## 2 Materials and Methods  
### Related Works  
In [8], the author carried out an analysis of student performance using different classifiers. The dataset used is made up of 225 instances made up of ten attributes each from the student college database. Each instance is characterized by intermediate assessments, course attendance, participation in seminars, and a final exam expressed on a scale of three values: Poor, Average, Good; participation in the activities, laboratory experiments, development of the Office Automation project, and participation in the Workshops expressed in two values, Yes and No; the evaluation in the previous semester and the final evaluation expressed with a scale of four values High average, Average, Poor, and Fail. The dataset was analyzed with the help of WEKA software using the following classifiers: Naive Bayes, BayesNet, Iterative Dichotomiser 3 (ID3), C4.5, and Neural Network (NN). WEKA is open-source software developed at the University of Waikato in New Zealand. It contains four applications: explorer, experimental, knowledge flow, and the command line interface (CLI) and contains tools for data preprocessing, classification, regression, and visualization. For the comparison between the various classifiers various measures have been adopted: True Positive Rate (TP Rate), False Positive Rate (FP Rate), and Accuracy. The experimentation showed that the Bayesian Networks performed better than the other classifiers in terms of accuracy obtaining a value equal to 92%. The authors of [9] analyze the kalboard 360 dataset using WEKA software with the following algorithms: random forest, SVM, Naive Bayes, Multilayer Perceptron, and DT-J48. The results obtained show that the Multilayer Perceptron achieved the best performance compared to the other classifiers reaching an accuracy value equal to 76.07%. The authors of [10] carried out a study on student performance prediction by comparing the Random Forest algorithm with the Decision Tree and Naive Bayes algorithms usingthe WEKA open-source software. The comparisons showed that of the three algorithms, the random forest is the one with the highest prediction accuracy at 81%. The authors of [11] analyzed the results of learners at the end of the quarter in a mixed educational setting. Three different classification algorithms were used for the analysis: logistic regression, decision trees, and random forest. This study applies these three models to make medium-term forecasts and to identify the best one. The objective variables are whether the final exam was passed or not. The analyzed data were collected through an LMS and were weekly assignments, quiz results, and clicks on the contents of the platform. Data from Facebook Graph API were added, which made it possible to collect in-formation regarding the activities of learners on Facebook Live. Comparing the performance of the algorithms previously mentioned, it was found that the Random Forest algorithm was the most performing with 0.81 of F1-Score and 0.83 of Accuracy. In [12], the authors used time series classification method to predict student's dropout rates using the OULAD dataset. The result of this work is that is possible to obtain an accuracy value of 84% with only 5% of the data. The highest accuracy reached was 90% with the full dataset. More recently, a review about benchmark studies involving the OULAD dataset was performed in [13], where the authors compare different studies involving learning-based advanced approaches at the state of the art, also based on machine learning and deep learning approaches. Even if these approaches are widespread, the authors of [13] point out current challenges about the level of development of this technology in terms of feature selection, state-of-the-art approaches that are still missing (e.g., encoder-decoder or ensemble modeling), and feature scaling.