Okuma bozukluğunun göz izleme ve makine öğrenmesiyle belirlenmesi: Alanyazının gözden geçirilmesi
Determining reading disorder with eye tracking and machine learning: A review of the literature

Summary/Abstract: Reading disorder, namely dyslexia, is described as the difficulty in the pronunciation and comprehensiondimensions of reading. Studies in which dyslexia, one of the most common learning disorders, are examinedusing a technology-based and innovative technique, eye tracking, are frequently encountered. By means ofeye tracking, the saccade and the fixation of dyslexic readers are reached during reading and analysis areperformed with the obtained physiological data. Thus, the analysis and examination of the reading skills ofindividuals with dyslexia and their reading performance and profiles are revealed. In addition, in recent years,eye tracking and machine learning have started to be applied together in determining whether a reader is dys-lexic or not. This review aimed to analyze and summarize the researches carried out to identify dys-lexicindividuals using eye tracking and machine learning. For this reason, in the article, after the definitions of eyemovements and machine learning algorithms, studies on the detection of dyslexia in readers in four differentlanguages, namely Spanish, Swedish, Greek and Finnish, were summarized. Therefore, it is critical to evaluateindividuals with dyslexia clinically and educationally with physiological data, to diagnose them in the earliestperiod, to apply specific intervention programs, and to prevent academic failure and negative experiences.Thus, the accurate diagnosis can be made without loss of time and economic loss as a result of the applicationof eye tracking and machine learning even if it is complementary by clinical psychologists, guidance, psychological counseling and special education specialists in psychiatry clinics and guidance research centers. Inaddition to studies conducted in four different languages regarding the diagnosis of reading disorders withhigh accuracy using eye tracking and machine learning, individuals with dyslexia whose mother tongue isTurkish can also be evaluated and diagnosed in this way at the earliest age and specific intervention programscan be designed for them.

• Details
• Contents