@inproceedings{ad0d65412ed04e8b82dde56daaaca781,
title = "Paragraph specific n-gram approaches to automatically assessing essay quality",
abstract = "In this paper, we describe an n-gram approach to automatically assess essay quality in student writing. Underlying this approach is the development of n-gram indices that examine rhetorical, syntactic, grammatical, and cohesion features of paragraph types (introduction, body, and conclusion paragraphs) and entire essays. For this study, we developed over 300 n-gram indices and assessed their potential to predict human ratings of essay quality. A combination of these n-gram indices explained over 30% of the variance in human ratings for essays in a training and testing corpus. The findings from this study indicate the strength of using n-gram indices to automatically assess writing quality. Such indices not only explain text-based factors that influence human judgments of essay quality, but also provide new methods for automatically assessing writing quality.",
keywords = "Automatic feedback, Computational linguistics, Corpus linguistics, Essay quality, Intelligent tutoring systems",
author = "Scott Crossley and Caleb DeFore and Kris Kyle and Jianmin Dai and McNamara, {Danielle S.}",
year = "2013",
month = jan,
day = "1",
language = "English (US)",
series = "Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013",
publisher = "International Educational Data Mining Society",
editor = "D'Mello, {Sidney K.} and Calvo, {Rafael A.} and Andrew Olney",
booktitle = "Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013",
note = "6th International Conference on Educational Data Mining, EDM 2013 ; Conference date: 06-07-2013 Through 09-07-2013",
}