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Discourse Structure
Scoring System
Small Samples
System Design
Educational Testing Service
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Automated Essay Scoring for Nonnative English Speakers
Automated Essay Scoring for Nonnative English Speakers,Jill Burstein,Martin Chodorow
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Automated Essay Scoring for Nonnative English Speakers
(
Citations: 16
)
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Jill Burstein
,
Martin Chodorow
The e-rater system TM ~ is an operational automated essay scoring system, developed at
Educational Testing Service
(ETS). The average agreement between human readers, and between independent human readers and e-rater is approximately 92%. There is much interest in the larger writing community in examining the system's performance on nonnative speaker essays. This paper focuses on results of a study that show e-rater's performance on Test of Written English (TWE) essay responses written by nonnative English speakers whose
native language
is Chinese, Arabic, or Spanish. In addition, one small sample of the data is from US-born English speakers, and another is from non-US-born candidates who report that their
native language
is English. As expected, significant differences were found among the scores of the English groups and the nonnative speakers. While there were also differences between e-rater and the human readers for the various language groups, the average agreement rate was as high as operational agreement. At least four of the five features that are included in e-rater's current operational models (including discourse, topical, and syntactic features) also appear in the TWE models. This suggests that the features generalize well over a wide range of linguistic variation, as e-rater was not 1 The e-rater system TM is a trademark of Educational Testing Service. In the paper, we will refer to the e-rater system TM as e-rater. confounded by non-standard English syntactic structures or stylistic discourse structures which one might expect to be a problem for a system designed to evaluate
native speaker
writing.
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wing.comp.nus.edu.sg
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Citation Context
(8)
...
Burstein and Chodorow (1999
) evaluated essays from the Test of Written English (TWE) that were scored both by humans and by an early version of e-rater...
Brent Bridgeman
,
et al.
Comparison of Human and Machine Scoring of Essays: Differences by Gend...
...It involves grammatical error detection [5], off-topic essay detection [3], style and mechanics evaluation [1], and essay content evaluation [1,
2
]. The rest of this paper is structured as follows...
...For example, Burstein and Chodorow [
2
] have proposed a method for predicting the human score of a given essay by retrieving most similar essays in training essays where the similarity is calculated based on word frequencies...
...It is also used in the conventional topic-dependent methods [1,
2
]...
...The topic-dependent method based on TFIDF [
2
] was also implemented for comparison...
Ryo Nagata
,
et al.
A Topic-Independent Method for Automatically Scoring Essay Content Riv...
...Rater in [
11
], in which he collected essays from both native learners and nonnative learners and had these essays scored by teachers and E-Rater respectively...
Wen Zhuge
,
et al.
WordNet-Based Way to Identify Chinglish in Automated Essay Scoring Sys...
...Although there are some research efforts on the automatic scoring of essay type questions, mainly in the area of natural language understanding (
Burstein & Chodorow, 1999;
Foltz et al.,1999), the assessment of this class of questions relies on the manual intervention of the teacher for the commercial products on the market...
Salvatore Valenti
,
et al.
Computer Based Assessment Systems Evaluation via the ISO9126 Quality M...
...To recognize when the writer is avoiding unfamiliar words, we need a separate measure that is related to vocabulary size — such as the content vector analysis of the e-rater™ system (
Burstein & Chodorow, 1999
)...
Claudia Leacock
,
et al.
Automatic Assessment of Vocabulary Usage Without Negative Evidence
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Conference:
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(
Citations: 250
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, vol. 25, no. 2-3, pp. 285-307, 1998
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Citations
(16)
Comparison of Human and Machine Scoring of Essays: Differences by Gender, Ethnicity, and Country
Brent Bridgeman
,
Catherine Trapani
,
Yigal Attali
Journal:
Applied Measurement in Education - APPL MEAS EDUC
, vol. 25, no. 1, pp. 27-40, 2012
Features selection of high quality essays in automated essay scoring system
Mingtao Wang
,
Yongmei Tan
,
Chao Li
Conference:
International Conference on Electrical and Control Engineering - ICECE
, 2011
A Topic-Independent Method for Automatically Scoring Essay Content Rivaling Topic-Dependent Methods
Ryo Nagata
,
Jun'ichi Kakegawa
,
Yukiko Yabuta
Conference:
International Conference on Advanced Learning Technologies - ICALT
, pp. 88-92, 2009
WordNet-Based Way to Identify Chinglish in Automated Essay Scoring Systems
Wen Zhuge
,
Jingyu Hua
Published in 2009.
Diagnosing meaning errors in short answers to reading comprehension questions
(
Citations: 8
)
Stacey Bailey
Published in 2008.