## 1 Introduction
### Machine Learning Algorithms Used in the Interpretation of Cognitive Load  
As discussed above, many different factors can influence the performance of a task or the resolution of a problem. Measuring them provides a huge amount of data to be \begin{table}
\begin{tabular}{l l} \hline \hline
**Elements to Consider** & **Measurement** \\ \hline Task characteristics, indicators of the extrinsic cognitive load (form of task presentation (visual—image, text, etc.)) and extrinsic cognitive load written, auditory, all), multimedia learning environment simulation, task execution schedule, etc. & Description of the type of task Analysis of the presentation of the task \\ \hline Physical space [characteristics of the space in which the task takes place (lighting, space, etc.)]. & Indicators for brightness, temperature, noise, time, and time of day \\ \hline Learner characteristics (age, gender, prior knowledge, motivation towards the task, way of responding, way of solving, type of reasoning applied, etc.). & Variables assigned (age, gender, etc.) Analysis of prior knowledge Cognitive style and learning style \\ \hline \hline \multirow{4}{*}{Physiological indicators of the intrinsic cognitive load} & Pupil diameter Fixations \\ \cline{1-1}  & Saccades \\ \cline{1-1}  & GSR amplitude \\ \cline{1-1}  & SCR count \\ \hline Perception of the germane load & Performance indicators in the execution of the task \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of elements to be considered in the task performance space and measurement indicators. processed. Various statistical and machine learning techniques can be used to carry out this processing. These techniques are divided into supervised techniques using labelled data to train models (within which we can differentiate between classification and regression techniques); unsupervised techniques that do not require labels and are used to discover hidden patterns in the data; and semi-supervised learning (combines the most appropriate supervised and unsupervised approach in each case of study). The techniques include algorithms such as logistic regression [34]. These can help in the classification of intrinsic cognitive load into levels (low, medium, and high) [19]. A probabilistic study from a percentile analysis can also be used for this purpose [35]. Similarly, supervised machine learning algorithms can be used--where it is advisable to select features beforehand in order to detect the most influential ones [19]. Next, regression and/or classification techniques can be applied. Regression techniques may include linear regression, Support Vector Machine (SVM), Decision tree, and Neural Network algorithms. Classification techniques that can be used include discriminant analysis and \(k\)-Nearest neighbour (KNN) algorithms, among others. Unsupervised learning techniques (clustering) include applying algorithms such as \(k\)-Means, \(k\)-Medoids, and s-Means [36]. Therefore, before data processing, it is important for noise to be removed and classifiers to be found. The aim is to determine the best indicators of cognitive load in each task [19]. Principal component analysis can also be applied to find a feature matrix [37]. For example, many workload studies have used the SVM algorithm in multiclass classification [36], whereas other studies have used KNN, Naive Bayes, Random Forest, SVM [38; 39; 40], and neural network-based models to infer cognitive load levels [41; 42]. Classifier effectiveness can be determined using accuracy, recall, and precision indicators in the different algorithms used [41; 42; 43]. Fit can be examined using goodness-of-fit index models, which include various fit tests (NFI = normed-fit-index; RMSEA = root-mean-square error of approximation; SRMR = standardised root-mean-square residual; TLI = Tucker-Lewis index; CFI = comparative fit index; AIC = Akaike information criterion; and ECVI = parsimony index) [44].