The procedure for the grading of students’ essays in subject-based examinations is quite challenging particularly when dealing with large number of students. Hence, several automatic essay-grading systems have been designed to alleviate the demands of manual subject grading. However, relatively few of the existing systems are able to give informative feedbacks that are based on elaborate domain...

Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays. We present a new dataset of essays written by highly proficient German native speakers that is scored using a fine-grained rubric with the goal to provide detailed feedback. Our experiments with two state-of-the-art scoring systems (a neural and a SV...

We have developed an automated Japanese essay scoring system called Jess. The system needs expert writings rather than expert raters to build the evaluation model. By detecting statistical outliers of predetermined aimed essay features compared with many professional writings for each prompt, our system can evaluate essays. The following three features are examined: (1) rhetoric — syntactic var...

This paper describes an automated scorer for assessing students’ Creative Problem-Solving (CPS) abilities via modeling the intra-structure of students’ essays describing their thoughts on solving particular problems. The automated scorer aims to grade students’ open-ended responses to an essay-question-type CPS ability test, instead of using typical Likert-type or multiple-choice questions that...

The use of Natural Language Processing (NLP) for Automated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting performance comparable to human scorers. However, AES Hindi and other low-resource languages remains unexplored. In this study, we reproduce compare state-of-the-art methods domain. We employ classical feature-based Machine Learning (ML)...