage vs. Age 19 20s spent the most time for typing on mobile device. Age group
  20. 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. 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. 22. Posture 22 Most participants (74%) use both thumbs to type. = Buschek et al. 2018
  23. 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. 24. Speed vs. ITE 24 A: Autocorrection P: Prediction G: Gesture
  25. 25. Speed vs. ITE 25 A: Autocorrection P: Prediction G: Gesture Autocorrection-only users are faster than all the others
  26. 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. 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. 28. Typing performances vs. ITE usage 28 A: Autocorrection P: Prediction G: Gesture Pearson correlation values
  29. 29. Typing performances vs. ITE usage 29 A: Autocorrection P: Prediction G: Gesture Pearson correlation values With more autocorrections, the speed gets faster.
  30. 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. 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. 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. 33. Conclusion 33
  34. 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. 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. 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. 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/

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