Exploring Machine Learning for Supporting the Recognition of Dyslexia Symptoms in Children During the Literacy Process

• Lui Gill Aquini Federal University of Pelotas http://orcid.org/0009-0009-8852-2498
• Eduarda Pereira Medeiros Federal University of Pelotas https://orcid.org/0009-0001-1603-9503
• Tiago Duarte Mackedanz Federal University of Pelotas https://orcid.org/0009-0004-3770-5570
• Laura Quevedo Jurgina Federal University of Pelotas https://orcid.org/0000-0002-3050-8855
• Tiago Thompsen Primo Federal University of Pelotas https://orcid.org/0000-0003-3870-097X
• Leomar Soares da Rosa Junior Federal University of Pelotas https://orcid.org/0000-0002-7150-5685

DOI: https://doi.org/10.5753/sbie.2024.244968

Abstract

This work aims to develop and integrate a machine learning algorithm into the Alfaba device, an educational tool designed to support the literacy process, especially for students with dyslexia. The algorithm was created using the Affinity Propagation technique to analyze and group common errors in word construction, providing feedback tailored to students' needs. Using synthetic data, the model was tested and demonstrated the ability to identify and cluster writing errors. This study highlights the algorithm's potential to enhance Alfaba's performance, making it an even more robust tool for supporting students with learning difficulties.

Keywords: inclusive education, Affinity Propagation, dyslexia, machine learning

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