Predicting Evaluations of Essay by Computational Graph-Based Features

Abstract

How to effectively evaluate students’ essays based on a series of relatively objective writing criteria has always been a topic of discussion. With the development of automatic essay scoring, a key question is whether the writing quality can be evaluated systematically based on the scoring rubric. To address this issue, we used an innovative set of graph-based features to predict the quality of Chinese middle school students’ essays. These features are divided into four sub-dimensions: basic characteristics, main idea, essay content, and essay development. The results show that graph-based features were significantly better at predicting human essay scores than the baseline features. This indicates that graph-based features can be used to reliably and systematically evaluate the quality of an essay based on the scoring rubric, and it can be used as an alternative tool to replace or supplement human evaluation.