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Motivated by the potential benefit of using physiological eye-tracking giorno and the insufficiency of the investigation of these data Per mezzo di text readability prediction, this research proposes incorporating eye-movement data for automatic readability assessment, particularly for the Arabic language. Offering human reading giorno while reading Arabic text could serve different applications Durante addition to readability assessment, which would be a great contribution for Arabic.

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As shown in Table 4, using eye-tracking data Per mezzo di the prediction task improved the correlation coefficient between the predicted and the actual readability scores and decreased all error types. This observation is evident for né-tree-based models, LR, SMO-reg, and MLP, compared to the M5P Tree model. The improvement Per mezzo di the correlation coefficient and the decrease of all error types Per mezzo di the readability prediction task implied that there was a relationship between using eye movement measures and the ability of the used algorithms to model words’ difficulty level more accurately.

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Texts’ aggregated eye-tracking measures and common subjective readabilities assigned by participants.

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To investigate the potential of using eye-tracking features Sopra Arabic readability assessment tasks and gain insight into their usefulness as indicators of Arabic text difficulty, we conducted an exploratory study. We summarize the main steps of the study Per mezzo di Figure 1. The details of the study are as follows:

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