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Peichin Chang; Pin-Ju Chen; Li-Ling Lai – Computer Assisted Language Learning, 2024
Machine Translation (MT) tools have advanced to a level of reliability such that it is now opportune to consider their place in language teaching and learning. Given their potential, the current study sought to engage EFL university sophomores in recursive editing afforded by Google Translate (GT) for one semester, and investigated (1) whether the…
Descriptors: Editing, Computer Software, Artificial Intelligence, Translation
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
Rassaei, Ehsan – Computer Assisted Language Learning, 2023
The main purpose of the present study is to propose a framework for implementing group dynamic assessment (DA) using students' smartphones for improving and assessing EFL learners' ability to produce well-formed and appropriate requests. This study focuses on five learner reciprocity moves during DA interactions to get deeper insights into the…
Descriptors: Computer Assisted Testing, Second Language Learning, Second Language Instruction, English (Second Language)
Bolgün, M. Ali; McCaw, Tatiana – Computer Assisted Language Learning, 2019
With the ever-increasing number of available language technology products, there is also a need to evaluate them objectively. Unsubstantiated beliefs about what language technology can and cannot do inside or outside the language classroom often influence decisions about the choice of language technology to be used. The declarative/procedural…
Descriptors: Neurosciences, Second Language Learning, Second Language Instruction, Metalinguistics
Guo, Qian; Feng, Ruiling; Hua, Yuanfang – Computer Assisted Language Learning, 2022
AWCF can facilitate academic writing development, especially for novice writers of English as a foreign language (EFL). Existing AWCF studies mainly focus on teacher and learner perceptions; fewer have investigated the error-correction effect of AWCF and factors related to the effect. Especially lacking is research on how successfully students can…
Descriptors: Error Correction, Feedback (Response), English (Second Language), Second Language Learning
Harvey-Scholes, Calum – Computer Assisted Language Learning, 2018
Software can facilitate English as a Foreign Language (EFL) students' self-correction of their free-form writing by detecting errors; this article examines the proportion of errors which software can detect. A corpus of 13,644 words of written English was created, comprising 90 compositions written by Spanish-speaking students at levels A2-B2…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Error Correction
Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel – Computer Assisted Language Learning, 2016
Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of…
Descriptors: Discourse Analysis, Feedback (Response), Undergraduate Students, Accuracy
Amaral, Luiz; Meurers, Detmar; Ziai, Ramon – Computer Assisted Language Learning, 2011
Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life…
Descriptors: Feedback (Response), Second Language Learning, Intelligent Tutoring Systems, Information Management
Fischer, Robert – Computer Assisted Language Learning, 2007
This article presents a survey of computer-based tracking in CALL and the uses to which the analysis of tracking data can be put to address questions in CALL in particular and second language acquisition (SLA) in general. Adopting both quantitative and qualitative methods, researchers have found that students often use software in unexpected ways,…
Descriptors: Computer Mediated Communication, Computer Assisted Instruction, Second Language Learning, Second Language Instruction
Byrne, Timothy – Computer Assisted Language Learning, 2007
Many teachers today use Learning Management Systems (LMS), several of which are open-source. Specific examples are Claroline and Moodle. However, they are not specifically designed for language learning, and hence not entirely suitable. In this article, I will compare two uses of the Claroline LMS available at Louvain-la-Neuve within the framework…
Descriptors: Management Systems, Computer Software, Computer Assisted Instruction, Second Language Instruction

Lambacher, Stephen – Computer Assisted Language Learning, 1999
Explains the use of a computer-assisted language-learning tool that utilizes acoustic data in real time to help Japanese second-language learners improve their perception and production of English consonants. The basic features of the speech-learning software that runs on a networked workstation and is used for pronunciation training are…
Descriptors: Acoustic Phonetics, Articulation (Speech), Computer Assisted Instruction, Computer Software