Neural machine translation in EFL classrooms: learners’ vocabulary improvement, immediate vocabulary retention and delayed vocabulary retention

Lo, Siowai

doi:10.1080/09588221.2023.2207603

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…Participants reported using OMT for unknown words and phrases, resulting in improved vocabulary retention. The findings align with previous research emphasizing the benefits of OMT in vocabulary acquisition (Fredholm, 2019;Lee, 2020;Lo, 2023), but this can be particularly evident when used in combination with other learning strategies and resources.…”

Section: Discussionsupporting

confidence: 89%

“…The area of OMT shares the same characteristics, but it is different in the sense that learners seem to be finding various ways to integrate it in their L2 learning. In Lo's (2023) ISSN 1110-2721 retention. Findings suggested the high proficiency level students maintained a parallel level of immediate and delayed retention compared to their lexical improvement, but lower proficiency learners were highly bound to vocabulary improvement and immediate vocabulary retention.…”

Section: Vocabulary Learning and (Machine) Translationmentioning

confidence: 99%

“…words, OMT has recently "gained increasing popularity among EFL learners as a CALL tool to improve vocabulary, and many learners have reported its helpfulness for vocabulary learning". Based on the scarcity of OMT research in relation to vocabulary retention,Lo (2023) studied whether editing with OMT would result in vocabulary improvement, immediate vocabulary retention and delayed vocabulary…”

mentioning

confidence: 99%

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The Impact of Online Machine Translation (OMT) on Vocabulary Learning and Translation Ability

ELEbyary

2023

CDELT Occasional Papers in the Development of English Education

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There has been growing evidence in recent years that L2 learners are increasingly using various online machine translation (OMT) applications. While advanced technologies have improved the accuracy of machine translated texts, many language teachers are still sceptical about their pedagogical value. Recently, many L2 researchers have shown interest in investigating the affordances of OMT in language learning. This study initially examined the role of OMT in L2 learning and whether L2 learners would value and/or trust OMT output. The study also investigated the extent to which vocabulary learning can accrue from integrating OMT in a translation course and whether the quality of selftranslated texts would improve as a result. A total of 128 participants attending a core translation course took part in this study. The study involved the use of a pre-post vocabulary test, a pre-post translation test, vocabulary uptake sheets (VUSs), a questionnaire, self-translated and machine-translated tasks. The major findings indicated perceptions of its positive role in enhancing L2 learning. The study also found evidence of vocabulary enhancement and a slight improvement in self-translated text quality. The affordances and limitations of OMT for L2 teaching and learning are discussed.

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Section: Discussionsupporting

confidence: 89%

Section: Vocabulary Learning and (Machine) Translationmentioning

confidence: 99%

mentioning

confidence: 99%

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The Impact of Online Machine Translation (OMT) on Vocabulary Learning and Translation Ability

ELEbyary

2023

CDELT Occasional Papers in the Development of English Education

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“…In a more recent study, Lo (2023) explored whether editing with MT improved Chinese English-asa-foreign-language learners' vocabulary learning and retention and found an immediate recall effect, but that 2 weeks later, lower proficiency level students had significantly declined in recall compared to their higher proficiency peers, suggesting that language proficiency may be an important factor in the degree of vocabulary learning from MT use. In any event, it remains unclear what long-term learning accrues from using MT in L2 writing.…”

Section: Machine Translationmentioning

confidence: 99%

Twenty‐first century technologies and language education: Charting a path forward

Kern

2024

The Modern Language Journal

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The COVID-19 pandemic, necessitating remote instruction in the vast majority of educational institutions around the world, put technology squarely at the center of language learning and teaching. Virtually all teaching and learning was mediated by Zoom or similar videoconferencing platforms. Students could see one another's faces (rather than the backs of their peers' heads), but they could also completely hide themselves from view. They used the chat window as a communicative backchannel for both authorized and unauthorized comments. They engaged in small group work in breakout rooms that were truly out of earshot of the teacher. Interacting with native speakers online was almost as easy as holding a regular class, once a mutually convenient meeting time could be negotiated. Web content such as videos, songs, news reports, artwork, and historical documents could be seamlessly integrated into lessons without the need for data projectors. However, in many quarters, this technological mediation weakened students' feelings of belonging, of camaraderie, of esprit de corps. Many teachers (and students) believe that learning remotely was not nearly as effective as face-to-face instruction (Muscanell, 2023).We have now returned to those face-to-face classrooms, and students (at least at my institution) are overjoyed to be in co-present community with one another. But the world is not quite the same as it was prepandemic. The fact that we were able to teach students remotely with some degree of success raises new questions about how we manage instruction going forward. Should we, in an effort to increase lagging enrollments, modify our 5-day-a-week teaching schedule so that we have 3 days in person and 2 days of synchronous or asynchronous online instruction? In theory, this would reduce conflicts with other courses, especially multihour labs. Should we, in an effort to "flip" our classrooms, shift the bulk of presentation of new material to online work that students do at home and then come prepared to apply that material to interactive and reflective activities in the classroom? Should we, to support less commonly taught languages and to broaden our teaching impact more generally, negotiate interinstitutional agreements that would allow sharing of resources (e.g., adding remote students from other institutions to in-person classes)? Should we, to expand our students' opportunities to use the language they are studying, have them do virtual internships abroad (or in the United States), promoting not only their language and cultural knowledge but also their professional skill set? These and many other questions are raised by new technological possibilities that invite us to reflect on whatThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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AI and English language teaching: Affordances and challenges

Crompton,

Edmett,

Ichaporia

et al. 2024

Brit J Educational Tech

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English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. However, English learners face many challenges in gaining English language skills. Extant studies show that AI has affordances to support in English language teaching and learning ELT/L. This study answers the call to examine specific challenges and affordances for using AI in ELT/L. A systematic review method was used with PRISMA principles to identify 42 studies. Findings reveal the geographical locations of studies, learner ages and years of study. Grounded coding was then used to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self‐regulation. AI in ELT/L challenges uncovered were technology breakdowns, limited capabilities, fear and standardising language. Policymakers, funders, practitioners and educational leaders can use the information provided in this study to gain a holistic understanding of the current trend in the use of AI in ELT/L, and practical implications are provided to guide future use of AI. Practitioner notesWhat is already known about this topic English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. Empirical evidence shows that pupils can often face difficulties when learning English, with challenges such as irregularity in English spelling. AI has supported language teaching and learning with studies showing that AI can support language‐specific skills. What this paper adds Provides the scholarly community with a unique systematic review in the use of AI in ELT/L across learner levels. Identifies affordances of AI in ELT/L in speaking, writing, reading, pedagogy and self‐regulation. Identifies challenges of AI in ELT/L in technology breakdowns, limited capabilities, fear and standardising language. Provides researchers with a review of the field with identification of gaps and future research opportunities. Implications for practice and/or policy Provides practical implications from the findings for educators, policy makers and program designers. Highlights the gaps in academic knowledge as a lack in the use of AI for assessment in ELT/L.

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Neural machine translation in EFL classrooms: learners’ vocabulary improvement, immediate vocabulary retention and delayed vocabulary retention

Cited by 3 publications

References 26 publications

The Impact of Online Machine Translation (OMT) on Vocabulary Learning and Translation Ability

The Impact of Online Machine Translation (OMT) on Vocabulary Learning and Translation Ability

Twenty‐first century technologies and language education: Charting a path forward

AI and English language teaching: Affordances and challenges

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