Measuring Syntactic Complexity in L2 Writing Using Fine‐Grained Clausal and Phrasal Indices
By K. Kyle, Scott Andrew Crossley. 2018
316 citations recorded by the source corpus.
Syntactic complexity is an important measure of second language (L2) writing proficiency (Larsen–Freeman, 1978; Lu, 2011). Large‐grained indices such as the mean length of T‐unit (MLTU) have been used with the most consistency in L2 writing studies (Ortega, 2003). Recently, indices such as MLTU have been criticized, both for the difficulty in interpretation (e.g., Norris & Ortega, 2009) and for a potentially misplaced focus on clausal subordination (e.g., Biber, Gray, & Poonpon, 2011). In this article, we attempt to address both of these criticisms by using traditional indices of syntactic complexity (e.g., MLTU), fine‐grained indices of clausal complexity, and fine‐grained indices of phrasal complexity to predict holistic scores of writing quality. In 4 studies, we used indices of each index type to predict holistic writing quality scores in independent essays on the Test of English as a Foreign Language (TOEFL). We then used all index types in a combined analysis to predict a holistic writing score. The results indicated that fine‐grained indices of phrasal complexity were better predictors of writing quality than either traditional or fine‐grained clausal indices, though a single fine‐grained index of clausal complexity contributed to the combined model. These results provide some support for Biber et al.’s (2011) claims regarding complexity and academic L2 writing proficiency. [ABSTRACT FROM AUTHOR]
Subfields: Artificial Intelligence, Developmental and Educational Psychology, Literature and Literary Theory
View Paper: https://www.semanticscholar.org/paper/34a1c06870b28d6ed9364cb2a0b92150a7979251
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In Artificial Intelligence
In Developmental and Educational Psychology
In Literature and Literary Theory
Citations in the corpus
- Linguistic features in writing quality and development: An overview (2020, background): “Sentence length and T-unit calculations are consider large-grain, length features (Kyle & Crossley, 2018) and have been the most common approaches to measuring syntactic”