Data-driven learning for languages other than English: the cases of
                        French, German, Italian, and Spanish

Jablonkai, Reka Ratkaine; Forti, Luciana; Castelló, Magdalena Abad; Iguenane, Isabelle Salengros; Schaeffer-Lacroix, Eva; Vyatkina, Nina

doi:10.14705/rpnet.2020.48.1177

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Corpus-informed materials for L2 English have grown significantly. However, LOTE resources remain scarce (Jablonkai et al, 2020). This disparity extends to research on the effectiveness of pedagogical corpus applications, where most studies focus on L2 English.…”

Section: Introductionmentioning

confidence: 99%

Data-driven learning beyond English: Insights and implications from three monographs

Forti,

Vyatkina,

Schaeffer-Lacroix

2023

EuroCALL 2023. CALL for All Languages - Short Papers

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This paper stems from the 2023 CorpusCALL SIG Symposium on Data-Driven Learning (DDL) for Languages Other Than English (LOTE). Three monographs on DDL for LOTE were presented and are briefly illustrated in this paper (Forti, 2023; Vyatkina, 2024; Schaeffer-Lacroix, 2019). Despite the growth of DDL in second language (L2) research and education in many languages, its focus has largely been on English. As a result, the knowledge about the applicability of DDL for learning LOTE remains limited. This hinders the validity and generalizability of DDL as a whole, and conceals the important implications related to bridging the research vs. practice gap in such contexts where the focus is on LOTEs. The monographs introduced in this paper demonstrate the relevance of DDL for learning LOTEs by discussing corpus-based resources, pedagogical applications, and empirical research from three perspectives.

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Section: Introductionmentioning

confidence: 99%

Data-driven learning beyond English: Insights and implications from three monographs

Forti,

Vyatkina,

Schaeffer-Lacroix

2023

EuroCALL 2023. CALL for All Languages - Short Papers

View full text Add to dashboard Cite

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Spanish corpora and their pedagogical uses: challenges and opportunities

Sánchez‐Gutiérrez

Cock

Tracy‐Ventura

2022

Journal of Spanish Language Teaching

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An Investigation Into Learners’ Cognitive Processes in Data-Driven Learning: Case Studies of Six Learners of Chinese

Liu,

Chen

2023

Chinese Journal of Applied Linguistics

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Data-driven learning (DDL), the direct use of corpus by learners in the second language classroom, has been shown to be effective in improving learners’ acquisition of various English linguistic items (Boulton & Cobb, 2017). However, it is still limited to our knowledge regarding how learners interact with DDL-related materials or tools and how DDL works in the learning/teaching of languages other than English. This study aims to closely examine the cognitive processes of six learners of Chinese in using printed concordance-based materials to learn Chinese resultative constructions. These materials contained adapted complete concordance sentences drawn from the Lancaster Corpus for Mandarin Chinese (McEnery & Xiao, 2004). The results show that in general, learners employed various strategies when scrutinising the concordance lines, particularly cognitive strategies such as summarising and grouping. Differences among individual learners were also found: learners who used more diverse strategies with higher frequencies could successfully infer the regularities, which led to learning gains. The study provides some implications for effective teacher guidance in DDL.

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Data-driven learning for languages other than English: the cases of French, German, Italian, and Spanish

Cited by 4 publications

References 11 publications

Data-driven learning beyond English: Insights and implications from three monographs

Data-driven learning beyond English: Insights and implications from three monographs

Spanish corpora and their pedagogical uses: challenges and opportunities

An Investigation Into Learners’ Cognitive Processes in Data-Driven Learning: Case Studies of Six Learners of Chinese

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