Evidence map›Paper›PMID 37520995›Full record

ArticleHeliyon2023

Using DeepL translator in learning English as an applied foreign language - An empirical pilot study.

Petra Polakova, Blanka Klimova

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Petra PolakovaDepartment of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Králové, Hradec Králové, Czech Republic.
Blanka KlimovaDepartment of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Králové, Hradec Králové, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advent of new emerging technologies, machine translation, especially natural machine translation (NMT) and its tools, is being increasingly applied in second language (L2) acquisition. The aim of this study is to investigate the usefulness of machine translation, specifically DeepL Translator, in the second language acquisition process, since it has a great potential to transform foreign language education. The present empirical pilot study describes an experiment dealing with the use of neural machine translation in the process of formal writing (i.e., writing a summary) in a foreign language. Altogether 16 university students learning English as an applied foreign language with C1 level of English proficiency participated in the experiment. The results show differences between pre-test and post-test, and a significant improvement in students' language skills due to the use of DeepL Translator. The questionnaire survey, among other things, reveals positive perceptions of this tool and awareness of improved language skills by the research participants. The findings indicate that purposefully guided working with a NMT tool can contribute to the perceived usefulness of its use in learning English as an applied foreign language.

Indexed as

DeepL translatorExperimentNeural machine translationSecond language acquisition

Identifiers

PMID37520995
PMCPMC10382660

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.