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Godwin-Jones, Robert – Language Learning & Technology, 2022
In recent years, advances in artificial intelligence (AI) have led to significantly improved, or in some cases, completely new digital tools for writing. Systems for writing assessment and assistance based on automated writing evaluation (AWE) have been available for some time. That is the case for machine translation as well. More recent are…
Descriptors: Writing Instruction, Artificial Intelligence, Feedback (Response), Writing Evaluation
Dongkawang Shin; Yuah V. Chon – Language Learning & Technology, 2023
Considering noticeable improvements in the accuracy of Google Translate recently, the aim of this study was to examine second language (L2) learners' ability to use post-editing (PE) strategies when applying AI tools such as the neural machine translator (MT) to solve their lexical and grammatical problems during L2 writing. This study examined 57…
Descriptors: Second Language Learning, Second Language Instruction, Translation, Computer Software
Zhang, Hong; Torres-Hostench, Olga – Language Learning & Technology, 2022
The main purpose of this study is to evaluate the effectiveness of Machine Translation Post-Editing (MTPE) training for FL students. Our hypothesis was that with specific MTPE training, students will able to detect and correct machine translation mistakes in their FL. Training materials were developed to detect six typical mistakes from Machine…
Descriptors: Computational Linguistics, Translation, Second Language Learning, Second Language Instruction
Wu, Yi-ju – Language Learning & Technology, 2021
Adopting the approaches of "pattern hunting" and "pattern refining" (Kennedy & Miceli, 2001, 2010, 2017), this study investigates how seven freshman English students from Taiwan used the Corpus of Contemporary American English to discover collocation patterns for 30 near-synonymous change-of-state verbs and new ideas about…
Descriptors: Phrase Structure, Teaching Methods, Second Language Learning, Second Language Instruction
Yoon, Hyunsook; Jo, Jung Won – Language Learning & Technology, 2014
Studies on students' use of corpora in L2 writing have demonstrated the benefits of corpora not only as a linguistic resource to improve their writing abilities but also as a cognitive tool to develop their learning skills and strategies. Most of the corpus studies, however, adopted either direct use or indirect use of corpora by students, without…
Descriptors: Error Correction, English (Second Language), Foreign Countries, Case Studies
Lawley, Jim – Language Learning & Technology, 2015
This paper describes the development of web-based software at a university in Spain to help students of EFL self-correct their free-form writing. The software makes use of an eighty-million-word corpus of English known to be correct as a normative corpus for error correction purposes. It was discovered that bigrams (two-word combinations of words)…
Descriptors: Computer Software, Second Language Learning, English (Second Language), Error Correction
Cowan, Ron; Choo, Jinhee; Lee, Gabseon Sunny – Language Learning & Technology, 2014
This study illustrates how a synergy of two technologies--Intelligent Computer-Assisted Language Learning (ICALL) and corpus linguistic analysis--can produce a lasting improvement in L2 learners' ability to edit persistent grammatical errors from their writing. A large written English corpus produced by Korean undergraduate and graduate students…
Descriptors: Computational Linguistics, Computer Assisted Instruction, Second Language Instruction, Second Language Learning
Rimrott, Anne; Heift, Trude – Language Learning & Technology, 2008
This study investigates the performance of a spell checker designed for native writers on misspellings made by second language (L2) learners. It addresses two research questions: 1) What is the correction rate of a generic spell checker for L2 misspellings? 2) What factors influence the correction rate of a generic spell checker for L2…
Descriptors: Word Processing, German, Spelling, Second Language Learning