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European Conference on Technology Enhanced Learning

EC-TEL 2017: Data Driven Approaches in Digital Education pp 43–53Cite as

How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10474))

Abstract

Starting from the presumption that writing style is proven to be a reliable predictor of comprehension, this paper investigates the extent to which textual complexity features of nurse students’ essays are related to the scores they were given. Thus, forty essays about case studies on infectious diseases written in French language were analyzed using ReaderBench, a multi-purpose framework relying on advanced Natural Language Processing techniques which provides a wide range of textual complexity indices. While the linear regression model was significant, a Discriminant Function Analysis was capable of classifying students with an 82.5% accuracy into high and low performing groups. Overall, our statistical analysis highlights essay features centered on document cohesion flow and dialogism that are predictive of teachers’ scoring processes. As text complexity strongly influences learners’ reading and understanding, our approach can be easily extended in future developments to e-portfolios assessment, in order to provide customized feedback to students.

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Acknowledgements

The authors wish to thank Patrice Lombardo, head of the IFSI, Centre Hospitalier Annecy-Genevois, who helped make this research possible, and Jean-Luc Rinaudo, University of Rouen, for his valuable input all along the phases of this research. This research was partially supported by the FP7 2008-212578 LTfLL project, by the 644187 EC H2020 Realising an Applied Gaming Eco-system (RAGE) project, as well as by University Politehnica of Bucharest through the “Excellence Research Grants” Programs UPB–GEX 12/26.09.2016.

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Authors and Affiliations

  1. University Politehnica of Bucharest, Bucharest, Romania

    Mihai Dascalu & Stefan Trausan-Matu

  2. Academy of Romanian Scientists, Splaiul Independenţei 54, 050094, Bucharest, Romania

    Mihai Dascalu & Stefan Trausan-Matu

  3. Laboratoire des Sciences de l’Éducation, Univ. Grenoble Alpes, 38000, Grenoble, France

    Mihai Dascalu & Philippe Dessus

  4. IFSI, Centre Hospitalier Annecy-Genevois, 74374, Metz-Tessy, Pringy, France

    Laurent Thuez

Authors
  1. Mihai Dascalu
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  2. Philippe Dessus
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  3. Laurent Thuez
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  4. Stefan Trausan-Matu
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Correspondence to Mihai Dascalu .

Editor information

Editors and Affiliations

  1. Jean Moulin Lyon 3 University, Lyon, France

    Élise Lavoué

  2. German Institute for International Educational Research, Goethe University, Frankfurt, Germany

    Hendrik Drachsler

  3. KU Leuven, Leuven, Belgium

    Katrien Verbert

  4. University of Toulouse, Toulouse, France

    Julien Broisin

  5. Pontificia Universidad, Santiago de Chile, Chile

    Mar Pérez-Sanagustín

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Dascalu, M., Dessus, P., Thuez, L., Trausan-Matu, S. (2017). How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science(), vol 10474. Springer, Cham. https://doi.org/10.1007/978-3-319-66610-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-66610-5_4

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  • Online ISBN: 978-3-319-66610-5

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