The purpose of the report is to explore some of the mechanisms involved in the writing process. In particular, we examine students' process data (keystroke log analysis) to uncover how students approach a knowledge-telling task using 2 different task types. In the first task, students were asked to list as many words as possible related to a particular topic (word listing). In a second task, students were asked to write to a specific prompt that was designed to elicit their background knowledge of a topic using connected text (knowledge elicitation). Using a matrix incomplete block design, 1,592 high school students completed the 2 writing tasks in addition to a multiple-choice test of their background knowledge in 2 of 5 possible topics in the domain of U.S. history. An array of process data including students' typing and associated timing features was used to predict the writing scores on the 2 different types of tasks. The analyses revealed several distinct patterns that were associated with processing at the task knowledge productivity level, the editing effort level, and the keyboarding effort level. The robustness of the features was reflected in a set of hierarchical regressions that demonstrated that the process features were predictive of the writing score even when students' knowledge scores on the associated multiple-choice test were considered. In sum, the results indicate that process data in the form of log file analysis are useful for both understanding the writing process and exploring potential differences between students with high and low knowledge. [For the corresponding grantee submission, see ED586324.]