Rating the Acquisition of Pre-writing Skills in Children: An Analysis Based on Computer Vision and Data Mining Techniques in the Ecuadorian Context

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Pre-writing skills are a set of essential skills to learn to write. Commonly, in South America’s public schools, a teacher has a class with approximately 30 or more students. As a result, the teacher has the challenging task to detect if a child has difficulties in pre-writing essential activities. In light of the above, in this paper, we present an analysis to determine the feasibility of using computer vision and data mining techniques to determine if a child fails to meet, meets few, or meets a pre-writing skill. We conducted the process with the open corpus “UPS-Writing-Skills,” containing the HU moments and the shape signature descriptors extracted from a collection of 358 images drawn by children.

Original languageEnglish
Title of host publicationApplied Informatics - 5th International Conference, ICAI 2022, Proceedings
EditorsHector Florez, Henry Gomez
PublisherSpringer Science and Business Media Deutschland GmbH
Pages308-323
Number of pages16
ISBN (Print)9783031196461
DOIs
StatePublished - 2022
Event5th International Conference on Applied Informatics, ICAI 2022 - Arequipa, Peru
Duration: 27 Oct 202229 Oct 2022

Publication series

NameCommunications in Computer and Information Science
Volume1643 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference5th International Conference on Applied Informatics, ICAI 2022
Country/TerritoryPeru
CityArequipa
Period27/10/2229/10/22

Bibliographical note

Funding Information:
Acknowledgments. This work has been funded by the “Sistemas Inteligentes de Soporte a la Educación (v5)” research project, the Cátedra UNESCO “Tecnologías de apoyo para la Inclusión Educativa” initiative, and the Research Group on Artificial Intelligence and Assistive Technologies (GI-IATa) of the Universidad Politécnica Salesiana, Campus Cuenca.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Adaboost
  • Data mining
  • Education
  • Hu moments
  • Naïve Bayes
  • Pre-writing skills
  • Random Forest
  • Shape signatures

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