VR-based Silent Reading and Rosenberg Tests: Machine-Learning Approach to Identify Learning Disorders
Author(s)
Materazzini, Michele
Morciano, Gianluca
Alcalde-Llergo, José Manuel
Yeguas-Bolivar, Enrique
Date Issued
2024
Type
conferenceObject
Start Page
541
End Page
546
Abstract
This study investigates the self-esteem in
individuals with specific learning disorders (SLDs) by the
virtualization of well-established clinical scale in virtual reality
(VR) environment. In particular, the Rosenberg Self-Esteem
Scale has been employed. Participants included young adults
aged 20 to 34 years diagnosed with dyslexia, dyscalculia, or
dysgraphia, alongside typically developing peers. Results
revealed that individuals with SLDs took 40% longer to
complete VR tasks compared to typically developing peers but
showed no statistical difference self-esteem scores on the
Rosenberg Self-Esteem Scale. Further research will include
further virtualized diagnostic tools, as well as the application of
machine-learning algorithms to discriminate between different
cohort of subjects.
Conference(s)
2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)