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Autor/inn/enMaimone, Luciane; Jolley, Jason
TitelLooks Like Google to Me: Instructor Ability to Detect Machine Translation in L2 Spanish Writing
QuelleIn: Foreign Language Annals, 56 (2023) 3, S.627-644 (18 Seiten)
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ZusatzinformationORCID (Maimone, Luciane)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0015-718X
DOI10.1111/flan.12690
SchlagwörterCollege Faculty; Language Teachers; Spanish; Identification; Artificial Intelligence; Translation; Second Language Learning; Writing (Composition); Accuracy; Teacher Response; Technology Integration
AbstractThis article reports the results of an empirical study designed to determine the degree to which college instructors of Spanish can distinguish between machine translation (MT) and non-MT writing samples produced by second language (L2) learners of Spanish in an intermediate-level writing course. We also investigated relationships between detection accuracy rates, instructor teaching experience, and text type (narrative or argumentative), as well as signs instructors consider indicators of both kinds of writing. Results demonstrated that instructors were able to distinguish MT from non-MT writing with a high degree of accuracy by relying on a broad array of indicators. However, neither text type difference nor instructor experience related significantly to detection ability. These findings have practical implications for the L2 classroom with regard to instructor response to MT use and the integration of MT tools to support L2 writing development. (As Provided).
AnmerkungenWiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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