Automatically predicting the level of second language (L2) learner proficiency is an emerging topic of interest and research based on machine learning approaches to language learning and development. The key to the present paper is the combined use of what we refer to as ‘complexity contours’, a series of measurements of indices of L2 proficiency obtained by a computational tool that implements a sliding window technique, and recurrent neural network (RNN) classifiers that adequately capture the sequential information in those contours. We used the EF-Cambridge Open Language Database (Geertzen et al. 2013) with its labelled Common European Framework of Reference (CEFR) levels (Council of Europe 2018) to predict six classes of L2 proficiency levels (A1, A2, B1, B2, C1, C2) in the assessment of writing skills. Our experiments demonstrate that an RNN classifier trained on complexity contours achieves higher classification accuracy than one trained on text-average complexity scores. In a secondary experiment, we determined the relative importance of features from four distinct categories through a sensitivity-based pruning technique. Our approach makes an important contribution to the field of automated identification of language proficiency levels, more specifically, to the increasing efforts towards the empirical validation of CEFR levels.

In this paper we employ a novel approach to advancing our understanding of the development of writing in English and German children across school grades using classification tasks. The data used come from two recently compiled corpora: The English data come from the the GiC corpus (983 school children in second-, sixth-, ninth- and eleventh-grade) and the German data are from the FD-LEX corpus (930 school children in fifth- and ninth-grade). The key to this paper is the combined use of what we refer to as ‘complexity contours’, i.e. series of measurements that capture the progression of linguistic complexity within a text, and Recurrent Neural Network (RNN) classifiers that adequately capture the sequential information in those contours. Our experiments demonstrate that RNN classifiers trained on complexity contours achieve higher classification accuracy than one trained on text-average complexity scores. In a second step, we determine the relative importance of the features from four distinct categories through a Sensitivity-Based Pruning approach.

The purpose of this paper is twofold: [1] to introduce, to our knowledge, the largest available resource of keystroke logging (KSL) data generated by Etherpad (https://etherpad.org/), an open-source, web-based collaborative real-time editor, that captures the dynamics of second language (L2) production and [2] to relate the behavioral data from KSL to indices of syntactic and lexical complexity of the texts produced obtained from a tool that implements a sliding window approach capturing the progression of complexity within a text. We present the procedures and measures developed to analyze a sample of 14,913,009 keystrokes in 3,454 texts produced by 512 university students (upper-intermediate to advanced L2 learners of English) (95,354 sentences and 18,32,027 words) aiming to achieve a better alignment between keystroke-logging measures and underlying cognitive processes, on the one hand, and L2 writing performance measures, on the other hand. The resource introduced in this paper is a reflection of increasing recognition of the urgent need to obtain ecologically valid data that have the potential to transform our current understanding of mechanisms underlying the development of literacy (reading and writing) skills.