Automated Essay Scoring System Based on Rubric

In this paper, we propose an architecture of automated essay scoring system based on rubric, which combines automated scoring with human scoring. Rubrics are valid criteria for grading students' essays. Our proposed rubric has five evaluation viewpoints "Contents, Structure, Evidence, Style, and Skill" and 25 evaluation items which are subdivided viewpoints. The system is cloud-based application and consists of several tools such as Moodle, R, MeCab, and RedPen. At first, the system automatically scores 11 items included in the Style and Skill such as sentence style, syntax, usage, readability, lexical richness, and so on. Then it predicts scores of Style and Skill from these items' scores by multiple regression model. It also predicts Contents' score by the cosine similarity between topics and descriptions. Moreover, our system classifies into five grades "A+, A, B, C, D" as useful information for teachers, by using machine learning techniques such as support vector machine. We try to improve automated scoring algorithms and a variety of input essays in order to improve accuracy of classification over 90%.