Understanding the temporal organization of writing is key to studying writing processes. Existing methods to segment writing into phases often rely on arbitrary rules, extensive manual annotation, or focus on numerous transitions. This study aimed to develop an automated segmentation method to detect distinctive transition in the dominant writing process, particularly the transition from first draft to revision. For this, keystroke data (source-based L1 writing (N = 80) and text simplification in L2 (N = 88)) were manually annotated. The BEAST algorithm was applied for Bayesian change point detection, based on five characteristics derived from the annotation criteria: (1) percentage of the final text written so far, (2) distance between typed and remaining characters, (3) relative cursor position, (4) source use, and (5) pause timings. The first three features proved most effective in identifying change points. A rule-based approach was further applied to select one final change point, which resulted in mediocre accuracy ranging from 31% exact agreement to 49% agreement within 60 seconds. To conclude, the BEAST algorithm is useful in detecting a variety of change points in writing processes, yet connecting them to meaningful phases is still quite complex.

The study of revision has been a topic of interest in writing research over the past decades. Numerous studies have, for instance, shown that learning-to-revise is one of the key competences in writing development. Moreover, several models of revision have been developed, and a variety of taxonomies have been used to measure revision in empirical studies. Current advances in data collection and analysis have made it possible to study revision in increasingly precise detail. The present study aimed to combine previous models and current advances by providing a comprehensive product- and process-oriented tagset of revision. The presented tagset includes properties of external revisions: trigger, orientation, evaluation, action, linguistic domain, spatial location, temporal location, duration, and sequencing. We identified how keystroke logging, screen replays, and eye tracking can be used to extract both manually and automatically extract features related to these properties. As a proof of concept, we demonstrate how this tagset can be used to annotate revisions made by higher education students in various academic tasks. To conclude, we discuss how this tagset forms a scalable basis for studying revision in writing in depth.