Phase to phase: Developing an automated procedure to identify and visualize phases in writing sessions using keystroke data

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.