# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 4 Materials and Methods
### Pre-Processing of the Eye-Tracking Data  
We used SR Data Viewer software to display, manipulate, and analyze the data collected by the eye tracker during the experiments. Although the majority of reviewed studies have focused on assessing readability at the document and sentence level, we narrowed our attention to measure readability on the word level. The reason is that based on the observed behaviors of the participants during reading and based on the feedback or readability judgment from the participants, difficulty in reading was sourced primarily to the difficulty of the words. The participants did not struggle with the texts' structure, even though the participants may have been unfamiliar with the structure. Thus, for each text, we tokenized all texts word by word by defining each word to be an interest area (IA), which is the area under analysis in the screen [10]. There were 221 tokens in total and a vocabulary size of 186 words.  
Additionally, based on the observed behavior of some participants who scanned the screen randomly and looked at white space at the beginning of the reading, we removed these fixations as they were not part of any interest areas. Additionally, we adjusted drifted fixation events manually to match the interest areas. Based on previous research, \"if a portion of text causes the reader difficulty, then this difficulty can spill over and affect processing of subsequent text\" [64]. Thus, we did not remove stop words in order to capture the effect of reading easy and difficult words. Additionally, although there have been some suggestions of merging fixations of less than a minimum threshold, we limited ourselves to the manual correction of drifted fixation events to avoid any loss of significant data.  
Figure 2 shows examples of the output of the eye tracker during a participant's readings of two texts with different readability levels. The text on the right has a higher readability score compared with the text on the left. Each orange box represents an interest area--a word, in our case. The red circles represent the fixations, where the size of the circle correlates with the time spent in this fixation (fixation duration), and the yellow arrows represent the saccades. Note that the Arabic language is read from right-to-left.  
As shown in Figure 2, a less readable text has shorter saccades, more fixations, and more regression, which may imply reading difficulty. More specifically, they demonstrate that it is possible to establish the frequency, duration, and exact times of word fixations during reading, along with data related to revisited text when challenges arise [12].  
Additionally, eye fixations could be seen in fixation maps or heatmaps (Figure 3) that aggregate the fixations of all the participants when reading texts with low and high readability scores and show the words that are fixated upon and the duration of these fixations.  
Figure 3: Examples of heatmaps built from two texts by averaging the fixations of all participants when looking at the two texts. The text on the right is a text, which is a poem written in the Andalusian era, with a higher readability score than the text on the left, which is a poem written in the pre-Islamic era.  
Figure 2: Examples of the eye-tracker output from two texts that show fixations and saccades of one of the participants when reading the two texts. The text on the right is a poem written in the Andalusian era with a higher readability score than the text on the left, which is a poem written in the pre-Islamic era. Orange boxes represent interest areas, the red circles represent the fixations, the numbers above these fixations represent their durations, and the yellow arrows represent the saccades.