Predicting L2 Writing Proficiency with Computational Indices Based on N-grams
(주)코리아스칼라
- 최초 등록일
- 2023.04.05
- 최종 저작일
- 2017.12
- 20페이지/ 어도비 PDF
- 가격 5,500원
* 본 문서는 배포용으로 복사 및 편집이 불가합니다.
서지정보
ㆍ발행기관 : 서울대학교 외국어교육연구소
ㆍ수록지정보 : 외국어교육연구 / 21권
ㆍ저자명 : Byung-Doh Oh
목차
Ⅰ. Introduction
Ⅱ. Literature Review
Ⅲ. Methods
Ⅳ. Results and Discussion
Ⅴ. Conclusion
REFERENCES
영어 초록
Linguistic features that are indicative of higher writing proficiency levels can inform many aspects of lanauage assesment such as scoring rubrics, test items, and automated essay scoring(AES). The recent advancement of computer algorithms that automatically calculate indicates based on various linguistic features has made it possible to examine the relationship between linguistic features and writing proficiency on a larger scale. While the ability to use appropriate n-grams - recurring sequences of contiguous words - has been identified as a characteristic differentiating between proficiency levels in the literature, few studies have examined this relationship using computational indices. To this end, this study utilized the Tool for the Automatic Analysis of Lexical Sophistication(TAALES;Kyle&Crossley, 2015) to calcualte eight indices based on n-grams from a stratified corpus consisting of 360 argumentative essays written by Korean college-level learners. First, the indices from the training set of 240 essays were used to design a multinomial logistic regression model in order to identify indices that are significant predictors of writing proficiency levels. Subsequently, the regression model was applied to a test set of 120 essays to examine whether the model could be used to predict the proficiency levels of unseen essays. The results revealed that the mean bigram T, mean bigram Delta P, mean bigram-to-unigram Delta P, and proportion of 30,000 most frequent trigrams indices were significant predictors of proficiency levels. Furthermore, the regression model based on eight indices correctly classfied 52.5% of essays in the test set, demonstrating above-chance level accuarcy.
참고 자료
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