Assessing the validity of new paradigmatic complexity measures as criterial features for proficiency in L2 writings in English

This article addresses Second Language (L2) writing development through an investigation of new grammatical and structural complexity metrics. We explore the paradigmatic production in learner English by linking language functions to specific grammatical paradigms. Using the EFCAMDAT as a gold standard and a corpus of French learners as an external test set, we employ a supervised learning framework to operationalise and evaluate seven microsystems. We show that learner levels are associated with the seven microsystems (MS). Using ordinal regression modelling for evaluation, the results show that all MS are significant but yield a low impact if taken individually. However, their influence is shown to be impactful if taken as a group. These microsystems and their measurement method suggest that it is possible to use them as part of broader-purpose CALL systems focused on proficiency assessment.

Predicting CEFRL levels in learner English on the basis of metrics and full texts

This paper analyses the contribution of language metrics and, potentially, of linguistic structures, to classify French learners of English according to levels of the Common European Framework of Reference for Languages (CEFRL). The purpose is to build a model for the prediction of learner levels as a function of language complexity features. We used the EFCAMDAT corpus, a database of one million written assignments by learners. After applying language complexity metrics on the texts, we built a representation matching the language metrics of the texts to their assigned CEFRL levels. Lexical and syntactic metrics were computed with LCA, LSA, and koRpus. Several supervised learning models were built by using Gradient Boosted Trees and Keras Neural Network methods and by contrasting pairs of CEFRL levels. Results show that it is possible to implement pairwise distinctions, especially for levels ranging from A1 to B1 (A1=>A2: 0.916 AUC and A2=>B1: 0.904 AUC). Model explanation reveals significant linguistic features for the predictiveness in the corpus. Word tokens and word types appear to play a significant role in determining levels. This shows that levels are highly dependent on specific semantic profiles.