Investigating Syntactic Complexity in EFL Learners' Writing across Common European Framework of Reference Levels A1, A2, and B1

Abstract

The study investigates the linguistic basis of Common European Framework of Reference (CEFR) levels in English as a foreign language (EFL) learners’ writing. Specifically, it examines whether CEFR levels can be distinguished with reference to syntactic complexity (SC) and whether the results differ between two groups of EFL learners with different first languages (Sindhi and Finnish). This sheds light on the linguistic comparability of the CEFR levels across L1 groups. Informants were teenagers from Pakistan (N = 868) and Finland (N = 287) who wrote the same argumentative essay that was rated on a CEFR-based scale. The essays were analysed for 28 SC indices with the L2 Syntactic Complexity Analyzer and Coh-Metrix. Most indices were found to distinguish CEFR levels A1, A2, and B1 in both language groups: the clearest separators were the length of production units, subordination, and phrasal density indices. The learner groups differed most in the length measures and phrasal density when their CEFR level was controlled for. However, some indices remained the same, and the A1 level was more similar than A2 and B2 in terms of SC across the two groups.

Focused direct corrective feedback: Effects on the elementary English learners’ written syntactic complexity

“…For example, as Ortega (2015) stated, L1 is "a moderating variable of L2 syntactic complexity" (p. 84). This was also proved in Lu and Ai's (2015), Jiang, Bi, and Liu's (2019), Kuiken and Vedder's (2019), Khushik and Huhta's (2020) as well as Ströbel, Kerz, and Wiechmann (2020) studies. Further, genre/task/content also play roles in syntactic complexity in writing and L2 proficiency is also "a powerful source of influence that modulates syntactic complexity" (Ortega 2015, p. 88).…”

Transitivity Structure of Research Articles: Variations across Sections

“…(9) ||| The study investigates the linguistic basis of Common European Framework of Reference (CEFR) levels in English as a foreign language (EFL) learners' writing. ||| (Khushik & Huhta, 2020) (10) ||| The students came from two grades [[ that presumably represented A2 and B1 levels ]]. ||| (Khushik & Huhta, 2020) In example ( 9), one ranking clause, which is marked off by the symbol |||, was identified.…”

“…(Eguchi & Kyle, 2020) Sayer Process: verbal Verbiage (8) Indices of subordination showed fairly good separation between CEFR levels, particularly for A1 versus B1, but also between adjacent levels (Figure 2; Table S6 and S10). (Khushik & Huhta, 2020) Carrier Process: rel, attrib Attribute…”

Natural language processing for learner corpus research

“…Although early dependency annotation was derived from constituency parse annotation, most current dependency annotation is derived directly based on corpora annotated for dependency relationships. Dependency parsers use part of speech tags, word forms, lemmas, direction of dependencies and distance between words (among others, see e.g., Jurafsky & Martin, 2019) as feature sets to predict dependency relations. A variety of specific approaches and statistical/machine learning are used by various dependency parsers, but the distribution of feature set items in the training corpus are used to predict the dependency head of each word.…”

“…Furthermore, the release of web and desktopbased tools such as Coh-Metrix (Graesser, McNamara, Louwerse, & Cai, 2004;McNamara, Graesser, McCarthy, & Cai, 2014), the L2 Syntactic Complexity Analyzer (L2SCA; Lu, 2010;Lu & Ai, 2015) and the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC; Kyle, 2016), among others, have allowed researchers to leverage powerful NLP tools with little to no computer programming knowledge. Due to recent advances in core NLP processes (such as syntactic annotation), the growing availability of user-friendly tools, and the release of several large learner corpora such as the EF-Cambridge Open Language Database (EFCAMDAT; Huang, Murakami, Alexopoulou, & Korhonen, 2018) and many others 2 , researchers are increasingly using NLP tools to investigate the development of complex linguistic phenomena in large learner corpora (e.g., Díez-Bedmar & Pérez-Paredes, 2020;Green, 2019;Khushik & Huhta, 2020;Polio & Yoon, 2018).…”