Abstract
This paper illustrates the use of learner corpus data (extracted from Cambridge Learner Corpus – CLC) to carry out an error analysis to investigate authentic learner errors and their respective frequencies in terms of types and tokens as well as contexts in which they regularly occur across four distinct proficiency levels, B1-B2; C1-C2, as defined by Common European Framework of Reference for Languages (henceforth CEFR) (Council of Europe, 2001). As a variety of learner corpora compiled by researchers become relatively accessible, it is possible to explore interlanguage errors and conduct error analysis (EA) on learner-generated texts. The necessity to cogitate over these authentic learner errors in designing foreign language learning programs and remedial teaching materials has been widely emphasized by many researchers (see e.g., Juozulynas, 1994; Mitton, 1996; Cowan, Choi, & Kim, 2003; Ndiaye & Vandeventer Faltin, 2003; Allerton et al., 2004). This study aims at conducting a corpus-based error analysis of agreement errors to reveal the related error categories between Greek and Turkish EFL learners, the distribution of agreement errors along the B1 - C2 proficiency range according to CEFR, and the distribution of agreement error types in respect of the L1 of the learners. The data analyzed in this study is extracted from the Cambridge Learner Corpus (CLC), the largest annotated test performance corpus which enables the investigation of the linguistic and rhetorical features of the learner performances in the above stated proficiency bands. The findings from this study reveal that, across B1-C2 proficiency levels and across different registers and genres, the most common agreement error categories by the frequency in which they occur are Verb Agreement (AGV), Noun Agreement (AGN), Anaphor Agreement (AGA), Determiner Agreement (AGD), Agreement Error (AG), and Quantifier Agreement (AGQ) errors. This study’s approach uses the techniques of computer corpus linguistics and follows the steps of the Error Analysis framework proposed by Corder (1971): identification, description, classification and explanation of errors.
Highlights
Being integral parts of foreign language learning continuum as systemic formations, errors characterize the linguistic competence and may help reveal clues about English as a Foreign Language (EFL) learners’ learning strategies
Error analysis is a rational course of action to find out whether the semantic content and the linguistic form of learners’ communicative performances are erroneous or deviant from the norms and to reflect upon them
It aims at detecting and analyzing the interlanguage characteristics of developmental agreement errors in Turkish EFL learners using a substantial body of written data
Summary
Being integral parts of foreign language learning continuum as systemic formations, errors characterize the linguistic competence and may help reveal clues about EFL learners’ learning strategies. Error analysis is a rational course of action to find out whether the semantic content and the linguistic form of learners’ communicative performances are erroneous or deviant from the norms and to reflect upon them. This action could be executed by examining anecdotal evidence or by classroom observation (Swan & Smith 2001; Kızıl & Kilimci, 2014b). As it encompasses a large number of examples of comprehensive range of error types, a learner corpus is a more dependable option to investigate the (relatively) naturally occurring learner errors. “descriptions of structure, reliable models of usage, how words and phrases are translated,” determining the crucial components of a syllabus, and identifying learners’ errors can all be developed and/or supported by focusing on specific genres and subgenres (Sinclair, 2001, p. xiii)
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