본문 바로가기
KERD
논문KCI 등재

Direct Writing, Translated Writing, and Machine-Translated Writing: A Text Level Analysis with Coh-Metrix

신동광|전유아
한국영어교육학회 (Korea Association Of Teachers Of English)|2020.06|영어교육 (English Teaching)|제75권 제1호|pp. 25-48

1

조회

0

다운로드

24

인용

0

참고문헌

키워드

Coh-MetrixL2 writingmachine translationdirect writingtranslated writingmachine-translated writing
주제분류영어와문학

초록

While learners may have access to reference tools during second language (L2) writing, the latest developments in machine translation (MT), such as Google Translate requires examination as to how using the tool may factor into the second language learners’ writing products. To this end, the purpose of this study was to examine how MT may have an effect on L2 learners’ writing products relative to when writers wrote directly in L2, or translated a text to English from Korean. EFL university learners were asked to write for prompts that were counterbalanced for three writing modes and three writing topics. The learners’ writing products were analyzed with Coh-Metrix to provide information on text characteristics at the multilevel. The results indicate that MT could facilitate the learners to improve fluency and cohesion, produce syntactically complex sentences, and write concrete words to express their target messages. Pedagogical implications are provided for how MT can be used to improve the quality of the L2 learners’ writing products.

Abstract

While learners may have access to reference tools during second language (L2) writing, the latest developments in machine translation (MT), such as Google Translate requires examination as to how using the tool may factor into the second language learners’ writing products. To this end, the purpose of this study was to examine how MT may have an effect on L2 learners’ writing products relative to when writers wrote directly in L2, or translated a text to English from Korean. EFL university learners were asked to write for prompts that were counterbalanced for three writing modes and three writing topics. The learners’ writing products were analyzed with Coh-Metrix to provide information on text characteristics at the multilevel. The results indicate that MT could facilitate the learners to improve fluency and cohesion, produce syntactically complex sentences, and write concrete words to express their target messages. Pedagogical implications are provided for how MT can be used to improve the quality of the L2 learners’ writing products.

참고문헌 (38)

