# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
###### Abstract  
In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (\(k\)-means ++, fuzzy \(k\)-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary. machine learning; cognition; eye tracking; instance selection; clustering; information processing +
Footnote †: journal:  
the study of information processing in different learning tasks. The use of these resources is an opportunity for cognitive and instructional psychology to delve into the analysis of the variables that facilitate deep learning in different tasks. In addition, these tools allow the visualisation of the learning patterns of apprentices during the resolution of different activities. Initial research in this field [2] indicated that readers with prior knowledge showed little interest in the images embedded in the learning material. Furthermore, recent research [3] has found significant differences in eye tracking behaviour between experts vs. novices. It seems that experts allocate their attention more efficiently and learn more easily if automated monitoring processes are applied in learning proposals. Similarly, other studies [2] have indicated that the use of multimedia resources that incorporate zoom effects makes it easier for information to remain longer in short-term memory (STM). Likewise, if this information is accompanied by a narrating voice, attention levels and semantic comprehension increase [4,5]. A number of methods are used to analyse the effectiveness of the learning process including eye tracking-based methods. This technique offers an evaluation of eye movement in different metrics [1-5]. The eye tracking technique can use different algorithms [6-9]. They can be used to extract different metrics (more detailed explanations are given below). Specifically, eye tracking technology allows the analysis of the relationship between the level of visual attention and the eye-hand coordination processes during the resolution of different tasks within the executive attention processes [7,8]. Clearly, rapid eye movement has also been associated with the learner's fixation on the most relevant elements of the material being learned [2]. In this context, attention is considered to be the beginning of information processing and the starting point for the use of higher-order executive functions. In the same way, observational skills relate to eye tracking, which is directly related to the level of arousal and the transmission of information first to the STM and then its processing in working memory [6]. This development is influenced by learner-specific variables such as age, level of prior knowledge, cognitive ability and learning style [7]. However, some studies show that prior knowledge can compensate for the effects of age [8].