Computer-assisted detection of 90% of EFL student errors. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Harvey-Scholes, Calum
• Is Part Of: Computer assisted language learning. Volume 31:Issue 1/2(2018)
• Page Start: 144
• Page End: 156
• Publication Date: 2018-01-02
• Subjects: Error detection -- EFL -- writing -- software -- corpus; Language and languages -- Computer-assisted instruction -- Periodicals; 418.00285
• DOI: 10.1080/09588221.2017.1392322
• Languages: English
• ISSNs: 0958-8221

Abstract

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 tense (7%), misuse of false friends (4%) or problems with determiners (3%). Again, software which can help students detect and correct such errors is outlined. The limitations and pedagogical significance of the research are then briefly discussed.