## 2 State-of-the-Art
They employed Naive Bayes, C4.5 decision tree and K-Nearest Neighbors (KNN) algorithms. Their results regarding accuracy are 80.89%, 74.52% and 71.97% using the C4.5, Naive Bayes and K-Nearest Neighbors algorithms, respectively. The study by the Ref. [28], the authors studied the available approaches to predict job satisfaction, looking for identify the main factors influencing the job satisfaction of IT professionals and explore the possibilities of generalizing job satisfaction prediction models to other areas of the IT industry. They employed Random Forest, Logistic Regression, Support Vector Machines and Neural Networks algorithms. They obtained the data set from Stack Overflow's developer survey and IBM's HR analytic data set. The resultsshow that the Random Forest algorithm obtained the best-value scores for accuracy of 0.80, precision of 0.74, specificity of 0.80 and an F1 of 0.75. From what the Ref. [17] has done, we could observe that in the prediction of job satisfaction the algorithms of Logistic Regression, Random Forest and Extreme GB (EGB) obtained accuracy values of 78%, in the study by the Ref. [28] we observed that the Random Forest algorithm obtained an accuracy value of 80% thus surpassing algorithms such as Logistic Regression, Support Vector Machines and Neural Networks. This evidence shows that the Random Forest algorithm is a candidate for a good predictor of teacher job satisfaction, without losing sight of algorithms such as Logistic Regression, Extreme Gradient boosting and Gradient boosting whose accuracy values are very similar to those of the aforementioned algorithm [17]. The use of algorithms from the category of decision trees for the prediction of job satisfaction, such as CART (Classification and Regression Tree) used in the study of the Ref. [18; 25] and C4.5 used in the study of the Ref. [27] obtained an accuracy of 76.15% and 80.89% respectively, thus surpassing even neural network algorithms as in the case of the Ref. [18], Naive Bayes and k-nearest neighbors in the Ref. [27]. This evidence shows that decision trees perform well in predicting job satisfaction. The studies by the Ref. [26; 28] employed studies of neural network algorithms as part of the experiments to predict job satisfaction, where in the case of the Ref. [28] the Random Forest algorithm obtained an accuracy of 0.80, thus outperforming the neural network algorithm by a small margin of 0.01. In the case of the Ref. [26], the neural network algorithm and the \(J\)48 decision tree algorithm are validated as good algorithms for predicting job satisfaction. Our review of the state-of-the-art shows studies that address the relationships between different variables and teacher job satisfaction. One of the most used techniques is structural equations [22; 23; 29; 30; 31; 32; 33]. Other studies use techniques such as Chi-square and ANOVA [24], MANOVA [34], Pearson correlation [35], hierarchical linear models [36], independent samples t-test, coefficient Pearson Product Motion Correlation and Stepwise Regression [37], to name a few. In the review of the scientific literature, we found a few studies that address this issue from a machine learning approach, where these are: identification of the job satisfaction of a teacher in a new workplace [25], prediction of job satisfaction of teachers Google, Facebook, Amazon, Microsoft, and Apple employees [17], construction employee job satisfaction prediction [18], worker job satisfaction prediction [26], satisfaction prediction of school administrators [27], prediction of job satisfaction in information technology workers [28] or modeling teacher job satisfaction using artificial neural networks [16]. However, all the studies that we have identified analyze the relationship between the variables studied by applying traditional techniques such as structural equations, Pearson or Spearman correlation coefficients, Chi-square, ANOVA and its variants.