Motivated by this, we present a pilot study to explore the extent to which eye-tracking measures enhance Arabic text readability. We collected the eye movements of 41 participants while reading Arabic texts to provide real-time processing of the text; these giorno were further analyzed and used to build several readability prediction models using different regression algorithms. The findings show an improvement Con the readability prediction task, which requires further investigation. To the best of our knowledge, this work is the first study to explore the relationship between Arabic readability and eye movement patterns.
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Because these texts are basically composed of the words that have been used at the word-level readability prediction and there was a correlation between the readability level of these texts (as measured by the OSMAN tool) and the eye-tracking measures obtained for those texts, we can infer that the generated readability scores at the word level are acceptable.
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Regarding our second question about the features that contribute website the most to Arabic readability prediction, we used the Correlation Ranking Filter Per WEKA. We ranked the features Con the first round based on their correlation with the predicted value (OSMAN score) as shown Per mezzo di Figure 4.
Readability score of a sentence = Total reversed ranking of each word Per mezzo di a sentence/Risposta negativa. of words Durante sentence.
However, Sopra the case of the REPTree, we can observe that it did not perform similarly to the other models, as its risultato decreased when using gaze data.
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Regressions: backward movements to return to earlier areas of text. Short regressions may imply local challenges, whereas more prolonged regressions may indicate the text’s general level of difficulty and lack of clarity [12,39].