Machine Learning Based Evaluation of Reading and Writing Difficulties

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

doi:10.3233/978-1-61499-798-6-1001

Machine Learning Based Evaluation of Reading and Writing Difficulties

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 language	English
Title of host publication	Harnessing the Power of Technology to Improve Lives
Editors	Peter Cudd, Luc de Witte
Publisher	IOS Press
Pages	1001-1004
Number of pages	4
ISBN (Electronic)	9781614997979
DOIs	https://doi.org/10.3233/978-1-61499-798-6-1001
Publication status	Published - 2017
Externally published	Yes

Publication series

Name	Studies in Health Technology and Informatics
Volume	242
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Keywords

URAWSS
dysgraphia
dyslexia
evaluation
machine learning

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

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10.3233/978-1-61499-798-6-1001

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Machine Learning Based Evaluation of Reading and Writing Difficulties. / Iwabuchi, Mamoru; Hirabayashi, Rumi; Nakamura, Kenryu et al.

Harnessing the Power of Technology to Improve Lives. ed. / Peter Cudd; Luc de Witte. IOS Press, 2017. p. 1001-1004 (Studies in Health Technology and Informatics; Vol. 242).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Nakamura, Kenryu

AU - Dim, Nem Khan

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AB - 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.

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KW - dysgraphia

KW - dyslexia

KW - evaluation

KW - machine learning

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ER -

Machine Learning Based Evaluation of Reading and Writing Difficulties

Abstract

Publication series

Keywords

ASJC Scopus subject areas

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