Supporting the professional development of graduate teaching assistants (GTAs) is a strategic necessity for both English studies and higher education. At many academic institutions, GTAs represent a significant proportion of instructional staff for first-year composition courses (Young and Bippus 116). These courses serve a crucial institutional mission as an academic entry point for the majority of undergraduate students and have been closely linked with student retention, graduation rates, and academic performance (Garrett, Bridgewater, and Feinstein; Holmes and Busser). Based on a recent national study, Amy Cicchino found most rhetoric and composition programs offer intensive, but condensed, GTA training programs that typically include a preservice orientation, semester-long teaching proseminar, and peer or faculty mentorship (93). Yet, time is a significant constraint—most programs take place over a single semester or academic year and end just as GTAs gain enough teaching experience and confidence to become more interested in composition theory and professional development (Obermark, Brewer, and Halasek; Reid).

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.