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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
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
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
Rakhun Kim – Language Learning & Technology, 2024
This study investigated the instructional effects of learner uptake following automatic corrective recast from artificial intelligence (AI) chatbots on the learning of the English caused-motion construction. 69 novice-level EFL learners in a Korean high school were recruited to investigate the instructional effects of corrective recast from AI…
Descriptors: Artificial Intelligence, Error Correction, Second Language Learning, Second Language Instruction
Blazquez-Carretero, Miguel; Woore, Robert – Language Learning & Technology, 2021
Accurate spelling matters for L2 learners: It facilitates communication, affects other aspects of the writing process, and is an important assessment criterion. However, even in phonologically transparent writing systems like Spanish, L2 learners experience spelling difficulties. Nonetheless, explicit spelling instruction appears to be neglected…
Descriptors: Spelling, Second Language Learning, Spanish, Feedback (Response)
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
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
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
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
Bronson Hui; Björn Rudzewitz; Detmar Meurers – Language Learning & Technology, 2023
Interactive digital tools increasingly used for language learning can provide detailed system logs (e.g., number of attempts, responses submitted), and thereby a window into the user's learning processes. To date, SLA researchers have made little use of such data to understand the relationships between learning conditions, processes, and outcomes.…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Learning Processes
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
Kourtali, Nektaria-Efstathia – Language Learning & Technology, 2022
The role of recasts, a corrective feedback technique, has received much attention from instructed SLA researchers. While a variety of factors have been identified as influencing their effectiveness in facilitating uptake and L2 development (e.g., learners' age and level of proficiency), the role of mode of interaction has been the object of…
Descriptors: Teaching Methods, Computer Mediated Communication, Second Language Learning, Second Language Instruction
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
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