Writing is an iterative process, yet traditional assessments often prioritize the final product over the transformations that shape it. This study investigates the complex nature of revision in student writing through computational modeling, leveraging keystroke data to capture and analyze revision behaviors. Focusing on a dataset of 1,975 annotated revision events from 10th-grade French students, we assess multiple methodological approaches, including rule-based heuristics, machine learning classifiers, and large language models (LLMs). While previous research has demonstrated the feasibility of automated revision detection, we extend these efforts by introducing a novel framework for identifying embedded revisions, i.e. instances where a revision occurs within another. By comparing the efficacy of different computational strategies, our findings reveal key insights into how revisions unfold in real-time writing. Annotations evaluated by agreement measures underline the complexity of the task. This work not only enhances the precision of automated revision classification but also lays the groundwork for intelligent writing support systems that provide targeted feedback to students, fostering a deeper engagement with the revision process.