# Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education
## 5 Results and Discussion
Values represent mean and standard deviation. class is also significantly higher (0.808 \(\pm\) 0.026) than the best F1 score values obtained for the \"Enrolled\" or \"Dropout\" classes ( 0.616 \(\pm\) 0.009 and 0.493 \(\pm\) 0.055, respectively). Regarding feature importance, Figures 5-7 present the relative weights of the top five most important features for the three models with best scores (SMOTE + RF, SVMSMOTE + RF and BRF) for each dataset, S0, S1, and S2, respectively. Grade\", \"Previous Habilitation Grade\", and \"Displaced Student\". The \"Degree\", \"Average Grade at the National Exams\" and \"Tuition fees up to date\" are on the top five list for at least one of the models. The weights of the feature importance are similarly low in all of the cases and close to 0.05. In the case of dataset S1, the three most important features common to all the models refer to the academic performance after the enrollment: \"Number of CUs approved (first sem)\", the \"Average Grade (first sem)\", and the \"Number of CU attended (first sem)\". The \"Age at enrollment\", \"Tuition fees up to date\", and the \"Admission Grade\" features also belong to the top five of at least one of the models, although with lower relative scores. In the case of dataset S2, the features related to the academic performance are also the most important: \"Number of CUs approved\" in the second and in the first semester and the \"Average Grade\" in the second semester. Overall, the features related to the macroeconomic situation, the social-economic features, and the demographic features are of irrelevant importance in the models, at the detriment of features related to the academic performance after enrollment. This holds true only for the datasets S1 and S2 since S0 does not contain such information. The \"Age at enrollment\" is the only social-demographic feature that consistently appears in the top five list of feature importance. Worthy of note is the fact that for datasets S0 and S2, all the features present low feature importance scores, below 0.10. The maximum feature importance score is slightly above 0.10 for the most important feature in dataset S2. The results obtained compare fairly with some other works that attempt to predict students' dropout or academic performance at early stages, although our performance results are slightly lower. For instance, in a study to predict dropout at early stages [16], the authors obtained a model with 72% accuracy at the time of enrollment and 82% by the end of the first semester. A dataset from an engineering school was used. In another study for performance prediction [10], the model achieved 79% accuracy for the prediction of students' performance at the time of admission in a Computer Science degree. Both works used datasets from specific fields of study and binary classification tasks. This is in contrast with our multi-class problem using a heterogeneous dataset, which includes samples from several different fields of study and different schools, and it might explain the slightly lower performance scores of our models.