Towards an automated estimation of English skill via TOEIC score based on reading analysis

Estimating automatically the degree of language skill by analyzing the eye movements is a promising way to help people from all over the world to learn a new language. In this study, we focus on the English skills of non-native speakers. Our aim is to provide an algorithm that can assess accurately and automatically the TOEIC score after reading English texts for few minutes. As a first step towards this direction, we propose an algorithm that can predict accurately this score after reading and answering some questions about the comprehension of few English texts. We use an eye tracker in order to record the eye gaze, i.e. the positions where the reader is looking at. Then we extract several features to characterize the behavior, and consequently the skill of the reader. We also add a feature based on the number of correct answers to the questions. By using a machine learning based on multivariate regression, the score is estimated user independently. A backward stepwise feature selection is used to select the relevant features and to optimize the estimation. As a main result, the TOEIC score is estimated with 21.7 points of mean absolute error for 21 subjects after reading and answering the questions of only 3 documents.