This study investigates how and whether information about students' writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students' writing sessions. A corpus of prompt-based essays was collected from 126 undergraduates along with keystrokes logged during the session. Holistic scores and linguistic properties of these essays were then automatically calculated using natural language processing tools. Results indicated that keystroke indices accounted for 76% of the variance in essay quality and up to 38% of the variance in the linguistic characteristics. Overall, these results suggest that keystroke analyses can help to recover crucial information about writing, which may ultimately help to improve student models in computer-based learning environments. [This paper was published in: "Proceedings of the 9th International Conference on Educational Data Mining," p22-29.]