Predicting risk of dyslexia with an online gamified test
June 07, 2019 Β· Declared Dead Β· π PLoS ONE
"No code URL or promise found in abstract"
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Authors
Luz Rello, Ricardo Baeza-Yates, Abdullah Ali, Jeffrey P. Bigham, Miquel Serra
arXiv ID
1906.03168
Category
cs.HC: Human-Computer Interaction
Citations
58
Venue
PLoS ONE
Last Checked
3 months ago
Abstract
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -- a tablet instead of a desktop computer -- reaching a recall of over 72% for the class with dyslexia for children 9 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool based on our methods has already been used by more than 200,000 people.
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