@inproceedings{8783c5ee524a4c2db44d6341567b4021,
title = "Machine Learning Based Evaluation of Reading and Writing Difficulties",
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.",
keywords = "URAWSS, dysgraphia, dyslexia, evaluation, machine learning",
author = "Mamoru Iwabuchi and Rumi Hirabayashi and Kenryu Nakamura and Dim, {Nem Khan}",
note = "Publisher Copyright: {\textcopyright} 2017 The authors and IOS Press. All rights reserved.",
year = "2017",
doi = "10.3233/978-1-61499-798-6-1001",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1001--1004",
editor = "Peter Cudd and {de Witte}, Luc",
booktitle = "Harnessing the Power of Technology to Improve Lives",
}