The e-rater system 1 , an automated essay scoring system, developed at Educational Testing Service (ETS), is currently being used to score essay responses on the Graduate Management Admissions Test (GMAT). The average agreement between human readers, and between independent human readers and e-rater is approximately 92%. There is much interest in the larger writing community in examining the sy...

Neural approaches to automated essay scoring have recently shown state-of-theart performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on content accuracy. The inputs to neural essay scoring models – ngrams and embe...

We present the first system developed for automated grading of high school essays written in Swedish. The system uses standard text quality indicators and is able to compare vocabulary and grammar to large reference corpora of blog posts and newspaper articles. The system is evaluated on a corpus of 1 702 essays, each graded independently by the student’s own teacher and also in a blind re-grad...

Research on automated essay scoring (AES) indicates that computer-generated essay ratings are comparable to human ratings. However, despite investigations into the accuracy and reliability of AES scores, less attention has been paid to the feedback delivered to the students. This paper presents a method developers can use to quickly evaluate the usability of an automated feedback system prior t...

Chinese automated essay scoring (CAES) is a very important tool for many educational researches. However, none of the methods for retrieving features in English essays is applicable to Chinese writings because Chinese grammar parsers cannot produce reliable and useful syntactic features. CAES systems must explore other distinct features in Chinese writing. This paper proposes a method for retri...