Name
Abstract | ||
|---|---|---|
More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes.. Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3194658.3194675 | INTERNATIONAL DAIRY JOURNAL |
Keywords | Field | DocType |
Dyslexia,screening,early detection,diagnosis,linguistics,serious games,machine learning | Population,Early detection,Working memory,Psychology,Cognitive skill,Artificial intelligence,Statistical model,Executive functions,Perception,Machine learning,Dyslexia | Conference |
Citations | PageRank | References |
1 | 0.38 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luz Rello | 1 | 22 | 3.69 |
| Enrique Romero | 2 | 2 | 1.43 |
| Maria Rauschenberger | 3 | 31 | 7.49 |
| Abdullah Ali | 4 | 52 | 8.30 |
| Kristin Williams | 5 | 19 | 5.30 |
| jeffrey p bigham | 6 | 2647 | 189.29 |
| Nancy Cushen White | 7 | 5 | 1.03 |