Literature DB >> 28873918

Machine Learning Based Evaluation of Reading and Writing Difficulties.

Mamoru Iwabuchi1, Rumi Hirabayashi1, Kenryu Nakamura1, Nem Khan Dim2.   

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.

Entities:  

Keywords:  URAWSS; dysgraphia; dyslexia; evaluation; machine learning

Mesh:

Year:  2017        PMID: 28873918

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  1 in total

1.  Eye tracking based dyslexia detection using a holistic approach.

Authors:  Boris Nerušil; Jaroslav Polec; Juraj Škunda; Juraj Kačur
Journal:  Sci Rep       Date:  2021-08-03       Impact factor: 4.379

  1 in total

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