# Predicting Student Academic Performance by Means of Associative Classification
## 4 Results
### Performance Comparison between Different Algorithms
* _Recall of class fail_: It is the ratio between number of students who have been correctly labeled as belonging to class _fail_ (\(TN\)) divided by the total number of students who actually belong to class _fail_ (\(TN+FP\)). * _F1-score of class fail_: It is the harmonic mean of precision and recall of class _fail_. * _Balanced Accuracy_: It is the average of the recall computed over the two classes and is given by \(\frac{1}{2}(\frac{TP}{TP+FP}+\frac{TN}{TN+FN})\)[45]. It evaluates the ability of the classifier to correctly assign both class labels. It is especially useful when the classes are imbalanced in the test sample since it rewards the correct predictions on the minority class. When the test samples are balanced over the two classes, it corresponds to the conventional accuracy measure (i.e., percentage of correct predictions). While the accuracy measure is independent of the class, the other metrics are specific to class _fail_. Since the main goal of student performance prediction is to early detect at-risk students, we specifically analyzed the ability of the classifiers to correctly classify this particular category of students. Figure 2a-f plot for each course the accuracy scores achieved by the classifiers at different time points. The vertical dashed lines indicate the examination sessions scheduled during the academic year. The accuracy values achieved by most of the algorithms before the beginning of the first semester is around 60%. For the 1 st semester courses (MA, CH, CS) the performance decreases after the first examination session, because predicting students' outcomes at the next sessions (i.e., at the second trial) is significantly more challenging. For the 2 nd semester courses (PH, LA) similar results were achieved after the first examination, which is scheduled after time \(t_{9}\). \(L^{3}\) performed as good as the best performing classifiers (K-NN, MLP) while decision tree classifiers (DT and RF) performed worse. Slight fluctuations in the series of accuracy values have shown in the last time points (\(t_{9}\)-\(t_{11}\)). The reason is that since the number of students under evaluation decreases (because the majority of them have already passed the exams), the models are less robust and more sensitive to noise. Figure 2f-t, respectively, plot for each course the F1-score, precision, and recall of class _fail_ achieved by the classifiers at different time points. They describe the ability of the classifiers to accurately predict exam failures. Precision and recall values increase as time goes by (e.g., for MA the \(L^{3}\) precision ranges from 65% at \(t_{0}\) to 90% \(t_{10}\)). The recall values significantly increase after the first examination session (at \(t_{5}\) for the 1 st semester courses, at \(t_{9}\) for the 2 st semester ones) because students who failed once are more likely to fail again. \(L^{3}\) performed as good as the best performing classifiers for all the courses and for most of the considered time points. In order to assess the statistical significance of the performance variations (computed in terms of accuracy, F1-score, precision, and recall of the class _fail_), we applied the [PERSON] signed rank \(t\)-tests [46] using a significance level equal to 0.5%. The results show that \(L^{3}\) performed significantly better than DT and RF at specific time points for the majority of the analyzed courses, while it performed as good as the best performing approaches (K-NN, MLP). Hence, the \(L^{3}\) associative model could be deemed as a reliable model for early predicting student performance.