Corpus Use in Italian Language Pedagogy: Exploring the Effects of Data-Driven Learning provides a comprehensive overview of corpus use in Italian L2 Pedagogy. The author addresses Italian language corpus resources, their potential uses in pedagogical settings, and the range of research methods available to evaluate their effectiveness. Overall, the book: provides a comprehensive account of Italian corpora and corpus-based research on Italian that can inform the design, implementation and evaluation of DDL practices in Italian learning and teaching contexts; traces the history of DDL, by describing its origins and discussing its theoretical underpinnings, in relation to both linguistics and pedagogy; examines the state-of-the-art in DDL research, in light of the available empirical evidence on both etic and emic dimensions, while placing particular emphasis on the methodological gaps; illustrates the main methodological challenges in researching DDL, from corpus resource selection to empirical evaluation of its pedagogical effectiveness, and describes how they can be overcome; demonstrates, by means of an in-depth case study, how the guidelines provided above can be applied when researching DDL effects in a specific second language learning and teaching context; discusses the overall challenges the field faces today, while outlining some desirable avenues for future research and pedagogical practice. This book will not only be of interest to those conducting research in corpus linguistics and teaching in the Italian domain, but also to those working with other languages.

This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions.

This paper summarises the contributions to EuroCALL’s CorpusCALL SIG Symposium for the year 2020. In line with this year’s EuroCALL conference theme, ‘CALL for widening participation’, the Symposium centred around the theme of Data-driven learning for languages other than English. This paper gives a brief overview of developments and challenges when using Data-Driven Learning (DDL) to teach French, German, Italian, and Spanish. As research suggests, a DDL approach has been effectively utilised to teach these languages. However, there are differences in available DDL resources and corpora for the respective languages that are appropriate for language teaching. The main challenges for future developments are also discussed.

This article introduces the CELI corpus, a new learner corpus of written Italian consisting of ca. 600,000 tokens, evenly distributed among CEFR (Common European Framework of Reference for Languages) proficiency levels B1, B2, C1 and C2. The collected texts derive from the language certification exams administered by the University for Foreigners of Perugia all around the world. The corpus contains rich metadata pertaining to text-related and learner-related variables. It expands the domain of learner corpora by being, among other things, both freely available online to the research community, and by focusing on a target language other than English. The article also presents and evaluates the POS-tagging procedure, thus contributing to best practices in learner corpus annotation.

This paper aims to shed light on how research findings stemming from Learner Corpus Research (LCR) can inform the development of Data-driven learning (DDL) pedagogical activities. By doing this, it seeks to show how the gap between corpora built to be used by linguists and those tailored for learners can be filled. It starts by defining what a corpus is and how second language learning studies can benefit from the research findings based on corpora, but also from the direct use of corpora in the classroom. Then, it provides an overview of the available native and learner corpora of Italian, and how corpora in general can be adapted for DDL purposes. Finally, it describes an example of how an LCR finding can be used to develop DDL activities. It concludes with some desiderata for the future.