International Journal of Learner Corpus Research - Volume 8, Issue 2, 2022

Abstract

The difficulty of automatically extracting syntactic structures from authentic learner data has previously limited the kinds of questions addressed by means of Contrastive Interlanguage Analysis (CIA; Granger, 2015), or has forced researchers to resort to manual analysis of smaller corpora. This study responds to the call for greater use of parsed corpora by using automated parsing and refined corpus analysis software to investigate advanced EFL learners’ use of relative and participial clauses for nominal postmodification. The use of clausal postmodifiers inside the noun phrase may be seen as a marker of proficiency and syntactic maturity in academic writing, but the proportional use of relative and participial clauses for nominal postmodification may also be subject to L1 transfer. To investigate the role of transfer and syntactic maturity in the use of nominal postmodification, we compare texts written by Dutch EFL learners with those produced by learners from typologically distinct L1s, i.e. Czech and French, and by native speakers of English. Use of participial clauses and relative clauses in Dutch learners’ EFL texts is then compared with their use in L1 Dutch. The high degree of L1-interlanguage congruity suggests that transfer plays an important role in the Dutch learners’ strong preference for relative clauses for nominal postmodification. Based on the comparison with both other learner groups, we hypothesize that such transfer effects in the use of clausal postmodification can only emerge once the learner has reached a sufficient level of syntactic maturity in the target language.

Abstract

Investigation of the characteristics of spoken learner language has increased in recent years but has been primarily limited to the investigation of one linguistic level (e.g., lexico-grammar), which gives a limited picture of learners’ overall linguistic competence (e.g., Skarnitzl & Rumlová, 2019). In this study, we investigate lexico-grammar, fluency, and prosody in LINDSEI (German, Czech, and Spanish) alongside British and American English comparable corpora, using multidimensional analysis, a statistical procedure that identifies co-occurring linguistic features and leads to functional interpretation of the discourse. Results show significant differences between L1 and L2 groups on four of six dimensions and reveal novel patterns of co-occurrence. Dimension 1, for example, identifies correlates of informationally driven discourse on all three linguistic levels under investigation. These findings show the importance of including multiple linguistic levels in the analysis of learner discourse and have implications for a more holistic and functionally based approach to language instruction.

Abstract

Learner corpus research has a strong tradition of collecting metadata. However, while we tend to collect rich descriptive information about learners on directly measurable variables such as age, year of study, and time spent abroad, we frequently do not know much about learner characteristics that cannot be measured directly (and that thus need to be measured through questionnaires and tests) such as language aptitude, working memory, and motivation, which have been identified as important variables in neighboring fields such as Second Language Acquisition. In this position piece, we (i) join the proponents of increased focus on learner characteristics in LCR in arguing in favor of collecting information about such variables and (ii) introduce an analytical framework that can be used to model these variables. Specifically, the primary focus of this paper is to discuss the concept of latent variables as it relates to LCR and show how their standard form can be used to model learner characteristics within the structural equation modeling analytical framework.

Abstract

This article provides a detailed account of the framework, pedagogical and research applications of the Multilingual Academic Corpus of Assignments – Writing and Speech (MACAWS).1 MACAWS is a monitor learner corpus of written and oral assignments produced by foreign language learners in the context of their language learning classrooms. Currently the corpus focuses on two less commonly taught languages rarely represented in learner corpora, Portuguese and Russian, and contains 124,054 words in Russian and 536,168 in Portuguese, being updated each semester as new texts are added to the corpus. The online interface is designed for ease of use by teachers and students. Our novel interactive data-driven learning (iDDL) tool allows embedding of concordance lines into websites and learning management systems (LMS), facilitating student interaction with concordance lines. Researchers can gain access to an offline corpus for greater flexibility.

This article reviews Understanding Development and Proficiency in Writing: Quantitative Corpus Linguistic Approaches9781108770101

This article reviews Particle Placement in English L1 and L2 Academic Writing9782875589859

This article reviews The Routledge Handbook of Second Language Acquisition and Corpora978-0-8153-5287-7978-1-351-13790-4