  1. Abdel Latif, M. M. M., "What do we mean by writing fluency and how can it be validly measured?", Applied Linguistics, 34(1), pp.99~105 (2012)
  2. Ali, N. H., "Translation versus direct composition: Effects of first language on second language Arab writers", Doctor, pp.1996
  3. Austermühl, F., "On clouds and crowds : Current developments in translation technology", Translation in Transition, 9, pp.1~27 (2011)
  4. Bulté, B., "Dimensions of L2 performance and proficiency: Complexity, accuracy and fluency in SLA", pp.21~46 (2012), John Benjamins
  5. Chenoweth, N. A., "Fluency in writing : Generating text in L1 and L2", Written Communication, 18(1), pp.80~98 (2001)
  6. Cohen, A. D., "Research on direct versus translated writing : Students’strategies and their results", The Modern Language Journal, 85(2), pp.169~188 (2001)
  7. Crossley, S. A., "Text readability and intuitive simplification : A comparison of readability formulas", Reading in a Foreign Language, 23, pp.84~102 (2011)
  8. Crossley, S. A., "A linguistic analysis of simplified and authentic texts", The Modern Language Journal, 91(1), pp.15~30 (2007)
  9. Crossley, S. A., "Predicting second language writing proficiency : The roles of cohesion and linguistic sophistication", Journal of Research in Reading, 35(2), pp.115~135 (2012)
  10. Crossley, S. A., "The development of writing proficiency as a function of grade level : A linguistic analysis", Written Communication, 28, pp.282~ (2011)
  11. Garcia, I., "Machine translation-assisted language learning : Writing for beginners", Computer Assisted Language Learning, 24(5), pp.471~487 (2011)
  12. Graesser, A. C., "Coh-Metrixmeasures text characteristics at multiple levels of language and discourse", The Elementary School Journal, 115(2), pp.210~229 (2014)
  13. Graesser, A. C., "Coh-Metrix : Providing multilevel analyses of text characteristics", Educational Researcher, 40(5), pp.223~234 (2011)
  14. Graesser, A. C., "Coh-Metrix: Analysis of text on cohesion and language", Behavior Research Methods, Instrument& Computers, 36(2), pp.193~202 (2004)
  15. Groves, M., "Friend or foe? Google Translate in language for academic purposes", English for Specific Purposes, 37, pp.112~121 (2015)
  16. Ismail, A., "Errors made in Google Translate in the Indonesian to English translations of news item texts", ELT Forum : Journal of English Language Teaching, 5(2), pp.1~6 (2016)
  17. Jarvis, S., "Vocabulary knowledge: Human ratings and automated measures", pp.13~45 (2013), John Benjamins
  18. Jewitt, C., "Technology, literacy, learning: A multimodalapproach" (2012), Routledge
  19. Johnson, M. D., "Vocabulary knowledge and vocabulary use in second language writing", TESOL Journal, 7(3), pp.700~715 (2016)
  20. Kirchhoff, K., "Application of statistical machine translation to public health information : A feasibility study", Journal of the American Medical Informatics Association, 18(4), pp.473~478 (2011)
  21. Kliffer, M. D., "An experiment in MT post-editing by a class of intermediate", advanced French majors, 2005, ~
  22. Kobayashi, H., "Effects of first language on second language writing : Translation versus direct composition", Language Learning, 42(2), pp.183~209 (1992)
  23. Landauer, T., "Handbook of latent semantic analysis" (2007), Erlbaum
  24. La Torre, M. D., "A web-based resource to improve translation skills", ReCALL, 11, pp.41~49 (1999)
  25. Sangmin-Michelle Lee, "The impact of using machine translation on EFL students’ writing", Computer Assisted Language Learning, 33(3), pp.157~175 (2020), Informa UK Limited[DOI]
  26. Long, M., "Foreign language research in cross-cultural perspective", pp.39~52 (1991), John Benjamins
  27. McCarthy, P. M., "MTLD, vocd-D, and HD-D : A validation study of sophisticated approaches to lexical diversity assessment", Behavior Research Methods, 42(2), pp.381~392 (2010)
  28. McNamara, D. S., "Measuring up: Advances in how we assess reading ability", pp.89~116 (2012), Rowman & Littlefield Education
  29. McNamara, D. S., "Automated evaluation of text and discourse with Coh-Metrix" (2014), Cambridge University Press
  30. McNamara, D. S., "Coh-Metrix : Capturing linguistic features of cohesion", Discourse Processes, 47(4), pp.292~330 (2010)
  31. Nation, I. S. P., "Researching and analyzing vocabulary" (2011), Heinle Cengage Learning
  32. Niño, A., "Evaluating the use of machine translation post-editing in the foreign language class", Computer Assisted Language Learning, 21(1), pp.29~49 (2008)
  33. Development of writing assessment software for high school classroom in Korea, "Park, Y.", Korea Institute for Curriculum and Evaluation, ~, pp.Report No. RRE 2014-11 (2014)
  34. Slocum, J., "Machine translation systems", pp.1~47 (1988), Cambridge University Press
  35. Somers, H., "Computers and translation: A translator’s Guide", pp.319~340 (2003), John Benjamins
  36. Uzawa, K., "Second language learners’processesof L1 writing, L2 writing, and translation from L1 into L2", Journal of Second Language Writing, 5(3), pp.271~294 (1996)
  37. Van Rensburg, A., "Applying Google Translate in a higher education environment : Translation products assessed", Southern African Linguistics and AppliedLanguage Studies, 30(4), pp.511~524 (2012)
  38. Wallwork, A., "English for academic research: A guide for teachers" (2016), Springer

관련 논문AI추천

Discriminating L2 Writing Proficiency through the Use of Computational Tool Coh-Metrix안수진언어과학회 · 언어과학연구2020 · 제92호Students’ Perceptions of the Use of Online Machine Translation in L2 Writing안수진, 정은선한국멀티미디어언어교육학회 · 멀티미디어 언어교육2020 · 제23권 제2호Exploring the Linguistic and Textual Factors That Affect the Quality of L2 Writing: The Coh-Metrix Study임효정, Inam Ullah현대영어교육학회 · 현대영어교육2023 · 제24권 The Effect of Using Machine Translation on Korean Middle School Students’ L2 Writing Performance and Affective Aspects손선영한국멀티미디어언어교육학회 · 멀티미디어 언어교육2022 · 제25권 제2호Characteristics of Eight Korean EFL Young Adults’ L2 Writing Using Machine Translation Programs이기인, 이병민한국멀티미디어언어교육학회 · 멀티미디어 언어교육2021 · 제24권 제2호기계번역의 오류 양상과 사후수정(post-editing)이L2 쓰기 적절성에 미치는 영향신동광, 전유아한국영어교과교육학회 · 영어교과교육2022 · 제21권 제1호

이 간행물의 다른 논문 (9건)

“I actually picked up a physics textbook:” Complexities of the Freedom Principle in Extensive Reading이미진, 노은석pp. 3-23Use of Be-Forms as Topic Markers in Interlanguage남보라pp. 9-33Cross-Linguistic Influence in the Use of Be in L3 English by L1-Chinese and L1-Russian Children in Korea김기택, 조규희, 홍승진pp. 35-53Spontaneous Motion in L1- and L2-English Speech: A Corpus-Based Study성민창, 김기택pp. 49-66The Long-Term Effect of Automated Writing Evaluation Feedback on Writing Development이영주pp. 67-92
원문 보기