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Computer-assisted detection of 90% of EFL student errors
- Source :
- Computer Assisted Language Learning. 31:144-156
- Publication Year :
- 2017
- Publisher :
- Informa UK Limited, 2017.
-
Abstract
- Software can facilitate English as a Foreign Language (EFL) students’ self-correction of their free-form writing by detecting errors; this article examines the proportion of errors which software can detect. A corpus of 13,644 words of written English was created, comprising 90 compositions written by Spanish-speaking students at levels A2-B2 (inclusive) of the Common European Framework. A total of 1,310 language errors were detected by the researcher. It was found that approximately 21% of these errors were spelling errors. A further 58% were characterised as either two-word phrases (45%), three-word phrases (9%), or four- and five-word phrases (4%) which are either absent from or rare in a large corpus of English which is known to be correct. The nature of software which can detect such words and phrases and bring them to students’ attention with a view to self-correction is briefly described. Of the remaining 21% of errors not detected by such software, most were found to be either errors of te...
- Subjects :
- 060201 languages & linguistics
Linguistics and Language
business.industry
Computer science
Teaching method
05 social sciences
Phrase structure rules
050301 education
Computer-Assisted Instruction
06 humanities and the arts
computer.software_genre
Language and Linguistics
Spelling
Linguistics
Computer Science Applications
Software
Morpheme
0602 languages and literature
Artificial intelligence
Computational linguistics
Error detection and correction
business
0503 education
computer
Natural language processing
Subjects
Details
- ISSN :
- 17443210 and 09588221
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- Computer Assisted Language Learning
- Accession number :
- edsair.doi...........293970ce4594453555bcf98e661d34c1
- Full Text :
- https://doi.org/10.1080/09588221.2017.1392322