NotesFAQContact Us
Collection
Advanced
Search Tips
Source
Computer Assisted Language…28
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing 1 to 15 of 28 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Kiliçkaya, Ferit – Computer Assisted Language Learning, 2022
Although a plethora of research has been conducted on written corrective feedback and timing of feedback in various teaching and learning contexts, there is a paucity of research on learners' preferences regarding different online written corrective feedback. Such a lacuna becomes prominent in EFL contexts, especially in grammar classes, where…
Descriptors: Preservice Teachers, Language Teachers, Electronic Learning, Written Language
Peer reviewed Peer reviewed
Direct linkDirect link
Sarré, Cédric; Grosbois, Muriel; Brudermann, Cédric – Computer Assisted Language Learning, 2021
Corrective feedback (CF) can be provided to learners in different ways (explicit or implicit, focused or unfocused) and is the subject of major controversies in second language acquisition research. As no clear consensus has been reached so far about the most effective approach to CF with a view to fostering accuracy in second language (L2)…
Descriptors: Blended Learning, Comparative Analysis, Second Language Learning, Second Language Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Bibauw, Serge; François, Thomas; Desmet, Piet – Computer Assisted Language Learning, 2019
This article presents the results of a systematic review of the literature on dialogue-based CALL, resulting in a conceptual framework for research on the matter. Applications allowing a learner to have a conversation in a foreign language with a computer have been studied from various perspectives and under different names (dialogue systems,…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Teaching Methods
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Vakili, Shokoufeh; Ebadi, Saman – Computer Assisted Language Learning, 2022
Theoretically grounded in Vygotsky's sociocultural theory of mind, Dynamic Assessment (DA) provides researchers with the opportunity to investigate different aspects of learners' developmental trajectory, including the ways they overcome their errors. As a qualitative inquiry into the nature of errors reflecting learners' development in academic…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Assisted Testing
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Lee, Sangmin-Michelle – Computer Assisted Language Learning, 2020
Although it remains controversial, machine translation (MT) has gained popularity both inside and outside of the classroom. Despite the growing number of students using MT, little is known about its use as a pedagogical tool in the EFL classroom. The present study investigated the role of MT as a CALL tool in EFL writing. Most studies on MT as a…
Descriptors: Translation, Computational Linguistics, English (Second Language), Second Language Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Ranalli, Jim – Computer Assisted Language Learning, 2018
Automated written corrective feedback (AWCF) has qualities that distinguish it from teacher-provided WCF and potentially undermine claims about its value for L2 student writers, including disparities in the amounts of useful information it provides across error types and the fact that inaccuracies in error-flagging must be anticipated. It remains…
Descriptors: Error Correction, Feedback (Response), Computer Assisted Instruction, Second Language Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Bodnar, Stephen; Cucchiarini, Catia; Penning de Vries, Bart; Strik, Helmer; van Hout, Roeland – Computer Assisted Language Learning, 2017
Although corrective feedback (CF) has received much interest in the second language acquisition literature, relatively little research has investigated the relationship between CF and learner affect in concrete practice situations. The present study investigates learners' affective states and practice behaviour in a novel context: oral grammar…
Descriptors: Computer Assisted Instruction, Teaching Methods, Feedback (Response), Second Language Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Rassaei, Ehsan – Computer Assisted Language Learning, 2017
This study investigated the effects of two modes of corrective feedback, namely, face-to-face recasts and computer-mediated recasts during video-conferencing on Iranian English as a foreign language (EFL) learners' second language (L2) development. Moreover, the accuracy of the learners' interpretations of recasts in the two modalities was…
Descriptors: English (Second Language), Second Language Learning, Indo European Languages, Native Language
Peer reviewed Peer reviewed
Direct linkDirect link
Zou, Bin; Wang, Dongshuo; Xing, Minjie – Computer Assisted Language Learning, 2016
Wikis provide users with opportunities to post and edit messages to collaborate in the language learning process. Many studies have offered findings to show positive impact of Wiki-based language learning for learners. This paper explores the effect of collaborative task in error correction for English as a Foreign Language learning in an online…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Electronic Publishing
Peer reviewed Peer reviewed
Direct linkDirect link
de Vries, Bart Penning; Cucchiarini, Catia; Bodnar, Stephen; Strik, Helmer; van Hout, Roeland – Computer Assisted Language Learning, 2015
Speaking practice is important for learners of a second language. Computer assisted language learning (CALL) systems can provide attractive opportunities for speaking practice when combined with automatic speech recognition (ASR) technology. In this paper, we present a CALL system that offers spoken practice of word order, an important aspect of…
Descriptors: Grammar, Computer Assisted Instruction, Feedback (Response), Error Correction
Peer reviewed Peer reviewed
Direct linkDirect link
Choi, Inn-Chull – Computer Assisted Language Learning, 2016
A Web-based form-focused intelligent computer-assisted language learning (ICALL) tutoring system equipped with a process-oriented corrective feedback function was developed to investigate the extent to which such a program may serve as a viable method of teaching grammar to Korean secondary and elementary students. The present study was also…
Descriptors: Instructional Effectiveness, Computer Assisted Instruction, Intelligent Tutoring Systems, English (Second Language)
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Engwall, Olov – Computer Assisted Language Learning, 2012
Pronunciation errors may be caused by several different deviations from the target, such as voicing, intonation, insertions or deletions of segments, or that the articulators are placed incorrectly. Computer-animated pronunciation teachers could potentially provide important assistance on correcting all these types of deviations, but they have an…
Descriptors: Feedback (Response), Phonetics, Pronunciation, Computer Assisted Instruction
Previous Page | Next Page »
Pages: 1  |  2