# Forecasting Students Dropout: A UTAD University Study
## 2 Background Theory and Literature Review  
Some publications regarding data mining (DM) on predicting academic success focus on distance learning platforms and tutoring systems driven by AI [3; 4; 5]. [PERSON] et al. [3] developed a solution using only students' interactions with the virtual learning environment and its derivative features for early prediction of at-risk students in a Brazilian distance technical high school course. They use an elitist genetic algorithm (GA) for tuning the hyperparameters of machine learning algorithms. The population is formed by several classifiers: decision tree (DT), random forest (RF), multilayer perceptron (MLP), logistic regression (LG), and the meta-algorithm AdaBoost (ADA) with different hyperparameters. The approach obtains an AUC medium value of 0.845. Other work, proposed by [PERSON] et al. [4] used a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), called CONV-LSTM, to automatically extract features from Massive Open Online Courses raw data and predict whether students will drop out. They used a cost-sensitive technique in the loss function, which considers the various misclassification costs for false negatives and false positives. They claim that the proposed model is better when compared to baseline methods. The dataset stores activity students' records about which course they are enrolled in. [PERSON] et al. [5] presented a model to predict the student dropout in online courses considering features of daily learning progress. They used a Random Forest Model, obtaining 87.5% as the F1-score. In the traditional educational system, several approaches were found using many classifier systems. The use of Artificial Neural Networks (ANNs) was demonstrated and considered promising by [PERSON] and [PERSON] [6]. Their study was carried out with data obtained from 2670 students, from the Public University of Ecuador, over three years (2014-2017). Two types of algorithms were used: multilayer neural networks and radial basis function network (ANN that uses radial basis functions as activation functions, RNN), with both presenting very high dropout forecast rates of 96.3% and 96.8%, respectively. In another study, [PERSON] [7] concluded that, with the use of ANNs, the forecast rate was relatively high when using two semesters of data, decreasing dramatically when using only one. [PERSON] and [PERSON] [8] used an RF to predict students at risk of dropping out. They used 165,715 high school students' data from Korea's National Education Information System of the year 2014. They obtained an accuracy of 95% binary classification. [PERSON] and [PERSON] [9] used decision trees (DT) to identify patterns of student dropout from socioeconomic, academic, disciplinary, and institutional data of students from undergraduate programs at the University of Narino. They used three datasets and obtained a confidence threshold greater than 80%. [PERSON] et al. [10] defined several models from enrollment up to the fourth semester using mainly academic data. The approach considered the output of previous stages, i.e., each step assumed the prior knowledge generated. The model goal consisted of identifying engineering students with a high probability of dropping out to design and apply dropout prevention policies effectively. The predictive model could identify 72% of the students that will dropout. At the end of the fourth semester, the results could reach 91.5%. [PERSON] et al. [11] considered three singles classifiers: K-Nearest Neighbor (KNN), Naive Bayes (NB), and Decision Tree (DT), to identify the best in predicting students' dropout at a private university in Jakarta. They use demographic indicators and academic performance to predict student dropout. Their model reached 79.12% of accuracy. [PERSON] et al. [12] identified students at risk of dropping out at a large Hungarian technical university using predictive analytical tools. They use data of 10,196 students who finished their undergraduate studies (either by graduation or dropping out) between 2013 and 2018.