Targeting Turkish-to-English Interlingual Interference Through Context-Heavy Data-Driven Learning
| Autor: | Mehmet Ali Yavuz, Keith John Lay |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Grammar Computer science Turkish General Arts and Humanities media_common.quotation_subject 05 social sciences 050301 education General Social Sciences Context (language use) lcsh:History of scholarship and learning. The humanities Linguistics language.human_language Interlanguage lcsh:Social Sciences lcsh:H Language transfer Transfer of training lcsh:AZ20-999 language 0501 psychology and cognitive sciences Computational linguistics 0503 education Data-driven learning media_common |
| Zdroj: | SAGE Open, Vol 10 (2020) |
| ISSN: | 2158-2440 |
| Popis: | This study investigates the effect of grammar-focused hands-on in-class data-driven learning (DDL) with a heavily contextualized corpus on the frequency of written errors attributable to common interlingual interference issues in low–intermediate Turkish learners ( n = 30) of English. Items representing the most common Turkish-to-English interlingual errors were selected through a two-step process involving the analysis of past studies and a subsequent ranking survey of teachers ( n = 10) of Turkish learners of English. Participants’ grammar development in terms of types of written errors was measured over a ten-week period through written tasks in a pre/posttest design, producing 19,328 words for analysis. The results, although variable by item, suggest that targeted DDL with the TED Corpus Search Engine (TCSE) helps reduce written errors in Turkish learners of English to a significant degree with a moderate effect size. Consequently, the investigation of DDL with the TCSE for the targeting of interlingual interference in other first-language contexts is recommended. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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