Identifying Chinese Children with Dyslexia Using Machine Learning with Character Dictation

ABSTRACT

Purpose

Dyslexia is characterized by its diverse causes and heterogeneous manifestations. Chinese children with dyslexia exhibit orthographic, phonological, and semantic deficits across character and radical levels when writing. However, whether character dictation can be used to distinguish children with dyslexia from their typically developing peers remains unexplored.

Method

A dataset of written characters from 1,015 Chinese children with and without dyslexia from Grades 2–6 was used to train multiple machine models with different learning algorithms.

Results

The multi-level multidimensional model reached a predictive accuracy of 78.0%, with stroke, grade, lexicality, and character configuration manifesting as the most predictive features. The accuracy of the model improved to 80.0% when only these features were included.

Conclusion

These results not only provide evidence for the multidimensional causes of Chinese dyslexia, but also highlight the utility of machine learning in distinguishing children with dyslexia from their peers via Chinese dictation, which elucidates a promising area of future research.

Acknowledgments

We thank the Research Fellow Scheme (RFS 2021-7H05) of the Hong Kong Government Research Grant Council for its funding to Shelley Xiuli Tong. We are grateful to the children and their parents who participated in our study. We thank all of the undergraduate assistants from the Speech, Language & Reading Lab for their help in data encoding. Additionally, we appreciate John Martino and Justine Wai’s proofreading of the original draft of our manuscript.

Disclosure statement

Stephen Man Kit Lee and Hey Wing Liu contributed equally to this paper. No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by funding from General Research Fund (17673216) and Research Fellow Scheme (RFS2021-7H05), from Hong Kong Government Resarch Grant Council to Shelley Xiuli Tong.