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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. 

Writing quality is dependent upon the organization and sequencing of cognitive processes during writing. College students need writing-strategy advice that is tailored to their individual needs and is cognizant of their already-established writing processes. However, there is an obstacle to providing such advice: Both writing instructors and the writers lack awareness of the moment-by-moment actions by which text was produced. This is because switching between the processes of defining the task, coming up with ideas, outputting text, evaluating, and revising is largely regulated implicitly. To address this shortcoming, the present study uses a design-based research approach to develop and evaluate a minimally viable prototype of a system called “ProWrite” that uses novel biometric technology (concurrent keystroke logging and eye tracking) for providing real-time, individualized, automated, process-focused feedback to writers. This feedback is grounded in the analysis of each writer's individual needs and is presented in the context of a learning cycle consisting of an initial diagnostic, an intervention assignment, and a final follow-up. In two iterations, eight students used the system. Effects on student behavior were determined through direct analysis of biometric writing-process data before and after remediation and through changes in writing-process and written-product measures. Semi-structured interviews revealed that students generally considered the system useful, and they would try to use the newly learned strategies in their future writing experiences. The study demonstrated that individualized, real-time feedback informed by biometric technology can effectively modify writers' processes when writing takes place.