Machine Translation An Enduring Chasm between Language Students and Teachers

A. Brown, Caroline Bennett, G. Bulman, Stefano Giannini, R. Habib, Emma Ticio Quesada
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Abstract

The COVID-19 pandemic has elevated focus on educational technologies (Elaish, et al., 2021). One area of sustained controversy in this domain centers around machine translation (MT), where language teachers and students have historically disagreed (Lee, 2020). While research has demonstrated the benefits of MT (e.g. Benda, 2013; Chon et al. 2021; Correa, 2014; Dziemianko, 2017; Enkin & Mejías-Bikandi, 2016; Garcia & Pena, 2011; Lee, 2020; Lee & Briggs, 2021) and studies have consistently reported frequent student usage of MT (e.g. Alhaisoni & Alhaysony, 2017) Clifford, Merschel, & Munné, 2013; Jin & Diefell, 2013; Tsai, 2019; Yang & Wang, 2019), teacher views have traditionally been negative (e.g. Case; 2015; Clifford, Merschel, & Munné, 2013; Niño, 2009; Stapleton & Leung Ka Kin, 2019). Given that recent research on MT has targeted ESOL (e.g. Lee, 2020; Murphy Odo, 2019; Tsai, 2019), that MT itself has evolved considerably since 2016 (Yang & Wang, 2019), and that teacher beliefs can be influenced by professional development and context (Borg, 2015), this study examined (1) contemporary attitudes toward and practices around MT among students (n=75) and teachers (n=25) of diverse languages, and (2) changes in instructor views after high impact pedagogical events: (a) a professional development seminar specifically on MT and (b) the “crisis‐prompted [shift to] remote language teaching” (Gacs et al, 2020) as a result of the global COVID-19 pandemic. Results from four surveys indicate a wide, enduring chasm between students, who increasingly use and feel positively towards MT but are varied in their understanding of implications of its use for academic integrity, and teachers, most of whom make no instructional use of MT, feel negatively about it, have clearer reviews on its relationship to academic integrity, and maintain their views after specific professional development and broad and far-reaching contextual events related to technology. Implications for practice, especially in the context of a surge in academic integrity violations related to MT during the COVID-19 pandemic (Çelik & Lancaster, 2021), will be discussed.
机器翻译:语言学生与教师之间的鸿沟
2019冠状病毒病大流行提高了对教育技术的关注(Elaish等,2021年)。该领域持续存在争议的一个领域是机器翻译(MT),这是语言教师和学生历来存在分歧的领域(Lee, 2020)。虽然研究已经证明了机器翻译的好处(例如Benda, 2013;Chon et al. 2021;科雷亚,2014;Dziemianko, 2017;Enkin & Mejías-Bikandi, 2016;Garcia & Pena, 2011;李,2020;Lee & Briggs, 2021),并且研究一直报告学生频繁使用MT(例如Alhaisoni & Alhaysony, 2017)。Clifford, Merschel, & munn, 2013;Jin & Diefell, 2013;蔡,2019;Yang & Wang, 2019),教师的观点传统上是消极的(例如Case;2015;Clifford, Merschel, & munn, 2013;尼诺,2009;斯台普顿&梁家健,2019)。鉴于最近MT研究的目标是ESOL(例如Lee, 2020;Murphy Odo, 2019;Tsai, 2019),机器翻译本身自2016年以来已经发生了很大的变化(Yang & Wang, 2019),教师的信念可以受到专业发展和背景的影响(Borg, 2015),本研究调查了(1)不同语言的学生(n=75)和教师(n=25)对机器翻译的当代态度和实践,以及(2)高影响力教学事件后教师观点的变化。(a)专门针对语言翻译的专业发展研讨会;(b)全球COVID-19大流行导致的“危机促使[转向]远程语言教学”(Gacs等人,2020年)。四项调查的结果表明,学生和教师之间存在广泛而持久的分歧,学生越来越多地使用机器学习并对其持积极态度,但对其使用对学术诚信的影响的理解各不相同,而教师大多数没有将机器学习用于教学,对其持消极态度,对其与学术诚信的关系有更清晰的评价,并在具体的专业发展和与技术相关的广泛而深远的背景事件后保持自己的观点。将讨论对实践的影响,特别是在2019冠状病毒病大流行期间与MT相关的学术诚信违规行为激增的背景下(Çelik & Lancaster, 2021)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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