International Journal of Learner Corpus Research - Volume 11, Issue 2, 2025

Abstract

The present study concerns the effect of lexical complexity on grading of Swedish EFL learners’ texts during high-stakes exams. A learner corpus consisting of 142 texts graded by expert raters and 175 texts graded by teachers was analysed to establish if the latter graded in agreement with the former as intended by the Swedish National Agency for Education (SNAE). Four indices of lexical complexity available in TAALED and TAALES were chosen to explore if this is the case. The method includes conducting ordinal regression with interactions to determine the effect of the independent variables on grade and if these variables have the same effect in texts graded by teachers and expert raters. The findings reveal a discrepancy between expert raters and teachers as they appear to consider lexical complexity to a different extent. It was also found that expert raters and teachers graded more in agreement during source-based writing tasks compared to independent writing tasks.

Abstract

The present study compares the use of adjective intensification in written L2 Italian production in South Tyrolean upper secondary schools with that of young Italian native speakers. By relying on a Diasystematic Construction Grammar approach, it explores the role of learners’ L1s, L2 proficiency levels and their linguistic environments as potential variables affecting the use and choice of different intensifying constructions. Results show that a dominant German-speaking linguistic environment is a significant predictor of learners’ preferences for a syntactic over a morphological intensification type. Unexpectedly, however, learners of Italian also make heavy use of the intensifying suffix — issimo, an unfamiliar construction in German. Results also show a difference in the diversity of intensification types used by learners compared to native speakers. Learners are limited to the most frequent types and make a very limited use of maximizers, which seem to be a “blind spot”.

Abstract

This paper introduces MultiGEC, a dataset for multilingual Grammatical Error Correction (GEC) in twelve European languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Russian, Slovene, Swedish and Ukrainian. MultiGEC distinguishes itself from previous GEC datasets in that it covers several underrepresented languages, which we argue should be included in resources used to train models for Natural Language Processing tasks which, as GEC itself, have implications for Learner Corpus Research and Second Language Acquisition. Aside from multilingualism, the novelty of the MultiGEC dataset is that it consists of full texts — typically learner essays — rather than individual sentences, making it possible to train systems that take a broader context into account. The dataset was built for MultiGEC-2025, the first shared task in multilingual text-level GEC, but it remains accessible after its competitive phase, serving as a resource to train new error correction systems and perform cross-lingual GEC studies.

This article reviews Variation in University Student Writing: A Communicative Text Type Approach9789027250001