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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
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
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
Yang, Juan; Thomas, Michael S. C.; Qi, Xiaofei; Liu, Xuan – Computer Assisted Language Learning, 2019
From a psycholinguistic perspective of view, there are many cognitive differences that matter to individuals' second-language acquisition (SLA). Although many computer-assisted tools have been developed to capture and narrow the differences among learners, the use of these strategies may be highly risky because changing the environments or the…
Descriptors: Foreign Countries, Cognitive Ability, Phonological Awareness, English Teachers
Goh, Tiong-Thye; Sun, Hui; Yang, Bing – Computer Assisted Language Learning, 2020
This study investigates the extent to which microfeatures -- such as basic text features, readability, cohesion, and lexical diversity based on specific word lists -- affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268…
Descriptors: Essays, Writing Tests, English (Second Language), Second Language Learning
Crosthwaite, Peter – Computer Assisted Language Learning, 2017
An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at…
Descriptors: Feedback (Response), Error Correction, Morphology (Languages), Syntax
Yu, Ping; Pan, Yingxin; Li, Chen; Zhang, Zengxiu; Shi, Qin; Chu, Wenpei; Liu, Mingzhuo; Zhu, Zhiting – Computer Assisted Language Learning, 2016
Oral production is an important part in English learning. Lack of a language environment with efficient instruction and feedback is a big issue for non-native speakers' English spoken skill improvement. A computer-assisted language learning system can provide many potential benefits to language learners. It allows adequate instructions and instant…
Descriptors: English (Second Language), Foreign Countries, Second Language Instruction, Computer Assisted Instruction
Sha, Guoquan – Computer Assisted Language Learning, 2010
Data-driven learning (DDL), or corpus-based language learning, involves the learner in an exploratory task to discover appropriate expressions or collocates regarding his writing. However, the problematic units of meaning in each learner's writing are so diverse that conventional corpora often prove futile. The search engine Google with the…
Descriptors: Written Language, Search Engines, Second Language Learning, Computational Linguistics