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Disleksija je specifična učna težava, ki jo pri otrocih običajno najprej opazimo kot motnjo branja. Zaradi slabšega razumevanja besedila imajo otroci z disleksijo pogosto tudi druge učne težave, zato je zgodnje prepoznavanje znakov disleksije zelo pomembno za otrokov razvoj. Po predloženih specifikacijah implementirajte namizno aplikacijo za presejalni test disleksije. Aplikacijo naj bo možno uporabljati z napravo za sledenje pogledu. Zaledni del aplikacije naj administratorju omogoča osnovni pregled nad opravljenimi testi, staršem pa omogoči vizualizacijo rezultatov reševanja nalog njihovega otroka. Dyslexia is a specific learning disorder, which usually presents in children as a reading difficulty. Due to poor comprehension of the texts, children with dyslexia often struggle with other learning difficulties as well. Early detection of dyslexia is therefore very important for their development. Implement a desktop application for dyslexia screening test according to provided specifications. The application should support the use of an eye-tracking device. The backend part of the application should provide the administrator with a basic overview of the performed tests, and offer parents a visual summary of their child's achievements.

Disleksija spada med specifične učne težave in je genetsko pogojena. Zgodnje prepoznavanje prvih znakov je zelo pomembno in v diplomskem delu smo pokazali, da lahko z različnim izborom atributov in algoritmi to tudi storimo. Različni računalniški sistemi bi lahko že danes hitro in objektivno odkrili rizične znake za nastanek disleksije v zgodnjih letih in tako bi lahko otrokom nudili ustrezno pomoč že v začetku šolanja, ko težave še niso tako izrazite. V tem diplomskem delu se osredotočimo na prepoznavanje oseb z disleksijo z analizo podatkov pridobljenih iz transkripcije zvočnih posnetkov glasnega branja. Med seboj primerjamo in analiziramo različne algoritme in metode strojnega učenja in podamo rezultate. V nalogi smo ugotovili, da z različnimi algoritmi strojnega učenja že na manjšem številu primerov v učni množici dobro napovemo nagnjenost k disleksiji. Dyslexia is a specific learning difficulty that is genetic in origin. It is very important to predict the predisposition to dyslexia at an early age. In this thesis, we show that we can predict dyslexia with a different attributes and machine learning algorithms. Various computer systems could quickly and objectively detect the predisposition to dyslexia at an early age and thus be able to offer children appropriate help at the very beginning of their education when the problems are not yet noticeable. In this thesis, we focus on the identification of children with dyslexia by analyzing data obtained from the transcription of audio recordings of reading aloud. We compare and analyze different algorithms and machine learning methods and give results. In the thesis, we found that we can predict the predisposition to dyslexia well with different machine learning algorithms even with a small number of cases.