Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning
Reading acquisition is one the main keys for school success and a crucial component for empowering individuals to participate meaningfully in society. Yet, it is still a challenging skill to acquire for around 10% of children that have dyslexia, a type of neuro-developmental disorder that affects th...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10877811/ |
| _version_ | 1826799776974241792 |
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| author | Maria Rauschenberger Ricardo Baeza-Yates Luz Rello |
| author_facet | Maria Rauschenberger Ricardo Baeza-Yates Luz Rello |
| author_sort | Maria Rauschenberger |
| collection | DOAJ |
| description | Reading acquisition is one the main keys for school success and a crucial component for empowering individuals to participate meaningfully in society. Yet, it is still a challenging skill to acquire for around 10% of children that have dyslexia, a type of neuro-developmental disorder that affects the ability to learn how to read and write. Dyslexia is often under-diagnosed, and normally children with dyslexia are only detected once they fail in school, even though dyslexia is not related to general intelligence. In this work, we present an approach for screening dyslexia using language-independent games in combination with machine learning models. To reach this goal, we designed the content of a computer game, collected data from 137 children playing this game (51 with dyslexia) in different languages -German, Spanish and English- and created a prediction model using different machine learning classifiers. Our method provides a precision of 0.78 and recall of 0.79 for German and a precision of 0.83 and recall of 0.80 for all languages when Extra Trees are used, with an accuracy of 0.67 and 0.75, respectively. Our results open the possibility of inexpensive online early screening of dyslexia for young children using non-linguistic elements. |
| first_indexed | 2025-03-17T00:40:28Z |
| format | Article |
| id | doaj.art-2b452fddb6ed44888a00ffacfdfc17b3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-03-17T00:40:28Z |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2b452fddb6ed44888a00ffacfdfc17b32025-02-21T00:01:55ZengIEEEIEEE Access2169-35362025-01-0113295412955310.1109/ACCESS.2025.353971910877811Screening Dyslexia Using Visual Auditory Computer Games and Machine LearningMaria Rauschenberger0Ricardo Baeza-Yates1https://orcid.org/0000-0003-3208-9778Luz Rello2https://orcid.org/0000-0003-0886-026XFaculty of Technology, University of Applied Science Emden/Leer, Emden, GermanyInstitute for Experiential AI, Northeastern University at Silicon Valley, San Jose, CA, USAIE Business School, IE University, Madrid, SpainReading acquisition is one the main keys for school success and a crucial component for empowering individuals to participate meaningfully in society. Yet, it is still a challenging skill to acquire for around 10% of children that have dyslexia, a type of neuro-developmental disorder that affects the ability to learn how to read and write. Dyslexia is often under-diagnosed, and normally children with dyslexia are only detected once they fail in school, even though dyslexia is not related to general intelligence. In this work, we present an approach for screening dyslexia using language-independent games in combination with machine learning models. To reach this goal, we designed the content of a computer game, collected data from 137 children playing this game (51 with dyslexia) in different languages -German, Spanish and English- and created a prediction model using different machine learning classifiers. Our method provides a precision of 0.78 and recall of 0.79 for German and a precision of 0.83 and recall of 0.80 for all languages when Extra Trees are used, with an accuracy of 0.67 and 0.75, respectively. Our results open the possibility of inexpensive online early screening of dyslexia for young children using non-linguistic elements.https://ieeexplore.ieee.org/document/10877811/Dyslexialanguage disorderlanguage-independencemachine learningreading disorderdyslexia screening |
| spellingShingle | Maria Rauschenberger Ricardo Baeza-Yates Luz Rello Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning IEEE Access Dyslexia language disorder language-independence machine learning reading disorder dyslexia screening |
| title | Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning |
| title_full | Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning |
| title_fullStr | Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning |
| title_full_unstemmed | Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning |
| title_short | Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning |
| title_sort | screening dyslexia using visual auditory computer games and machine learning |
| topic | Dyslexia language disorder language-independence machine learning reading disorder dyslexia screening |
| url | https://ieeexplore.ieee.org/document/10877811/ |
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