This article demonstrates how recent advances in technology allow fine-grained analyses of candidate-produced essays, thus providing a deeper insight on writing performance. We examined how essay features, automatically extracted using natural language processing and keystroke logging techniques, can predict various performance measures using data from a large-scale and high-stakes assessment for awarding high-school equivalency diploma. The features that are the most predictive of writing proficiency and broader academic success were identified and interpreted. The suggested methodology promises to be practically useful because it has the potential to point to specific writing skills that are important for improving essay writing and academic performance for educationally at-risk adult populations like the one considered in this article.