## 5 Results
### Going Back and Looking at the Data  
A set of classification rules were created through online analytical processing (OLAP) queries to validate the knowledge extracted by the logistic regression coefficients. The rules were created over the top seven variables highlighted in the models and the outcomes. The rules are one conditional where the variables are the ascendent and the outcomes the consequence. The data used are two new versions of the original preprocessed dataset in which each variable, including the scores, was replaced by its trend value representing variable and outcome gains. Two different measures of trends were considered: (1) the slope, representing the linear tendency of variables from 2009 to 2019 and (2) the average difference from 2009. The goal is to confirm the influence and statistical significance of the ascendant variables in classify scores over time. For a meaningful interpretation, the variables were normalized between zero and one. Table 4 presents the best rules with the associated quality metrics. Expressed in percentages, the support (S) indicates the size of the data slice covered by the rule condition, and the confidence (C) indicates the concentration of schools within this slice that follows the second condition. Finally, the relevance of the feature is assessed by the departure of the lift (L) from the sample average [79]. Lifts close to one indicate low relevance of the antecedent variable regarding the consequent and lifts close to zero represent high negative effects. Overall, the variables highlighted as positive in Table 3 are strongly related to ENEM scores with lift higher than one. Additionally, corroborating with the logistic models, the variables with negative effects, student's computer and number of employees, have lifts lower than one. The variable student's computer has a lift close to zero, indicating a very low concentration of schools with average changes greater than 0.66 in the top quartile of score trends, confirming their negative effects on student outcomes. Father's education had a controversial result in the logistic models, with positive and negative effects in different periods, and it has a lift above one, indicating that a variable has a positive effect in the trend dataset when assessed by the classification rule. The potential of faculty education as a factor in classifying schools with higher gains in ENEM scores is significant, yet there is a scarcity of quantitative research in this area within the Brazilian secondary education system. This highlights the need for further investigation to better understand the relationship between faculty education and improved ENEM scores, and how this knowledge can inform policies and practices to support schools in achieving higher gains in this area. Figure 5 uses decision tree gains to classify schools in those with higher gains (third quartile) and lower gains (remains quartile) in order to describe schools which achieve higher gains in the variable faculty education. The trend version of the dataset which has the average change over time was used. The decision paths, represented by the tree's branches, express the knowledge embedded in the data in a humanly understandable format through \"if-then\" rules [80]. The leaves of the tree, located at the bottom, represent the outcomes of the decision paths (higher and lower gains), while the most important attributes are found at the top of the tree. The first number in each node represents the majority class present in the rule defined in the node, while the second represents the fraction of positive class (confidence) and the third the coverage of the rule (support). Only the dummy variables representing