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Siowai Lo – Computer Assisted Language Learning, 2025
Neural Machine Translation (NMT) has gained increasing popularity among EFL learners as a CALL tool to improve vocabulary, and many learners have reported its helpfulness for vocabulary learning. However, while there has been some evidence suggesting NMT's facilitative role in improving learners' writing on the lexical level, no study has examined…
Descriptors: Translation, Computational Linguistics, Vocabulary Development, English (Second Language)
Tanjun Liu; Dana Gablasova – Computer Assisted Language Learning, 2025
Collocations, a crucial component of language competence, remain a challenge for L2 learners across all proficiency levels. While the data-driven learning (DDL) approach has shown great potential for collocation learning from a shorter-term perspective, this study investigates its effectiveness in the long term, examining both linguistic gains and…
Descriptors: Phrase Structure, Learning Analytics, English (Second Language), Second Language Instruction
Poole, Robert – Computer Assisted Language Learning, 2022
The present study explores the attitudes of novice teachers towards corpus-aided language learning and teaching in an undergraduate writing course for multilingual students at a large US public university. The participating instructors facilitated approximately 75 minutes of corpus training for their students and implemented 4-6 corpus activities…
Descriptors: Undergraduate Students, Beginning Teachers, Computational Linguistics, Second Language Learning
Zare, Javad; Karimpour, Sedigheh; Aqajani Delavar, Khadijeh – Computer Assisted Language Learning, 2023
The purpose of the present study was to investigate if following data-driven learning (DDL) to raise the learners' awareness of discourse organizers through concordancing improves their comprehension of English academic lectures. To address this issue, the current study adopted a quasi-experimental (comparison group, pretest-posttest) design. 96…
Descriptors: Classroom Communication, Discourse Analysis, Computational Linguistics, English for Academic Purposes
Han, Chao; Lu, Xiaolei – Computer Assisted Language Learning, 2023
The use of translation and interpreting (T&I) in the language learning classroom is commonplace, serving various pedagogical and assessment purposes. Previous utilization of T&I exercises is driven largely by their potential to enhance language learning, whereas the latest trend has begun to underscore T&I as a crucial skill to be…
Descriptors: Translation, Computational Linguistics, Correlation, Language Processing
Tsai, Shu-Chiao – Computer Assisted Language Learning, 2022
This study investigates the effectiveness of using Google Translate as a translingual CALL tool in English as a Foreign Language (EFL) writing, keyed to the perceptions of both more highly proficient Chinese English major university students and less-proficient non-English majors. After watching a 5-minute passage from a movie, each cohort of…
Descriptors: Computer Assisted Instruction, Translation, Second Language Learning, Second Language Instruction
Chung, Eun Seon; Ahn, Soojin – Computer Assisted Language Learning, 2022
Many studies that have investigated the educational value of online machine translation (MT) in second language (L2) writing generally report significant improvements after MT use, but no study as of yet has comprehensively analyzed the effectiveness of MT use in terms of various measures in syntactic complexity, accuracy, lexical complexity, and…
Descriptors: Translation, Computational Linguistics, English (Second Language), Second Language Learning
Chen, Hao-Jan Howard; Lai, Shu-Li; Lee, Ken-Yi; Yang, Christine Ting-Yu – Computer Assisted Language Learning, 2023
Knowledge of collocations is essential for English academic writing. However, there are few academic collocation referencing tools available and there is a pressing need to develop more. In this paper, we will introduce the ACOP (Academic Collocations and Phrases Search Engine), a newly developed corpus-based tool to search large academic corpora.…
Descriptors: Academic Language, English for Academic Purposes, Phrase Structure, Computational Linguistics
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
Tsai, Shu-Chiao – Computer Assisted Language Learning, 2019
This study investigates the impact on extemporaneous English-language first drafts by using Google Translate (GT) in three different tasks assigned to Chinese sophomore, junior, and senior students of English as a Foreign Language (EFL) majoring in English. Students wrote first in Chinese (Step 1), then drafted corresponding texts in English (Step…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Software
Daskalovska, Nina – Computer Assisted Language Learning, 2015
One of the aspects of knowing a word is the knowledge of which words it is usually used with. Since knowledge of collocations is essential for appropriate and fluent use of language, learning collocations should have a central place in the study of vocabulary. There are different opinions about the best ways of learning collocations. This study…
Descriptors: Computational Linguistics, Phrase Structure, Verbs, Form Classes (Languages)
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
Varley, Steve – Computer Assisted Language Learning, 2009
Corpus consultation is gaining in prominence as a language learning tool. This approach to language analysis has made its way into the language classroom where its presence ranges from the presentation of printed concordance data with accompanying tasks to the direct use of concordancing software by learners themselves to carry out analyses of…
Descriptors: Vocabulary Development, English (Second Language), Class Activities, Second Language Instruction