Predicting misreadings from gaze in children with reading difficulties

TY - GEN

T1 - Predicting misreadings from gaze in children with reading difficulties

AU - Bingel, Joachim

AU - Barrett, Maria

AU - Klerke, Sigrid

PY - 2018

Y1 - 2018

N2 - We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching. Our approach employs sev-eral linguistic and gaze-based features to in-form an ensemble of different classifiers, in-cluding multi-task learning models that let us transfer knowledge about individual readers to attain better predictions. Notably, the data we use in this work stems from noisy readings in the wild, outside of controlled lab condi-tions. Our experiments show that despite the noise and despite the small fraction of mis-readings, gaze data improves the performance more than any other feature group and our models achieve good performance. We further show that gaze patterns for misread words do not fully generalize across readers, but that we can transfer some knowledge between readers using multitask learning at least in some cases. Applications of our models include partial au-tomation of reading assessment as well as per-sonalized text simplification.

AB - We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching. Our approach employs sev-eral linguistic and gaze-based features to in-form an ensemble of different classifiers, in-cluding multi-task learning models that let us transfer knowledge about individual readers to attain better predictions. Notably, the data we use in this work stems from noisy readings in the wild, outside of controlled lab condi-tions. Our experiments show that despite the noise and despite the small fraction of mis-readings, gaze data improves the performance more than any other feature group and our models achieve good performance. We further show that gaze patterns for misread words do not fully generalize across readers, but that we can transfer some knowledge between readers using multitask learning at least in some cases. Applications of our models include partial au-tomation of reading assessment as well as per-sonalized text simplification.

U2 - 10.18653/v1/w18-0503

DO - 10.18653/v1/w18-0503

M3 - Article in proceedings

SP - 29

EP - 34

BT - Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications,

PB - Association for Computational Linguistics

T2 - 13h Workshop on Innovative Use of NLP for Building Educational Applications

Y2 - 5 June 2018 through 5 June 2018

ER -