Machine Learning Based Evaluation of Reading and Writing Difficulties

Mamoru Iwabuchi, Rumi Hirabayashi, Kenryu Nakamura, Nem Khan Dim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The possibility of auto evaluation of reading and writing difficulties was investigated using non-parametric machine learning (ML) regression technique for URAWSS (Understanding Reading and Writing Skills of Schoolchildren) [1] test data of 168 children of grade 1-9. The result showed that the ML had better prediction than the ordinary rule-based decision.

Original languageEnglish
Title of host publicationHarnessing the Power of Technology to Improve Lives
EditorsPeter Cudd, Luc de Witte
PublisherIOS Press
Pages1001-1004
Number of pages4
ISBN (Electronic)9781614997979
DOIs
Publication statusPublished - 2017 Jan 1
Externally publishedYes

Publication series

NameStudies in Health Technology and Informatics
Volume242
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

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Keywords

  • dysgraphia
  • dyslexia
  • evaluation
  • machine learning
  • URAWSS

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Iwabuchi, M., Hirabayashi, R., Nakamura, K., & Dim, N. K. (2017). Machine Learning Based Evaluation of Reading and Writing Difficulties. In P. Cudd, & L. de Witte (Eds.), Harnessing the Power of Technology to Improve Lives (pp. 1001-1004). (Studies in Health Technology and Informatics; Vol. 242). IOS Press. https://doi.org/10.3233/978-1-61499-798-6-1001