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Abstract

In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students’ written productions. Beyond reporting classification results for different scenarios, we explore the connection between the most predictive features and the teaching curriculum, finding that our set of linguistic features often reflect the explicit instructions that students receive during each course.


Citation
@inproceedings{miaschi-etal-2020-tracking,
    title = "Tracking the Evolution of Written Language Competence in {L}2 {S}panish Learners",
    author = "Miaschi, Alessio  and
      Davidson, Sam  and
      Brunato, Dominique  and
      Dell{'}Orletta, Felice  and
      Sagae, Kenji  and
      Sanchez-Gutierrez, Claudia Helena  and
      Venturi, Giulia",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.9",
    pages = "92--101",
    abstract = "In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students{'} written productions. Beyond reporting classification results for different scenarios, we explore the connection between the most predictive features and the teaching curriculum, finding that our set of linguistic features often reflect the explicit instructions that students receive during each course.",
}