Title :
Towards the use of semi-structured annotators for Automated Essay Grading
Author :
Hon Wai Lam ; Dillon, Tharam ; Chang, Elizebeth
Author_Institution :
Curtin Bus. Sch., Curtin Univ. of Technol., Perth, WA, Australia
Abstract :
The amount of time teachers spend grading essays has increased over the past decade, prompting the development of systems that are able to lighten the workload. Many systems have thus far used linear regression or semi-supervised methods towards this objective. This paper discusses some of the main Automated Essay Grading systems, highlighting some of their strengths and weaknesses, in addition to providing a brief overview of Text Mining and meta-data annotation techniques that could be used to facilitate the process of grading essays through an automated system.
Keywords :
data mining; text analysis; automated essay grading system; linear regression; meta-data annotation; semi-structured annotator; semi-supervised method; text mining; Artificial intelligence; Artificial neural networks; Humans; Semantics; Speech; Text mining; Training; Automated Essay Grading; Named Entity Recognition; Part-of-speech Tagging; Text Mining; meta-data annotation;
Conference_Titel :
Digital Ecosystems and Technologies (DEST), 2010 4th IEEE International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-1-4244-5551-5
DOI :
10.1109/DEST.2010.5610643
