Use of recommendation models to provide support to dyslexic students
Author(s)
Date Issued
2024
Type
article
Volume
249
Start Page
123738
Journal
Abstract
Dyslexia is the most widespread specific learning disorder and significantly
impair different cognitive domains. This, in turn, negatively affects dyslexic
students during their learning path. Therefore, specific support must be given
to these students. In addition, such a support must be highly personalized,
since the problems generated by the disorder can be very different from one to
another. In this work, we explored the possibility of using AI to suggest the
most suitable supporting tools for dyslexic students, so as to provide a
targeted help that can be of real utility. To do this, we relied on
recommendation algorithms, which are a branch of machine learning, that aim to
detect personal preferences and provide the most suitable suggestions. We hence
implemented and trained three collaborative-filtering recommendation models,
namely an item-based, a user-based and a weighted-hybrid model, and studied
their performance on a large database of 1237 students' information, collected
with a self-evaluating questionnaire regarding all the most used supporting
strategies and digital tools. Each recommendation model was tested with three
different similarity metrics, namely Pearson correlation, Euclidean distance
and Cosine similarity. The obtained results showed that a recommendation system
is highly effective in suggesting the optimal help tools/strategies for
everyone. This demonstrates that the proposed approach is successful and can be
used as a new and effective methodology to support students with dyslexia.