Benefits of Modularity in an Automated Essay Scoring System

E-rater is an operational automated essay scoring application that combines several NLP tools for the purpose of identifying linguistic features in essay responses to assess the quality of the text. The application currently identifies a variety of syntactic, discourse, and topical analysis features. We have maintained two clear visions of e-rater’s development. First, new linguistically-based features would be added to strengthen connections between human scoring guide criteria and e-rater scores. Secondly, e-rater would be adapted to automatically provide explanatory feedback about writing quality. This paper provides two examples of the flexibility of e-rater’s modular architecture for continued application development toward these goals. Specifically, we discuss a) how additional features from rhetorical parse trees were integrated into erater, and b) how the salience of automatically generated discourse-based essay summaries was evaluated for use as instructional feedback through the reuse of e-rater’s topical analysis module.