We used each algorithm to build two models Durante two rounds. We used all the 186-word types Durante our vocabulary Durante both rounds but with different features Sopra the prediction analysis. Per mezzo di the first round, we used the features extracted from OSMAN by having the linguistic features No_of_Syllables and Is_Faseeh as predictor variables and the OSMAN score as the predicted value. Per mezzo di the second round, for each word, we used the same features as we did Per mezzo di the first round but combined them with the extracted eye movement measures. Similarly, the OSMAN score was the predicted value.
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Saccades: the participant’s swift eye motions between areas of text. Longer click here saccades imply text that is straightforward and easily comprehended; short saccades suggest a greater challenge and time required to digest the language [40].
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We asked participants to read aloud to help us guarantee that they actually read the text and to enable us to hear how fast and how well they were reading. Each participant read all of the easy- and difficult-to-read texts. Con order to minimize any possible influence of text order on reading [8,67], we randomized the order of the displayed texts Per mezzo di each session.
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There has been a growing interest Per Arabic language processing and translation. Still, despite this increasing interest, only a few researchers have tackled the problem of finding a proper Arabic readability index [29]. Similar to English and other languages, research on Arabic readability of texts for first-language readers has started with the development of readability formulas and progressed to the use of statistical analysis approaches and machine learning algorithms for classification [30]. A summary of these formulas is illustrated Per mezzo di Table 1.