Stefania Spina

Prof. Stefania Spina

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Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 December 2019 in Language Learning
Verified
Journal Article
Closed access
PDF file
ASAnna Siyanova-Chanturia
Anna Siyanova-Chanturia
SSStefania Spina
Stefania Spina
18 October 2020 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Valentino SantucciValentino Santucci
Valentino Santucci
FSFilippo Santarelli
Filippo Santarelli
LFLuciana Forti
Luciana Forti
SSStefania Spina
Stefania Spina
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.
20 August 2018 in Discourse Processes
Verified
Journal Article
Closed access
SSStefania Spina
Stefania Spina
Emoticons play a key role in digital written interactions. Since the 1980s research has highlighted their growing relevance, as they allow to convey increasingly rich emotional, social, and pragmatic information. This article contributes to this area of research by providing an analysis of emoticons as structural markers in Twitter interactions. Based on a large corpus of Italian tweets, mixed-effect models were used to investigate to what extent and how emoticons are used in this role and what variables most influence their use and their relationship with punctuation marks. Results indicate that emoticons often have the function of clause and sentence boundary marking, either replacing or integrating punctuation marks. Variables affecting their use included user’s age, emoticon type, and position within the tweets. In this role their use reveals the pursuit of coherent strategies in discourse organization.
1 January 2014
Verified
Book Chapter
Open access
SSStefania Spina
Stefania Spina
31 December 2021
Verified
Book Chapter
Closed access
TOTaha Omidian
Taha Omidian
ASAnna Siyanova-Chanturia
Anna Siyanova-Chanturia
SSStefania Spina
Stefania Spina
8 Development of Formulaic Knowledge in Learner Writing: A Longitudinal Perspective was published in Perspectives on the L2 Phrasicon on page 178.
22 April 2020 in Educazione Linguistica. Language Education
Verified
Journal Article
Open access
LFLuciana Forti
Luciana Forti
SSStefania Spina
Stefania Spina
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.