The Effect of Using DeepL on Improving Students' Writing Vocabulary

Abstract: As artificial intelligence technologies increasingly integrate into education, neural translation machines, such as Google Translate and DeepL, are gradually becoming an important tool for enhancing the effectiveness of foreign language learning. These technologies not only improve the efficiency of the learning experience but also address the limitations of traditional vocabulary acquisition methods. While research has examined DeepL's translation quality, there is limited focus on Spanish learners' attitudes and experiences with DeepL during the writing process, particularly regarding its impact on vocabulary enhancement and student feedback. This paper aims to investigate Chinese university students' perceptions of using DeepL to improve their Spanish writing vocabulary. The method used in this research was a descriptive quantitative technique. 60 undergraduate Spanish students completed a questionnaire. The results of this research showed that, Chinese undergraduate Spanish language students believe that the use of DeepL has a good effect on improving their writing vocabulary in general, but there are still some problems of the accuracy. Another conclusion worth emphasizing is that the use of DeepL to enrich the vocabulary of articles does not have a direct and strong connection with the improvement of the user's own vocabulary.

Authors

Institutions

Publication Details

Journal
Lecture Notes in Education Psychology and Public Media
Published
2024-11-08
DOI
https://doi.org/10.54254/2753-7048/64/20241004
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Effect of Using DeepL on Improving Students' Writing Vocabulary

Jingjing Jiang
Lecture Notes in Education Psychology and Public Media
Online Learning and Analytics
article

The Effect of Using DeepL on Improving Students' Writing Vocabulary

Jingjing Jiang
article en

Abstract

Abstract: As artificial intelligence technologies increasingly integrate into education, neural translation machines, such as Google Translate and DeepL, are gradually becoming an important tool for enhancing the effectiveness of foreign language learning. These technologies not only improve the efficiency of the learning experience but also address the limitations of traditional vocabulary acquisition methods. While research has examined DeepL's translation quality, there is limited focus on Spanish learners' attitudes and experiences with DeepL during the writing process, particularly regarding its impact on vocabulary enhancement and student feedback. This paper aims to investigate Chinese university students' perceptions of using DeepL to improve their Spanish writing vocabulary. The method used in this research was a descriptive quantitative technique. 60 undergraduate Spanish students completed a questionnaire. The results of this research showed that, Chinese undergraduate Spanish language students believe that the use of DeepL has a good effect on improving their writing vocabulary in general, but there are still some problems of the accuracy. Another conclusion worth emphasizing is that the use of DeepL to enrich the vocabulary of articles does not have a direct and strong connection with the improvement of the user's own vocabulary.

Lecture Notes in Education Psychology and Public MediaVol. 64(1)
Macau University of Science and Technology (MO)
Quality Education
Openalex Percentile: Top 79%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.