Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2 = .11. Although the variance explained was somewhat low, the linguistic features identified were consistent with the rhetorical goals of paragraph types. Avenues for bolstering this approach by considering individual writing styles and techniques are considered.

Students must be able to competently compose essays in order to succeed in school and progress into the workplace. Current intelligent tutoring systems (ITS) attempt to provide individual training that is lacking in the current educational system. To provide efficient individual training through ITS, the systems must be able to effectively assess writing input from students. Necessary components for computer-based writing tutors are algorithms that mimic human judgments of writing. The current study attempts to establish a connection between paragraph position and human ratings of paragraph type through the use of computational measures provided by Coh-Metrix. We find that expert raters do not easily identify paragraph type and ratings of paragraph type do not map onto paragraph position.