# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
### Application of the Use of Eye Tracking Technology  
The cognitive procedure in the process of visual tracking of images, texts or situations in natural contexts is based on the stimulus-processing-response structure. Information enters via the visual pathway (retina-fovea) and is processed at the level of the subcortical and cortical regions within the central nervous system. This processing results in a sensory stimulation response. Specifically, saccades are a form of sensory-to-motor transformation from a stimulus that has been found to be significant. Saccadic eye movements are used to redirect the fovea from one point of interest to another. Fixation is also used to keep the fovea aligned on the target during subsequent analysis of the stimulus. This alternative saccade-fixation behaviour is repeated several hundred thousand times a day and is essential in complex behaviours such as reading and driving. Saccades can be triggered by the appearance of a visual stimulus that is motivating to the subject or initiated voluntarily by the person's interest in an object. Saccades can be suppressed during periods of visual fixation. In these situations, the brain must inhibit the automatic saccade response [37]. Eye tracking technology collects, among others, metrics related to fixations, saccades and blinks. This technology is also used in studies on information processing in certain learning processes (reading, driving machines or vehicles, marketing, etc.) in people without impairments [38; 39; 40; 41; 42; 43; 44; 45] or in groups with different impairments such as attention deficit hyperactivity disorder or autism spectrum disorder [45]. In these cases, the objective is to analyse the users' difficulties in order to make proposals for therapeutic intervention. This technology is also being used as an accident avoidance strategy [43]. Similarly, this technology can be used to study the behavioural patterns of subjects and to analyse the differences or similarities between different groups [44; 45; 46]. Eye tracking is also currently being used to test the human-machine interface based on monitoring the control of smart homes through the Internet of Things [47]. In addition, this technology is being incorporated into mobile devices. This will soon facilitate its use by users in natural contexts [48]. Similarly, eye tracking technology is being incorporated into virtual and augmented reality scenarios as the software for registration is included within the glasses [49; 50; 51]. Similarly, eye tracking technology is being incorporated into the control of industrial robots [52; 53]. Finally, systems are being implemented to improve the calibration and tracking of gaze tracking for users who were previously unable to use it, due to various neurological conditions (stroke paralysis or amputations, spinal cord injuries, Parkinson's disease, multiple sclerosis, muscular dystrophy, etc.) [53]. However, these applications are still very novel and require very specific knowledge of application, and processing and interpretation of the metrics. However, progress is being made in this aspect with the implementation of interpretation algorithms in software, such as machine learning techniques for supervised learning of classification, including algorithms such as \(k\)-nn and random forests [54]. Based on the above theoretical foundation, a study was carried out on the analysis of the behaviour of novice vs. expert learners during the performance of a self-regulated learning task. This task was carried out in a virtual environment with multimedia resources (self-regulated video) and was monitored using eye tracking technology. In this study, two types of analysis were used. On the one hand, statistical techniques based on analysis of covariance (ANCOVA) were used on two fixed effects factors which have been shown to be relevant in the research literature, the type of participant (novice vs. expert) and age (in this study, over 50 years old vs. under 50 years old).