In this paper, we extracted content-based and structure-based features of text to predict human annotations for claims and nonclaims in argumentative essays. We compared Logistic Regression, Bernoulli Naive Bayes, Gaussian Naive Bayes, Linear Support Vector Classification, Random Forest, and Neural Networks to train classification models. Random Forest and Neural Network classifiers yielded the most balanced identifications of claims and non-claims based on the evaluation of accuracy, precision, and recall. The Random Forest model was then used to calculate the number, percentage, and positionality of claims and non-claims in a validation corpus that included human ratings of writing quality. Correlational and regression analyses indicated that the number of claims and the average position of non-claims in text were significant indicators of essay quality in the expected direction. [This paper was published in: V. Cavalli-Sforza, C. Romero, A. Rafferty, & J. R. Whitehill (Eds.), "Proceedings of the 13th International Conference on Educational Data Mining (EDM)" (pp. 691-695). Virtual Conference: International Educational Data Mining Society.]