Can a Support Vector Machine identify poor performance of dyslectic children playing a serious game?

Detta är en Master-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: This paper has been a part of developing the serious game Kunna, a web-based game with exercises targeting children diagnosed with dyslexia. This game currently consists of five different exercises aiming to practice reading and writing without a therapist or neuropsychologist present. As Kunna can be used anywhere, tools are needed to understand each individual's capacities and difficulties. Hence, this paper aims to present how a serious game and a support vector machine were used to identify children that performed poorly in Kunna’s exercises. Though, due to the current corona pandemic, Kunna could only be tested on children not diagnosed with dyslexia. Therefore, this paper should be seen as a proof of concept. As an initial step, several variables were identified to measure the performance of dyslectic children. Secondly, the variables were implemented into Kunna and tested on 16 Spanish-speaking children. The results were analyzed to identify how poor performance could be recognized using the identified variables. As a final step, the data was divided into two groups for each exercise, of which one group contained participants who appear to perform poorly. These were participants with clearly outlying values in the number of errors and duration. Thus, to train and evaluate if a Support Vector Machine (SVM) can separate the two groups and thereby identify the participants who performed poorly. From the discussion followed that the SVM is not the most efficient choice for this aim. Instead, it is suggested that future work should consider multiclassification algorithms. 

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