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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
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
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