# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
### Measurement Parameters in Eye Tracking Technology

Table 1: \(Cont.\)studies conclude the need to integrate this technology in the usual learning spaces such as classrooms, although its use is still conditioned to important technical and interpretation knowledge on the part of the teacher [24]. Therefore, more research studies are needed to find out which of the presentation conditions of a learning task are more or less effective in learning depending on the characteristics of each learner (age, previous knowledge, learning style, etc.) [25]. Use of Data Mining and Pattern Mining Techniques in the Interpretation of the Results Obtained with the Eye Tracking Methodology  
There are many studies on the application of eye tracking technology that address the model of understanding the results obtained in the different metrics. To do so, they analyse the differences in results between experts vs. novices. Experts use additional information and solve a task faster and in less time. These studies also analyse behavioural patterns by comparing the type of participant, the type of pattern and the efficiency in solving the task. Cluster analysis metrics on frequency, time and effort are used to perform these analyses. Experts vs. novices use the additional information, e.g., colour and layout, in order to use the most efficient way of navigating the platform [11]. Additionally, experts seem to be faster, meaning they will solve tasks faster and more accurately. However, novice students seem to have a greater ability to understand the tasks [13]. Nevertheless, a comparative analysis of the performance of either the same learner in their learning process or between different types of learners (e.g., novices vs. experts) [26; 27] requires the use of different data mining techniques [21; 28]. These can be supervised learning (related to prediction or classification) [21] or unsupervised learning (related to the use of clustering techniques) [29]. Such techniques applied to the analysis of user learning have been called educational data mining (EDM) techniques [30]. Likewise, especially in the field of analysing student behaviour during task solving, the importance of using pattern analysis techniques within what has been called educational process mining (EPM) [31] stands out. EPM is a process that focuses on detecting among the possible variables of a study those that have a greater predictive capacity. These variables may be unknown or partially known. In short, EPM thus focuses on assuming a different type of data called events. Each event belongs to a single instance of the process, and these events are related to the activities. EPM is interested in end-to-end processes and not in local patterns [31]. The general objective of instance selection techniques (e.g., prototype selection) is to \"try to eliminate from the training set those instances that are misclassified and, at the same time, to reduce possible overlaps between regions of different classes, i.e., their main goal is to achieve compact and homogeneous groupings\" [32] (p. 2). These analyses would belong to the supervised machine learning techniques of classification and also to the statistical techniques related to knowing which possible independent variable or variables are the ones that have a significant weight on the dependent variable or variables. The common aim of these techniques would be the elimination of noise [33], which in experimental psychology would be related to the development of pre-experimental descriptive studies [34]. In summary, feature selection techniques are a very important part of machine learning and very useful in the field of education, as they will make it possible to eliminate those attributes that contribute little or nothing to the understanding of the results in an educational learning process. Knowledge of these aspects will be essential for the proposal of new research and in the design of educational programmes [8; 35].