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A Composite Methodology for Supporting Early-Detection of Handwriting Dysgraphia via Big Data Analysis Techniques

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Intelligent Interactive Multimedia Systems and Services 2017 (KES-IIMSS-18 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 76))

Abstract

Handwriting difficulties represent a common cause of underachievement in children’s education and low self-esteem in daily life. Since proper handwriting teaching methods can reduce dysgraphia problems, the evaluation of these methods represents an important task. In this paper a methodology to compare visual and spatio-temporal teaching methods is proposed and applied in order to assess the influence of different teaching approaches on handwriting performance, via big data analysis techniques. Data was collected from children in their final years of primary school, when cursive writing skills have typically been mastered. Qualitative and kinematic parameters were considered: the former were calculated by means of quality checklists, whereas the latter were automatically extracted from digitizing tablet acquisitions. Results showed significant differences in pupils’ handwriting depending on the teaching method applied.

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Author information

Authors and Affiliations

  1. DIA Department, University of Trieste, Trieste, Italy

    Pierluigi D’Antrassi, Alfredo Cuzzocrea & Agostino Accardo

  2. DCD Department, ULSS 7, Treviso, Italy

    Iolanda Perrone

  3. ICAR-CNR, Rende, Italy

    Alfredo Cuzzocrea

Authors
  1. Pierluigi D’Antrassi
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  2. Iolanda Perrone
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  3. Alfredo Cuzzocrea
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  4. Agostino Accardo
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. National Research Council of Italy (CNR-ICAR), Institute for High-Performance Computing and Networking, Naples, Italy

    Giuseppe De Pietro

  2. National Research Council of Italy (CNR-ICAR), Institute for High-Performance Computing and Networking, Naples, Italy

    Luigi Gallo

  3. Fern Barrow, Bournemouth University, Poole, Dorset, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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D’Antrassi, P., Perrone, I., Cuzzocrea, A., Accardo, A. (2018). A Composite Methodology for Supporting Early-Detection of Handwriting Dysgraphia via Big Data Analysis Techniques. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-59480-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59479-8

  • Online ISBN: 978-3-319-59480-4

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