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Classical serial models view the process of producing a text as a chain of discrete pauses during which the next span of text is planned, and bursts of activity during which this text is output onto the page or computer screen. In contrast, parallel models assume that by default planning of the next text unit is performed in parallel with previous execution. We instantiated these two views as Bayesian mixed-effects models across six sets of keystroke data from child and adult writers composing different types of multi-sentence text. We modelled interkey intervals with a single distribution, hypothesised by the serial processing account, and with a two-distribution mixture model that is hypothesised by the parallel-processing account. We analysed intervals occuring before-sentence, before word, and within word. Model comparisons demonstrated strong evidence in favour of the parallel view across all datasets. When pausing occurred, sentence initial inter-keystroke intervals were longer than word initial pauses. This is consistent with the idea that edges of larger linguistic units are associated with higher level planning. However, we found – across populations – that interkey intervals at word and even at sentence boundaries were often too brief to plausibly represent time to plan what was written next. Our results cannot be explained by the serial processing but are in line with the parallel view of multi-sentence text composition. 

Revision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear S-notations, and the automated extraction of backspace keypresses. However, these approaches are time-intensive, vulnerable to construct, or restricted. Therefore, this article presents a computational approach to the automatic extraction of full revision events from keystroke logs, including both insertions and deletions, as well as the characters typed to replace the deleted text. Within this approach, revision candidates are first automatically extracted, which allows for a simplified manual annotation of revision events. Second, machine learning is used to automatically detect revision events. For this, 7120 revision events were manually annotated in a dataset of keystrokes obtained from 65 students conducting a writing task. The results showed that revision events could be automatically predicted with a relatively high accuracy. In addition, a case study proved that this approach could be easily applied to a new dataset. To conclude, computational approaches can be beneficial in providing automated insights into revisions in writing.