Automated Grammatical Error Detection for Language Learners

Tetreault, Joel; Leacock, Claudia

doi:10.2200/s00275ed1v01y201006hlt009

Cited by 156 publications

(160 citation statements)

References 113 publications

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“…Precision is a measure of how often the system is correct when it reports finding an error, whereas recall measures the system's coverage; that is, the proportion of actual usage errors that have been detected (Leacock, Chodorow, Gamon, & Tetreault, 2010).…”

Section: Accuracymentioning

confidence: 99%

Automated writing evaluation for formative assessment of second language writing: investigating the accuracy and usefulness of feedback as part of argument-based validation

Ranalli

Link

Chukharev‐Hudilainen

2016

Educational Psychology

102

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An increasing number of studies on the use of tools for automated writing evaluation (AWE) in writing classrooms suggest growing interest in their potential for formative assessment. As with all assessments, these applications should be validated in terms of their intended interpretations and uses. A recent argument-based validation framework outlined inferences that require backing to support integration of one AWE tool, Criterion, into a college-level English as a Second Language (ESL) writing course. The present research appraised evidence for the assumptions underlying two inferences in this argument. In the first of two studies, we assessed evidence for the evaluation inference, which includes the assumption that Criterion provides students with accurate feedback. The second study focused on the utilisation inference involving the assumption that Criterion feedback is useful for students to make decisions about revisions. Results showed accuracy varied considerably across error types, as did students' abilities to use Criterion feedback to correct written errors. The findings can inform discussion of whether and how to integrate the use of AWE into writing classrooms while raising important questions regarding standards for validation of AWE as formative assessment, Criterion developers' approach to accuracy, and instructors' assumptions about the underlying purposes of AWE-based writing activities. KeywordsAcademic writing, argument-based validation, automated writing evaluation, ESL, formative assessment AWE FOR FORMATIVE ASSESSMENT 2 AbstractAn increasing number of studies on the use of tools for automated writing evaluation (AWE) in writing classrooms suggests growing interest in their potential for formative assessment. As with all assessments, these applications should be validated in terms of their intended interpretations and uses (Kane, 2012). A recent argument-based validation framework outlined inferences that require backing to support integration of one AWE tool, Criterion, into a college-level ESL writing course. The present research appraised evidence for the assumptions underlying two inferences in this argument. In the first of two studies, we assessed evidence for the evaluation inference, which includes the assumption that Criterion provides students with accurate feedback. The second study focused on the utilization inference involving the assumption that Criterion feedback is useful for students to make decisions about revisions. Results showed accuracy varied considerably across error types, as did students' abilities to use Criterion feedback to correct written errors. The findings can inform discussion of whether and how to integrate the use of AWE into writing classrooms while raising important questions regarding standards for validation of AWE as formative assessment, Criterion developers' approach to accuracy, and instructors' assumptions about the underlying purposes of AWE-based writing activities.

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Section: Accuracymentioning

confidence: 99%

Automated writing evaluation for formative assessment of second language writing: investigating the accuracy and usefulness of feedback as part of argument-based validation

Ranalli

Link

Chukharev‐Hudilainen

2016

Educational Psychology

102

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“…Prepositions are addressed specifically because they appear often as errors in the preliminary analyses we conducted. Errors in prepositions are not unique to Arabic L2; they are rather common for non-native speakers of other languages such as English (Leacock et al, 2010;Rozovskaya and Roth, 2010).…”

Section: Correct Lexical Errorsmentioning

confidence: 99%

Proceedings of The 9th Linguistic Annotation Workshop

2015

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“…[35, p. 6] In the field of computational linguistics, most of the research on errors is concerned with students (i.e., writing novices) writing in their first or second language (for an overview, see [27])-a similarity to error analysis in writing research and language acquisition. However, the goals are different: Researchers code errors in learner corpora to improve checkers [17], to develop applications for computerassisted language learning [36], or to make syntactical parsers more robust [14].…”

Section: Research On Errorsmentioning

confidence: 99%

“…Research on robust parsing, i.e., on parsers that are able to handle ungrammatical input gracefully, is often done in the context of applications such as processing of learner language or user-generated content [27,13], and, of course, grammar checking [23,21,2]. The combination of robust and incremental parsing is mainly researched in the context of speech recognition [15,16]; Schulze [50] shows the implementation of a robust parser applying an incremental grammar formalism (LeftAssociative Grammar [20]) outside the domain of speech processing.…”

Section: Incremental Parsingmentioning

confidence: 99%

Learning from Errors: Systematic Analysis of Complex Writing Errors for Improving Writing Technology

Mahlow

2014

Language Production, Cognition, and the Lexicon

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In this paper, we describe ongoing research on writing errors with the ultimate goal to develop error-preventing editing functions in word-processors. Drawing from the state-of-the-art research in errors carried out in various fields, we propose the application of a general concept for action-slips as introduced by Norman [39]. We demonstrate the feasibility of this approach by using a large corpus of writing errors in published texts. The concept of slips considers both the process and the product: some failure in a procedure results in an error in the product, i.e., is visible in the written text. In order to develop preventing functions, we need to determine causes of such visible errors.

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Automated Grammatical Error Detection for Language Learners

Cited by 156 publications

References 113 publications

Automated writing evaluation for formative assessment of second language writing: investigating the accuracy and usefulness of feedback as part of argument-based validation

Automated writing evaluation for formative assessment of second language writing: investigating the accuracy and usefulness of feedback as part of argument-based validation

Proceedings of The 9th Linguistic Annotation Workshop

Learning from Errors: Systematic Analysis of Complex Writing Errors for Improving Writing Technology

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