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Wen Liu – Language Teaching Research Quarterly, 2024
Automated writing evaluation feedback (AWE) has become popular in writing classrooms. However, few studies have conducted a comprehensive review of the employment of AWE in learning areas. This study aimed to provide a systematic review of the current research on AWE feedback, including its validity, effects, and students' engagement with AWE…
Descriptors: Writing Instruction, Learner Engagement, Feedback (Response), Teaching Methods
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Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
Shabnam Behzad – ProQuest LLC, 2024
Second language learners constitute a significant and expanding portion of the global population and there is a growing demand for tools that facilitate language learning and instruction across various levels and in different countries. The development of large language models (LLMs) has brought about a significant impact on the domains of natural…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Second Language Learning
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Huang, Ping-Yu; Tsao, Nai-Lung – Computer Assisted Language Learning, 2021
In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), Error Correction
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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
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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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Öner Bulut, Senem; Alimen, Nilüfer – Interpreter and Translator Trainer, 2023
Motivated by the urgent need to investigate the possibilities for re-positioning the human translator and his/her educator in the machine translation (MT) age, this article explores the dynamics of the human-machine dance in the translation classroom. The article discusses the results of a collaborative learning experiment which was conducted in…
Descriptors: Translation, Teaching Methods, Self Efficacy, Second Languages
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Lee, Sangmin-Michelle; Briggs, Neil – ReCALL, 2021
In recent years, marked gains in the accuracy of machine translation (MT) outputs have greatly increased its viability as a tool to support the efforts of English as a foreign language (EFL) students to write in English. This study examines error corrections made by 58 Korean university students by comparing their original L2 texts to that of MT…
Descriptors: Translation, Computational Linguistics, Second Language Learning, Second Language Instruction
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Lee, Yo-An – English Teaching, 2023
Interactional modification is important in SLA research because it involves correcting problematic L2 use. However, not all modifications will lead to pedagogical changes. Participants in conversational interactions are not always oriented to linguistic forms or functions. One way to address this dilemma is to examine the process by which…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Discourse Analysis
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Gamze Emir; Gonca Yangin-Eksi – TEFLIN Journal: A publication on the teaching and learning of English, 2023
This study investigated the effectiveness of using corpus as a data-driven learning (DDL) tool to enhance the academic writing skills of Turkish EFL learners. The study also explored learners' views of the potential use of corpus in L2 academic writing. To achieve these objectives, a mixed-method sequential explanatory design was employed,…
Descriptors: Computational Linguistics, English for Academic Purposes, 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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Caprario, Marcella; Taguchi, Naoko; Reppen, Randi – Language Learning Journal, 2022
Pragmatic markers perform important communicative functions, but they can be difficult to learn in a second language because of their multifunctionality and lack of salience during communicative events. This study has two goals: (1) to describe the communicative functions of the pragmatic marker "I mean" in academic discourse; and (2) to…
Descriptors: Teaching Methods, Computational Linguistics, Second Language Learning, Second Language Instruction
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Mirzaeian, Vahid R. – The EUROCALL Review, 2021
Although the field of machine translation has witnessed huge improvements in recent years, its potentials have not been fully exploited in other interdisciplinary areas such as foreign language teaching. The aim of this paper, therefore, is to report an experiment in which this technology was employed to teach a foreign language to a group of…
Descriptors: Translation, Computational Linguistics, Error Correction, Phrase Structure
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Gaillat, Thomas; Lafontaine, Antoine; Knefati, Anas – CALICO Journal, 2023
In this article, we focus on the design of a second language (L2) formative feedback system that provides linguistic complexity graph reports on the writings of English for special purposes students at the university level. The system is evaluated in light of formative instruction features pointed out in the literature. The significance of…
Descriptors: Language Proficiency, English (Second Language), Second Language Learning, Second Language Instruction
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Valizadeh, Mohammadreza; Soltanpour, Fatemeh – Eurasian Journal of Applied Linguistics, 2021
This experimental study, using a pretest-treatment-posttest design, compared the effects of focused direct written corrective feedback and additional writing practice on L2 learners' written syntactic complexity. The participants were 60 Iranian elementary EFL learners, whose L2 proficiency as well as L2 writing syntactic complexity and accuracy…
Descriptors: Error Correction, Feedback (Response), Teaching Methods, Second Language Learning
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