## 3 Results
### Measuring Attention
#### 3.5.1 Machine Learning Methods  
A study by [PERSON] et al. uses a human-robot interaction system to extract head movement and eye features, then estimate the attention level of the user during three different tasks--reading, writing, and browsing [38]. Visual Focus of Attention (VFOA) is categorised into high, low, average, and no attention. These are classified based on how long the participant's attention was on their work. Video capture was performed with a Raspberry Pi camera and a further external USB camera. HOG-based feature detection is implemented using the dlib library. Four Artificial Neural Networks (ANNs) and an RNN-LSTM are used for classification, all of which were trained with validation accuracies greater than 96% and area under the curve greater than 99%. [PERSON] and [PERSON] developed deep learning models to detect and predict mind-wandering (MW) during reading, achieving an F1 score of 0.45, a 29% improvement over chance (F1 = 0.35) [39]. The models used gaze features such as fixation duration, saccade length, and regressions, segmented into temporal windows. Global gaze features (aggregated metrics) outperformed local features (word-specific metrics) for MW detection. Significant differences between MW and non-MW instances were observed for features such as saccade proportion and fixation duration kurtosis (Cohen's d ranging from \(-\)0.28 to \(-\)0.39, \(p\) < 0.05). These results highlight the decoupling of eye movements from salient content during MW and the potential for the gaze-based detection of attentional lapses in educational settings. [PERSON] et al. investigated the relationship between eye metrics, cognitive load, and fatigue using data from a Tobii Eye Tracker 4C [40]. Participants read and memorised sentences of varying difficulty (Leipzig corpus) and reported fatigue levels on a seven-point Likert scale before and after sessions. A Hidden Markov Model was employed to label saccades, fixations, and noise, and Random Forest Regression was the best-performing model for mapping eye metrics and task performance data (e.g., typing speed, error rate) in terms of cognitive load and fatigue. Key features included blink frequency, eye height, task difficulty, and baseline pupil diameter, with blink frequency showing the strongest correlation to fatigue (r = 0.184, \(p\) = 0.012), although fatigue correlations were generally weak. Cognitive load predictions showed better accuracy, outperforming Monte Carlo baselines. [PERSON] et al. investigated the relationship between performance and attention in humans and neural models during question-answering tasks [41]. Attention similarity, measured via Kullback-Leibler (KL) divergence, showed significant correlations with task performance for LSTMs (r = \(-\)0.73, \(p\) < 0.001) and CNNs (r = \(-\)0.72, \(p\) < 0.001), where greater alignment with human visual attention improved accuracy. In contrast, XLNet achieved high accuracy (91%) but showed weak correlation (r = \(-\)0.16, \(p\) = 0.381), indicating that performance was independent of attention similarity. This highlights the value of human-aligned attention in some models, but not all architectures. Eye movement data have been used with a Random Forest (RF) algorithm to classify thought patterns across nine dimensions, particularly focusing on task relevance--whether attention remains on-task or shifts off-task [42]. Using Particle Swarm Optimisation (PSO), the top-ten eye features linked to task relevance were identified, including fixation duration and saccade metrics (counts, durations, and velocity). The RF model demonstrated that off-task thoughts correlated with increased fixation counts and longer median fixation durations compared to on-task thoughts, reflecting attentional shifts during complex tasks. A related study introduced the Cognitive Load and Eye Analysis Region (CLERA) model, which employs CNNs to classify cognitive load tasks based on blink detection and pupil position, achieving an accuracy of 66.58% [43].