A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring).A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity.Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used.Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

We present an intelligent tutoring system called REAP that provides reader-specific lexical practice for improved reading comprehension. REAP offers individualized practice to students by presenting authentic and appropriate reading materials selected automatically from the web. We encountered a number of challenges that must be met in order for the system to be effective in a classroom setting. These include general challenges for a system that uses authentic materials, as well as more specific challenges that arise from integrating the system with pre-existing classroom curricula. We discuss how these challenges were met, and present evidence that REAP has gained acceptance into the classroom at the English Language Institute at the University of Pittsburgh. 1. System Description