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

Autor/inn/en	Lay, Keith John; Yavuz, Mehmet Ali
Titel	Targeting Turkish-to-English Interlingual Interference through Context-Heavy Data-Driven Learning
Quelle	In: SAGE Open, 10 (2020) 2, (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lay, Keith John)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2158-2440
DOI	10.1177/2158244020920596
Schlagwörter	Interference (Language); English (Second Language); Second Language Learning; Interlanguage; Turkish; Native Language; Computational Linguistics; Speeches; Error Patterns; Grammar; Item Analysis; Teacher Attitudes; Pretests Posttests; Learning Processes; Error Analysis (Language); Writing (Composition); Transfer of Training; Language Tests; Second Language Instruction; Undergraduate Students; Foreign Countries; Turkey + Suchen Sie Ihr Suchwort? English as second language; English; Second Language; Englisch als Zweitsprache; Zweitsprachenerwerb; Zielsprache; Türkisch; Linguistics; Computerlinguistik; Fehlertyp; Grammatik; Itemanalyse; Lehrerverhalten; Learning process; Lernprozess; Error analysis; Language; Fehleranalyse; Schreibübung; Training; Transfer; Ausbildung; Language test; Sprachtest; Fremdsprachenunterricht; Ausland; Türkei
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01