Enriching Automated Essay Scoring Using Discourse Marking

Electronic Essay Rater (e-rater) is a prototype automated essay scoring system built at Educational Testing Service (ETS) that uses discourse marking, in addition to syntactic information and topical content vector analyses to automatically assign essay scores. This paper gives a general description ore-rater as a whole, but its emphasis is on the importance of discourse marking and argument partitioning for annotating the argument structure of an essay. We show comparisons between two content vector analysis programs used to predict s c o r e s . EsscQ/'Content and ArgContent. EsscnContent assigns scores to essays by using a standard cosine correlation that treats the essay like a "'bag of words." in that it does not consider word order. Ark, Content employs a novel content vector analysis approach for score assignment based on the individual arguments in an essay. The average agreement between ArgContent scores and human rater scores is 82%. as compared to 69% agreement between EssavContent and the human raters. These results suggest that discourse marking enriches e-rater's scoring capability. When e-rater uses its whole set of predictive features, agreement with human rater scores ranges from 87°,/o 94% across the 15 sets of essa5 responses used in this study