## 6 Discussion  
Whereas computer scientists in EDM are typically experienced with predictions to derive knowledge from data, they lack experience when it comes to theory-driven approaches to resolve social science problems. In contrast, social scientists typically do not work on predictions but are knowledgeable regarding statistical tools to uncover causal mechanisms and derive models from theory [81]. Combining EDM and traditional statistical approaches can put forward research on LSA data, as the former is a well-equipped tool for finding generalizable patterns, while the latter is helpful for embedding domain knowledge to make causal analyses [2]. How the analyst can take advantage of both disciplines depends on the study aims and the dataset characteristics. In this study, we combine two-way fixed-effects and EDM tools to explore longitudinal LSA data. The methodology consisted of three steps. First, using domain expert knowledge about the data and the relevant literature, potential causal relationships in the Brazilian education system were structured by DAGs, supporting the identification of the primary sources of bias. Next, a two-way logistic regression fixed effects was used to control for the unobserved effects of schools and states. The AUC_ROC evaluated the models, and the predictive contribution of variables in the best models was assumed to be a less-biased estimation of the influence of variables in outcomes gains at the school level. Finally, additional data mining techniques described the data using posterior information to add knowledge about the relevance of the main variables highlighted in the first models. The initial models consist of a series of models applied to different time windows of the dataset, with the end period held constant at the last year for all windows and the start year ranging from the first year to the previous years before the last. This approach provides a more in-depth understanding of changes in certain variables and their relationship with gains in the ENEM. Some variables, such as students' access to computers, faculty education, and adequate pedagogical training, show greater relevance to the models when covering larger time periods than the small ones. It is likely due to the gradual persistence of interventions in these variables over time. Other variables, including father's education, students' race, and income, exhibited more mixed effects across the periods. These mixed results pose challenges for interpreting these variables, as they could be due to the dynamic complexities of the variables or noise in the data collection. This dual interpretation reinforces the need to match the results with the existing literature and conduct further investigations using other techniques, as was reported in this paper. The following analysis employed rules and decision trees to investigate two versions of temporal data, wherein all variables were replaced by a single value representing their trends. The rules aimed to identify relevant patterns that matched previous findings in logistic regression models. In contrast, the trees focus on exploring what was related to the interventions as detected in prior models. This exercise yields valuable insights for informing policies and practices related to these interventions. Although rules and trees are commonly used in the machine learning literature for predictive tasks, this paper only used them descriptively without concern for data reuse or predictive evaluation. Moreover, the trend versions of the dataset rely on assumptions that may not hold in reality. The transformations assume independence and linearity across variables, which may also not hold in reality. The knowledge extraction also raises an additional contribution of this paper, due to the scarcity of temporal and multivariate analysis in Brazilian secondary education. Overall, the results align with previous research from the educational field and could highlight the importance of well-known variables such as those related to socioeconomics.