A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies

Abstract

Dyslexia is a specific learning disorder that causes issues related to reading, which affects around 10% of the worldwide population. This can compromise comprehension and memorization skills, and result in anxiety and lack of self-esteem, if no support is provided. Moreover, this support should be highly personalized, to be actually helpful. In this paper, a model to classify the most useful methodologies to support students with dyslexia has been created, with a focus on university alumni. The prediction algorithm is based on supervised machine learning techniques; starting from the issues that dyslexic students experience during their career, it is capable of suggesting customized support digital tools and learning strategies for each of them. The algorithm was trained and tested on data acquired through a self-evaluation questionnaire, which was designed and then spread to more than 1200 university students. It allowed 17 useful tools and 22 useful strategies to be detected. The results of the testing showed an average prediction accuracy higher than 90%, which rises to 94% by renouncing to guess the less-predictable 8 tools/strategies. In the light of this, it is possible to state that the implemented algorithm can achieve the set goal and, thus, reduce the gap between dyslexic and non-dyslexic students. This achievement paves the way for a new modality of facing the problem of dyslexia by university institutions, which aims at modifying teaching activities toward students’ needs, instead of simply reducing their study load or duties. This complies with the definition and the aims of inclusivity.

Which Are the Needs of People with Learning Disorders for Inclusive Museums? Design of OLOS®—An Innovative Audio-Visual Technology

“…It is worth noticing that the usefulness and typology of technology strongly vary according to the needs and issues of the people to whom they are addressed. This article focuses on specific learning disabilities (SLDs), which refer to a group of neurological disorders that impact an individual's ability to process and use information efficiently [19,20], including reading, writing, speaking, doing math, and organizing information. These disorders can affect various aspects of learning, including difficulties with skills such as note-taking, essay organization, and expressing ideas in writing.…”

Deep learning-driven dyslexia detection model using multi-modality data

“…These discoveries may lead to a deeper understanding of the neurological processes behind dyslexia. Physicians can monitor the brain’s electrical activity using the EEG’s high temporal resolution ( Zingoni, Taborri & Calabrò, 2024 ). They can detect sensory, cognitive, or motor events by measuring EEG signals.…”

“…They can detect sensory, cognitive, or motor events by measuring EEG signals. The existing studies show that the DI has different event-related potentials in auditory perception and phonological processing ( Alqahtani, Alzahrani & Ramzan, 2023 ; Asvestopoulou et al, 2019 ; Zingoni, Taborri & Calabrò, 2024 ). Neural processing abnormalities in preliterate children or newborns at risk for dyslexia may be assessed using EEG.…”

“…Typical methods for diagnosing dyslexia tend to focus on the more apparent symptoms, including challenges with reading or spelling. However, these significant symptoms may not reveal the full scope of dyslexia-related cognitive and neurological abnormalities ( Zingoni, Taborri & Calabrò, 2024 ). Traditional techniques frequently employ static images of an individual for DD ( Banfi et al, 2021 ; Zainuddin et al, 2019 ).…”

“…DL models can extract complicated characteristics from EEG and fMRI data ( Asvestopoulou et al, 2019 ; Zingoni, Taborri & Calabrò, 2024 ). These characteristics may include neural activity, brain region interaction, and neurological temporal dynamics.…”