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Pérez-Paredes, Pascual – Computer Assisted Language Learning, 2022
This research uses the theoretical framework of CALL normalisation developed by Bax (2003) and Chambers and Bax (2006) to offer a systematic review (Gough et al., 2012) of the uses and spread of data-driven learning (DDL) and corpora in language learning and teaching across five major CALL-related journals during the 2011-2015 period. DDL research…
Descriptors: Computational Linguistics, Teaching Methods, Computer Assisted Instruction, Second Language Learning
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Kennedy, Claire; Miceli, Tiziana – Computer Assisted Language Learning, 2017
While there is widespread agreement on the expected benefits of hands-on access to corpora for language learners, reports abound of the difficulties involved in realising those benefits in practice. A particular focus of discussion is the challenge of transferring the skills of the corpus linguist to learners, so that they can explore this type of…
Descriptors: Computational Linguistics, Teaching Methods, Second Language Learning, Second Language Instruction
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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
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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
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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
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Rezaee, Abbas Ali; Marefat, Hamideh; Saeedakhtar, Afsaneh – Computer Assisted Language Learning, 2015
Collocational competence is recognized to be integral to native-like L2 performance, and concordancing can be of assistance in gaining this competence. This study reports on an investigation into the effect of symmetrical and asymmetrical scaffolding on the collocational competence of Iranian intermediate learners of English in the context of…
Descriptors: Computational Linguistics, Phrase Structure, Scaffolding (Teaching Technique), Foreign Countries
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Lai, Shu-Li; Chen, Hao-Jan Howard – Computer Assisted Language Learning, 2015
A number of studies have suggested the possible benefits of using concordancers in English as a Foreign Language (EFL) writing. To date, however, there is still limited understanding of how EFL writers may make use of such tools along with dictionaries when engaging in a writing task. To better understand the question, this study investigated a…
Descriptors: Dictionaries, Indexes, English (Second Language), Second Language Learning
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Chen, Hao-Jan Howard – Computer Assisted Language Learning, 2011
The development of adequate collocational knowledge is important for foreign language learners; nonetheless, learners often have difficulties in producing proper collocations in the target language. Among the various ways of learning collocations, the DDL (data-driven learning) approach encourages independent learning of collocations and allows…
Descriptors: Independent Study, Foreign Countries, Second Language Learning, Phrase Structure
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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
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Smith, Simon – Computer Assisted Language Learning, 2011
This exploratory study describes a framework for data-driven learning (DDL), in General (non-major) English university classes, in which learners "construct" linguistic corpora instead of merely "consulting" them. Prior related work has addressed the needs of language specialists, in particular trainee translators who are…
Descriptors: Foreign Countries, Nonmajors, Qualitative Research, Glossaries
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Schulze, Mathias; Penner, Nikolai – Computer Assisted Language Learning, 2008
The choice of grammatical framework in ICALL--the branch of CALL that applies artificial intelligence techniques--has important implications for both research and development. Matthews (1993) argued for one "that potentially meshes with SLA (second language acquisition)" (p. 5) and sketches three criteria that facilitate the crucial…
Descriptors: Research and Development, Grammar, Artificial Intelligence, Learning Processes
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Futagi, Yoko; Deane, Paul; Chodorow, Martin; Tetreault, Joel – Computer Assisted Language Learning, 2008
This paper describes the first prototype of an automated tool for detecting collocation errors in texts written by non-native speakers of English. Candidate strings are extracted by pattern matching over POS-tagged text. Since learner texts often contain spelling and morphological errors, the tool attempts to automatically correct them in order to…
Descriptors: Native Speakers, English (Second Language), Limited English Speaking, Computational Linguistics