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Yucel Yilmaz; Gisela Granena; Laia Canals; Aleksandra Malicka – Language Learning & Technology, 2024
The present study examines the impact of the explicitness of corrective feedback and explicit associative memory on the acquisition of -ing/-ed participial adjectives through delayed video-based corrective feedback. Fifty-two L1 Spanish learners were randomly assigned to one of three groups (implicit, explicit, or no-feedback) and performed an…
Descriptors: Foreign Countries, College Students, English (Second Language), Second Language Instruction
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Alif Silpachai; Reza Neiriz; MacKenzie Novotny; Ricardo Gutierrez-Osuna; John M. Levis; Evgeny Chukharev – Language Learning & Technology, 2024
It is unclear whether corrective feedback (CF) provided by L2 computer-assisted pronunciation training (CAPT) tools must be 100% accurate to promote an acceptable level of improvement in pronunciation. Using a web-based interface, 30 native speakers of Chinese completed a pretest, a computer-based training session to produce nine sound contrasts…
Descriptors: College Students, Foreign Students, English (Second Language), Second Language Instruction
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Elsayed Issa; Gus Hahn-Powell – Language Learning & Technology, 2025
This study investigates the effectiveness of a computer-assisted pronunciation training (CAPT) system on second language learners' acquisition of three grammatical features. It presents a CAPT system on top of a phoneme-based, fine-tuned speech recognition model, and is intended to deliver explicit, corrective feedback on the pronunciation of the…
Descriptors: Grammar, Computer Assisted Instruction, Arabic, Second Language Instruction
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Jining Han – Language Learning & Technology, 2024
The present study applied a multiple-case study design to investigate Chinese as a foreign language (CFL) students' responses to computer-mediated coded feedback and the factors that influence students' responses in an online multiple-draft Chinese writing context. Three intermediate-level students of Chinese completed four drafts for which they…
Descriptors: Computer Mediated Communication, Feedback (Response), Student Attitudes, Influences
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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
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Ranalli, Jim; Yamashita, Taichi – Language Learning & Technology, 2022
To the extent automated written corrective feedback (AWCF) tools such as Grammarly are based on sophisticated error-correction technologies, such as machine-learning techniques, they have the potential to find and correct more common L2 error types than simpler spelling and grammar checkers such as the one included in Microsoft Word (technically…
Descriptors: Error Correction, Feedback (Response), Computer Software, Second Language Learning
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Yiran Wen; Jian Li; Hongkang Xu; Hanwen Hu – Language Learning & Technology, 2023
The problem of cognitive overload is particularly pertinent in multimedia L2 classroom corrective feedback (CF), which involves rich communicative tools to help the class to notice the mismatch between the target input and learners' pronunciation. Based on multimedia design principles, this study developed a new multimodal CF model through…
Descriptors: Error Correction, Videoconferencing, Second Language Learning, Second Language Instruction
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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
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Yamashita, Taichi – Language Learning & Technology, 2021
This study investigated the effects of corrective feedback (CF) during in-class computer-mediated collaborative writing on grammatical accuracy in a new piece of individual writing. Forty-eight ESL students at an American university worked on two computer-mediated animation description tasks in pairs. The experimental group received indirect CF on…
Descriptors: Error Correction, Feedback (Response), Computer Mediated Communication, Synchronous Communication
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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
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Chen, Zhenzhen; Chen, Weichao; Jia, Jiyou; Le, Huixiao – Language Learning & Technology, 2022
Despite the growing interest in investigating the pedagogical application of Automated Writing Evaluation (AWE) systems, studies on the process of AWE-supported writing are still scant. Adopting activity theory as the framework, this qualitative study aims to examine how students incorporated AWE feedback into their writing in an English as a…
Descriptors: Writing Instruction, Writing Processes, Teaching Methods, Learning Strategies
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Gao, Jianwu; Ma, Shuang – Language Learning & Technology, 2019
This study investigated whether the effect of two forms of computer-automated metalinguistic corrective feedback in drills transferred to subsequent writing tasks. The English simple past tense, a learned structure, was selected as the target structure. Participants included 117 intermediate learners of English as a foreign language assigned to…
Descriptors: Feedback (Response), Metalinguistics, Computer Assisted Instruction, Drills (Practice)
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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
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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
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Cerezo, Luis – Language Learning & Technology, 2016
Research shows that computer-generated corrective feedback can promote second language development, but there is no consensus about which type is the most effective. The scale is tipped in favor of more explicit feedback that provides metalinguistic explanations, but counterevidence indicates that minimally explicit feedback of the…
Descriptors: Computer Assisted Instruction, Second Language Learning, Linguistic Input, Qualitative Research
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