## 3 Results
### Measuring Comprehension
#### 3.4.2 Behavioural Metrics Derived from Eye Movements  
In addition to multiple-choice questions, behavioural metrics from eye movements have been used to predict comprehension using advanced machine learning techniques. For example, [PERSON] et al. employed a Siamese Convolutional Neural Network (SCNN) to predict reading comprehension levels based on cognitive patterns observed during reading [33]. The study, which used fixation count heatmaps captured by a Tobii Pro Spectrum eye tracker, successfully predicted participants' English proficiency based on their performance on the CET-6 exam. The SCNN model proved effective with both \"trained from scratch\" and \"pretrained with fine-tuning\" strategies.  
[PERSON] et al. [34] demonstrated that using ensemble methods significantly improved the accuracy of identifying problematic text during questionnaire completion. For sentence-level prediction, combining features such as the number of regressions and fixation duration per letter achieved an accuracy of up to 74% when using J48 classifiers with bagging. For word-level analysis, SVM with boosting achieved an accuracy of 76%, with the number of fixations and regressions being the most predictive features. These results highlight that integrating weaker features like pupil dilation with strong predictors can further enhance classification performance.  
[PERSON] et al. explored the use of time-frequency representations of eye blinks to investigate task difficulty during mental arithmetic tasks [35]. It found that blink entropy, a novel metric derived from the time-frequency representations of eye-blink patterns, was significantly correlated with both perceived task difficulty ([PERSON]'s r = \(-\)0.341, \(p\) = 0.012) and correct answer rates during challenging tasks (r = 0.437, \(p\) = 0.07). Moreover, the use of LSTM models trained on blink spectrograms yielded higher classification accuracy for task difficulty (77.8%) compared to traditional blink metrics such as blink rate or duration, demonstrating the potential of advanced eye-blink analysis for cognitive state monitoring.  
[PERSON] et al. demonstrated that gaze-based predictive models can assess deep reading comprehension with significant accuracy [36]. Using linear regression (LR) and random forest (RF) models, they found moderate correlations between predicted and human-scored self-explanation (SE) scores: r = 0.322 (Study 1) and r = 0.354 (Study 2), with \(p\) < 0.001 for both. Eye-tracking metrics such as fixation duration, saccade distance, and regressions significantly contributed to these predictions. The SE model also predicted delayed post-test comprehension, particularly inference-level comprehension after one week, with correlations up to r = 0.35. These findings suggest that gaze metrics can reliably differentiate deep from shallow comprehension, offering generalisable insights into reading behaviour across participants. However, this study was ranked low quality in the MMAT as it was unclear if the data were representative of the target population, confounders were not accounted for, and there were no complete outcome data.  
A study by [PERSON] et al. used both postural and eye movement recordings to measure changes in cognitive engagement while reading text [37]. Two eye metrics, total fixation time and individual fixation duration, were extracted by the eye-tracking system, while a motion capture system was used to record head movements. Participants were informed about specific topics that would be referred to in parts of the texts that they read. Memory recall was tested for both task-relevant and task-irrelevant text. Task-relevant text had longer total fixation time and better memory recall. Total fixation time and distance-from-screen both strongly correlated with memory performance. As participants get 'into the flow of reading', fixations tend to become shorter. It should be noted that individual fixation duration is not reduced for task-relevant text over time.