# Forecasting Students Dropout: A UTAD University Study
## 3 Data and Methods
### Data Pre-Processing  
Figure 2 illustrates the pipeline used in this work. In the first step, _Prepare data_, the data is ingested, and some cleaning is done, before using the PFI technique. However, when the ANN model is used, some additional data manipulation is done (see Section 4). The second step regards model construction, training, and validation. In the last step, the model is implemented, used to predict new samples, and monitored. In this section, the first step is described.  
The data considered in this study were previously anonymized before pre-processing to comply with the current data protection regulation. The original dataset contained academic information for each student on a single line. Therefore, the first-year grades were extracted per column. Then, only the academic record of each student was kept, as well as their age. This was done due to the fact that the other features contained several empty fields, which would not contribute to the correct prediction. Additionally, ll students who are still completing the course and those who dropped out shortly after joining university (students with a zero score, in all subjects) were excluded from the dataset. A new binary variable was created, indicating whether the student dropped out or not, which was to be used as the output variable. It led to a new, cleaned data set with information from 331 students. There is some significant data imbalance in the present study, with 124 cases of school dropout and 207 cases of students that finished the study plan successfully.  
The extraction of the most relevant features was carried out using the PFI technique (Section 3.4). The results are presented in Figure 3. The top six features (highlighted in blue) were selected, as it was found that these cause a more significant drop in the accuracy.  
In this analysis, the younger students demonstrate more resilience in completing the study plan, which showed that age is an essential factor in academic dropout. Figure 4 reveals the age importance in academic dropout study. The third quartile of students' dropout has almost the same value as the median of students that are successful. On the other hand, the success of the most demanding curricular units is also decisive in the continuity of students' studies. Figure 5 illustrates the number of fa  
Figure 3: Permutation importance box plot.  
Figure 2: Machine learning modularization pipeline.  
The particular units in red are used in the model as important features, and the curricular units in blue are discarded. In general, the model used the curricular units with more fails as the most important features. The exception to the rule is Mathematical Analysis II. In this case, the number of fails is also significant in students who are successful in the course. Thus, the model cannot discriminate well students who drop out using this feature.  
The applied data division strategy is exhibited in Figure 6. A stratified split was applied to the data set, keeping 80% for training and 20% as a test set to perform final evaluations. In the training portion, a stratified cross-validation technique was used, dividing it into 10-folds. Models were later trained in 9-folds and validated in the remaining fold. Thus, eliminating the need to divide the data into three distinct datasets, which would drastically reduce the number of learning samples, given that the amount of available data is low.