Google Translate in Language Learning : Indonesian EFL Students' Attitudes

Murtisari, Elisabet Titik; Widiningrum, Rindang; Branata, Joshua; Susanto, Riana Devi

doi:10.18823/asiatefl.2019.16.3.14.978

Cited by 30 publications

(43 citation statements)

References 5 publications

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“…Fredholm (2015) also discovered that the MT group produced fewer errors in spelling and grammar (but more errors in syntax) than the non-MT group, and found in a later study (Fredholm, 2019) that the MT group produced texts with higher lexical diversity and density. Similarly, students significantly reduced lexical and grammatical errors in their revisions by using MT (Lee, 2020;Tsai, 2019), and, as a consequence, they found MT an effective pedagogical supplement to FL learning (Bahri & Mahadi, 2016;Murtisari, Widiningrum, Branata & Susanto, 2019;Stapleton & Kin, 2019). It also has been known that MT facilitated students' metacognitive knowledge toward L2 (Clifford, Mershel & Munné, 2013;Thue Vold, 2018) and provided a more comfortable L2 learning environment (Bahri & Mahadi, 2016;Niño, 2008).…”

Section: Literature Reviewmentioning

confidence: 89%

An investigation of machine translation output quality and the influencing factors of source texts

Lee¹

2021

ReCALL

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The use of machine translation (MT) in the academic context has increased in recent years. Hence, language teachers have found it difficult to ignore MT, which has led to some concerns. Among the concerns, its accuracy has become a major factor that shapes language teachers’ pedagogical decision to use MT in their language classrooms. Despite the urgency of the issue, studies on MT output quality in foreign language education remain scarce. Moreover, as MT is advancing every year, updated studies are imperative. Therefore, the present study investigated the quality of MT outputs (Google Translate) from Korean to English by comparing it with the English-translated texts of intermediate English as a foreign language students. The study also examined the factors within the source texts that affect the quality of MT outputs. Five trained evaluators examined multiple aspects of MT output samples (N = 104) and students’ English texts (N = 104), including mechanics, vocabulary, grammar, and context. The results showed that both texts were equally comprehensible, but MT outperformed the students in most aspects under investigation. The study further found that only two factors in the source texts – punctuation and sentence complexity – influenced MT output quality, whereas lexical and grammatical accuracy, lexical diversity, and contextual understanding did not affect it. Based on the results, the study presents classroom implications for using MT for educational purposes.

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Section: Literature Reviewmentioning

confidence: 89%

An investigation of machine translation output quality and the influencing factors of source texts

Lee¹

2021

ReCALL

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“…However, negative effects of machine translation include avoidance behavior in using the target language (Musk, 2014 ), incorrect translations, poor grammatical solutions (Josefsson, 2011 ), and instances of gender or cultural bias (Fitria, 2021 ). Murtisari et al ( 2019 ) consider one affordance of machine translation to include increased autonomy since students are less reliant on help-seeking strategies. Although, this trade-off between translating and help-seeking prevents students from developing social strategies associated with review and editing, frequently used by high-performing L2 writers (Bailey, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

A structural equation model of second language writing strategies and their influence on anxiety, proficiency, and perceived benefits with online writing

Bailey

Almusharraf

2022

Educ Inf Technol

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Second language (L2) writing strategies are essential for successful learning outcomes in courses with a substantial writing component and this is especially true during emergency remote teaching (ERT) when online writing tasks help compensate for the missing offline communication. Online writing tasks are multimodal and interactive, and widely delivered through assignment modules, discussion forums, social media, and other online channels, yet little is known pertaining to L2 writing strategies and online writing beliefs. The current study investigates the types of L2 writing strategies students employed during ERT in the midst of Covid-19 and then used structural modeling to understand how strategies relate to online writing task perceptions, L2 writing anxiety, and L2 writing proficiency. The four L2 writing strategy categories are related to planning, monitoring, reviewing, and translating. Following a cross-sectional survey design, a total of 256 South Korean EFL students completed the study’s questionnaire administered during their second semester of ERT. Overall, students reported using planning and monitoring strategies the most while using moderate levels of translation and review strategies. Translation strategies produced a significant positive relationship with L2 writing anxiety, indicating effort-avoidance behavior among apprehensive writers. Contrarily, planning and review strategies were positively associated with increased levels of perceived benefits with online writing tasks. When added to the model, the statistically significant correlations among monitoring category and outcome variables vanished, indicating a mediation effect. Along with addressing future directions in L2 writing strategy research in the post-Covid era, some pedagogical implications for the evolving application of translation strategies are discussed.

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“…Meanwhile, Murtisari's research revealed the use of MT in teaching English as a Foreign Language (EFL) in terms of the length of the text translated by students and their perceptions of MT. Murtisari's research examined EFL students' attitudes towards the use of MT in reading and writing text tasks (Murtisari et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Takarir: A new simultaneous translator voice to text to promote bi/multilinguality

Kharis

Kisyani²,

Suhartono³

et al. 2021

JLLS

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This study aims to evaluate Takarir as the product of the research development reviewed from the look and the results of its translation. This is a qualitative descriptive type of research. Data are obtained from students of German learners who understand translation theory and have the skills of the German language at least B1 level standards the common European framework of reference for languages (CEFR). Takarir is an application of a web-based translation machine and supports 44 input languages and dialects. Takarir is built with Macromedia Dreamweaver software. Respondents followed the seminars in German and Bahasa Indonesia, activated the Takarir as a translator, and observed its translation results. The analysis results show that the Takarir can translate speech to text from Bahasa Indonesia to German and vice versa with several errors. Based on the analysis, it can be concluded that Takarir takes advantage of the new technology, inspires to learn, and supports bi/-multilingualism activities.

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Google Translate in Language Learning : Indonesian EFL Students' Attitudes

Cited by 30 publications

References 5 publications

An investigation of machine translation output quality and the influencing factors of source texts

An investigation of machine translation output quality and the influencing factors of source texts

A structural equation model of second language writing strategies and their influence on anxiety, proficiency, and perceived benefits with online writing

Takarir: A new simultaneous translator voice to text to promote bi/multilinguality

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