# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
## 5 Conclusions  
The use of the eye tracking technique provides evidence on the processing of information in different types of participants during the resolution of different tasks [9; 10; 11]. This fact facilitates research in behavioural sciences [37]. Working with this technology opens up many fields of research applied to numerous environments (learning to read and write, logical-mathematical reasoning, physics, driving vehicles, driving dangerous machines, marketing, etc.) [38; 39; 40; 41; 42]. It can also be used to find out how people with different learning disabilities [45] (ADHD, ASD, etc.) learn. Therefore, it could improve their learning style and make proposals for personalised intervention according to the needs observed in each of them. In addition, this technology can be used to improve driving practices and accident prevention with regard to the handling of dangerous machinery. This training is being carried out in virtual and/or augmented reality scenarios [49; 50; 51] that apply eye tracking technology. All these possibilities open an important field to be addressed in future research. Another relevant aspect to take into account is the way tasks are presented. This study has shown that the use of self-regulated tasks facilitates the processing of information and homogenises learning responses between novice and expert learners [12; 13; 14; 15; 35; 36]. Therefore, in future studies, we will study participants' processing in different types of tasks (self-regulated designs with avatars, zooming in on the most relevant information, etc.). Likewise, the results will be tested in different educational stages (early childhood education, primary education, secondary education, university education and non-formal education) and in different subjects (experimental vs. non-experimental). Subsequently, this study has shown that the use of different automatic learning techniques such as feature selection facilitates the knowledge of attributes that may be more significant for the research. This functionality is very useful in research that works with a large volume of features or instances. Moreover, if this technique is combined with the use of machine learning techniques and traditional statistics, the results can provide more information, especially related to future lines of research. In fact, in this study, it has been found that some of the variables considered as independent in the statistical study were also selected as relevant features in the study that applied supervised learning techniques of instance selection (e.g., prior knowledge, type of participant (student vs. teacher)). However, the feature selection techniques have also provided clues to be taken into account in future studies on the influence of other variables (e.g., gender, employment status, level of education and field of knowledge). In this line, the use of different algorithms to test both feature selection and clustering in unsupervised learning provides the researcher with a repertoire of results whose fit can be contrasted with the ARI. This will make it possible to know the groupings among the learners and to isolate the patterns of the types of learners in order to be able to offer educational responses based on personalised learning. On the other hand, the use of statistical analysis methods makes it possible to ascertain whether the variables indicated as independent have an effect on the dependent variables. In summary, perhaps the most useful procedure is, first, to apply the techniques of supervised learning of characteristics and then, depending on the variables detected, to pose the research questions and apply the relevant statistical analyses to test them. Finally, the results of this study must be taken with caution, as this study has a series of limitations. These are mainly related to the size of the sample, which is small, and the selection of the sample, which was conducted convenience sampling.