We report on typing behaviour and performance of 168,000 volunteers in an online study. The large dataset allows detailed statistical analyses of keystroking patterns, linking them to typing performance. Besides reporting distributions and confirming some earlier findings, we report two new findings. First, letter pairs typed by different hands or fingers are more predictive of typing speed than, for example, letter repetitions. Second, rollover-typing, wherein the next key is pressed before the previous one is released, is surprisingly prevalent. Notwithstanding considerable variation in typing patterns, unsupervised clustering using normalised inter-key intervals reveals that most users can be divided into eight groups of typists that differ in performance, accuracy, hand and finger usage, and rollover. The code and dataset are released for scientific use.

Figure 1. This figure illustrates our implemented selection-based text entry candidates for VR. From left to right: Head Pointing (HP, red), Controller Pointing (CP, yellow), Controller Tapping (CT, blue), Freehand (FH, green), Discrete (DC, orange) and Continuous Cursor (CC, light blue).

Figure 1. Four users showing different typing behaviours involving different numbers of fingers and movement strategies. This paper reports typing rates, gaze and movement strategies for everyday typists, including both professionally trained and self-taught typists. We explain how untrained typists are able to type at very high rates, which were previously attributed only to the touch typing system that enforces the use of all 10 fingers.

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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. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI.