## 3 Results
### Measuring Comprehension  
#### 3.4.1 Multiple-Choice Questions  
One common method for measuring comprehension involves displaying text to participants, followed by multiple-choice questions. For example, the role of task-conditioned gaze behaviour in comprehension was explored in a study by [PERSON] et al., which utilised the EyeLink 1000 Plus eye tracker to analyse the eye movements of 269 participants reading passages from the OneStop QA dataset [26]. The study found that pre-questioning enhanced focus on task-relevant content, suggesting that comprehension is closely linked to focused attention. By combining eye-tracking metrics with a RoBERTa machine learning model, the study aimed to bridge human and machine reading comprehension, using multiple-choice questions and reading time as key measures of comprehension. Research by [PERSON] et al. provided insights into the role of refutation text in reading comprehension [27]. Using the Tobii T120 eye tracker, the study evaluated 40 university students reading scientific texts in Italian. The experiment compared standard (non-refutation) and refutation formats across four passages, examining eye movement metrics such as first-pass progressive fixation time, forward fixations, reinspection time, and look-back fixation time. Readers of refutation texts showed better comprehension (mean score 5.33 vs. 4.35, \(p\) = 0.018)) and faster initial processing (first-pass fixation: 1.23 ms/character vs. 1.48 ms/character, \(p\) = 0.011) than those reading non-refutation texts. Refutation texts also prompted more integrative rereading (look-back fixation: 2.48 ms/character vs. 2.12 ms/character, \(p\) = 0.029). [PERSON] et al. investigated the prediction of comprehension difficulty using a combination of eye-tracking and machine learning techniques [7]. Employing the Tobii 5L desktop eye tracker, the study analysed gaze behaviour, reading time, and revisit frequency to assess comprehension performance. Participants read English text paragraphs of varying difficulty levels, and comprehension was evaluated using multiple-choice questions. Logistic regression achieved 72% accuracy in predicting comprehension difficulty, with additional analysis performed using Linear Discriminant Analysis, highlighting the effectiveness of eye-tracking data in identifying comprehension challenges. A study by [PERSON] et al. (2015) also investigated the relationship between eye movements and text comprehension during reading [28]. Results demonstrated that difficult texts led to longer fixation durations (202 ms vs. 220 ms, \(p\) = 0.003), stronger power-law scaling for fixations (0.55 vs. 0.58, \(p\) = 0.008), and slower gaze step sizes (0.01 vs. 0.02, \(p\) = 0.025). Notably, power-law scaling at slower time scales emerged as a significant predictor of comprehension (\(p\) = 0.013), outperforming traditional metrics such as fixation count and regressions. These findings suggest that cognitive coordination, captured through power-law scaling, plays a critical role in comprehension during complex reading tasks, highlighting its potential as a superior measure compared to standard eye-tracking metrics. Similarly, [PERSON] et al. focused on classifying workload levels using microsaccades measured by the Tobii Pro Spectrum eye tracker [29]. Although the primary aim was workload classification, the study incorporated comprehension quizzes to establish a baseline for reading performance. SVMs were utilised to evaluate microsaccade patterns and their relation to cognitive effort during reading tasks. The findings indicated no statistically significant differences in reading comprehension related to workload levels, yet the inclusion of comprehension assessments added depth to the analysis. The relationship between eye movements and comprehension was further explored by [PERSON] et al., who investigated the correlation between consumer-grade eye-tracking data and comprehension performance [30].