Title
Automated assessment of non-native learner essays: Investigating the role of linguistic features
Campus Units
English
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
2017
Journal or Book Title
International Journal of Artificial Intelligence in Education
Abstract
Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?" has a different answer for each dataset.
Copyright Owner
Springer
Copyright Date
2016
Language
en
File Format
application/pdf
Recommended Citation
Vajjala, Sowmya, "Automated assessment of non-native learner essays: Investigating the role of linguistic features" (2017). English Publications. 98.
https://lib.dr.iastate.edu/engl_pubs/98
Included in
Bilingual, Multilingual, and Multicultural Education Commons, Educational Assessment, Evaluation, and Research Commons, Language and Literacy Education Commons
Comments
This is a manuscript of an article accepted for publication in International Journal of Artificial Intelligence in Education.