Targeting Turkish-to-English Interlingual Interference Through Context-Heavy Data-Driven Learning

Abstract

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.