Evidence map›Paper›PMID 38689960›Full record

ArticleHeliyon2024

Applying augmented reality multimedia technology to construct a platform for translation and teaching system.

Yue Zhao, Qilin Wang

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yue ZhaoSchool of Chinese Language and Literature, Liaoning Normal University, Dalian, 116021, China.
Qilin WangSchool of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper investigates the integration of augmented reality (AR) technology into English translation teaching for college students, emphasizing the pivotal role of innovative teaching methods in enhancing students' translation skills and learning experiences. To address the issue of insufficient interest in English translation teaching, the paper initially assesses the purpose and significance of learning English translation through a questionnaire survey, elucidating challenges encountered in English language acquisition. Subsequently, adhering to AR principles, a teaching demonstration platform rooted in AR technology is conceived and developed, intricately aligned with English translation instruction. The platform serves as a solution to issues in English learning, such as inadequate course comprehension, low utilization of teaching resources, and instructors' lack of experience. The research culminates in the analysis of survey results, wherein the quantitative disparities in translation ability between students utilizing the research platform and those subjected to traditional teaching methods are examined. The findings underscore the positive impact of the AR-based research platform on improving students' translation proficiency. The AR platform heightens learners' engagement in the learning process, contributes to constructing a robust knowledge framework, and enhances overall learning outcomes. The platform offers educators opportunities to optimize experimental courses and elevate teaching standards. The paper's outcomes present novel pedagogical scenarios for learners, propose technical solutions for other technical disciplines and furnish a theoretical foundation and application model for a new generation of experimental demonstration platforms.

Indexed as

AR technologyAugmented realityComputerEnglish translationMultimedia

Identifiers

PMID38689960
PMCPMC11059544

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.