# Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education
## 2 Related Works  
The application of intelligent systems in the higher education sector is an active field of research, powered by the abundance of available data and by the urgency to define effective, data-driven strategies to overcome students' dropout and improve students' academic performance. Therefore, there is a vast amount of scientific literature on this subject. In this paragraph, we briefly visit recent publications addressing the prediction of academic performance or dropout in higher education. The approaches reported in the literature depend strongly on factors such as the type of problem being addressed, the object of the prediction, the characteristics of the data used to create the predictions, the time to which the prediction refers, the size of the population, and the algorithms used. With respect to the type of problem being addressed, it can be observed that most studies focus their attention either on students' performance [5; 6; 7; 8; 9; 10; 11] or students' dropout [12; 13; 14; 15; 16]. One study investigates the ability to predict the time to degree completion and future enrollment in another program of studies [17]. A growing number of papers refer to online education or e-learning systems [14; 18; 19]. Among the studies related to on-site learning, many refer to single degree or module [8; 11; 20], others to full degrees [10; 13; 17], or a given faculty [5; 12; 15; 16]. Only a few take the whole university as a subject of consideration [6; 21]. How the problem is addressed depends heavily on the type of available databases. Many papers rely solely on academic information obtained from their academic management systems [7; 11; 13; 16; 17; 22], while others add social-demographic information [9; 12; 15; 21] and social-economic information [5]. A growing number of studies build their analysis on the sole information of the interaction of the students with the learning management system [8; 14], mostly for online education or e-learning modules. The size of the dataset varies from a small cohort of 23 data points [8] to some hundred data points [9], thousands of data points [5; 6; 10; 11; 12; 13; 16; 17; 21], and more than a million data points [7; 22]. Depending on the information available, some studies are able to make predictions as early as at the time of enrollment [21] or during the first years of enrollment [5; 10; 12]; other studies perform their predictions at sequential stages of the students' academic paths [7; 16]. Although a wide variety of classifiers are trained to build the predictive models for students; performance and dropout, there is no consensus regarding what is the best approach; thus, the experimental procedure prevails. It is possible to identify a set of preferred algorithms used to build the predictive models, such as Decision Trees [5; 8; 9; 10; 12; 13; 15; 21], Support Vector Machines [5; 9; 10; 16], Naive Bayes [5; 6; 10; 11; 12; 12], Gradient Boosting-based methods [9; 12; 16; 21], k-Nearest Neighbours [8; 9; 11; 12; 21], Random Forests [8; 9; 11; 16; 17; 22], and Artificial Neural Networks and Deep Learning [7; 9; 10]. The best values for accuracy of predictions, a common metric in most studies, vary from 75% to as high as 98%. Some of the unique aspects of our work are as follows:  
1. It does not focus on any specific field of study or module because the goal is to build a system that generalizes to any degree of the polytechnic university. Therefore, the dataset includes information from students enrolled in the several degrees of the four different schools of the university;
2. The base dataset built consists of demographic, social-economic information, macro-economic information, as well as academic information;
3. It performs phased predictions along the fist academic year because the focus is to develop a system that helps to segment students as soon as possible from the beginning of their path in higher education. Therefore, different versions of the base dataset are used, with an increasing number of features regarding academic performance;
4.