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Data‐driven learning: English as a foreign language writing and complexity, accuracy and fluency measures.

  • Published In: Journal of Computer Assisted Learning, 2023, v. 39, n. 4. P. 1382 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Şahin Kızıl, Aysel 3 of 3

Abstract

Background: Data‐driven learning (DDL) has been regarded as one of the promising approaches that could effectively enhance writing performance of English as a foreign language (EFL) learners. Although extensive research has been conducted on the use of DDL in developing various aspects of writing skill, there exist only few studies to date that focus on DDL for revising writing despite its great potential for revision. Furthermore, among the existing studies, very few investigated DDL at revision stage by referring to complexity, accuracy and fluency (CAF) measures which are objective and reliable indices of writing quality, and none has examined these three CAF measures jointly as an index of writing development. Objectives: With the purpose of addressing these gaps, the present article describes an instructional design which integrates DDL into the writing process of EFL learners who were taught how to exploit corpus data in revising their writings and then reports on the effects of such a design on their actual learning outcomes as measured in CAF. Methodology: In a quasi‐experimental between‐group pre‐test/post‐test design, 31 tertiary level EFL students were divided into experimental (DDL) and control (non‐DDL) groups. Experimental group was instructed on corpus consultation to revise their writings while the control group followed the conventional practices of using dictionaries, textbooks and teacher explanations in revising their writings. Writing performance of each group was analysed based on CAF measures and compared through one‐way between‐group multivariate analysis of covariance. Results and Conclusions: The findings showed that DDL used for writing revision had a significant, positive effect on fluency and lexical complexity as DDL group outperformed the non‐DDL group in producing lexically diverse and fluent writings. However, no statistically significant evidence was found favouring the effect on DDL on accuracy and grammatical complexity. Lay Description: What is already known about this topic?: DDL is an inductive approach that makes use of language corpora (i.e. large electronic collections of language data compiled and stored in a principled way) as the source of data.DDL is effective in improving students' linguistic capabilities mainly in vocabulary and grammar of the target language and in increasing language awareness and autonomy.DDL has great potential to improve writing skill by enabling learners to discover lexico‐grammatical patterns through multiple examples of target languageBeing multi‐componential in nature, L2 writing is thought to be best captured through CAF measures; however, the impact of DDL on CAF of learner writings is equivocal. What this article adds?: This articleis one of the first attempts focusing on the intersection of DDL and CAF development in EFL learners' writing skill.provides empirical evidence on the use of DDL as a revision tool in EFL writing pedagogy. The implications of study findings for practitionersLanguage corpora through DDL could be integrated into EFL writing instruction as one way of improving fluency and lexical complexity.EFL teachers should also consider combined use of other reference sources with DDL tools to ensure improvement on all dimensions of writing including accuracy and grammatical complexity. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Computer Assisted Learning. 2023/08, Vol. 39, Issue 4, p1382
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2023
  • ISSN:0266-4909
  • DOI:10.1111/jcal.12807
  • Accession Number:164914332
  • Copyright Statement:Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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