Predicting misreadings from gaze in children with reading difficulties
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Predicting misreadings from gaze in children with reading difficulties. / Bingel, Joachim; Barrett, Maria; Klerke, Sigrid.
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications,. Association for Computational Linguistics, 2018. p. 29-34.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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 -
ID: 217116273