# Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education
## 1 Introduction  
Enrolling in a Higher Education degree represents, for a large number of students, a major challenge related to adapting to more demanding and autonomous learning and, eventually, to a first experience living apart from their core families. This adaptation is not always plain, and the difficulties experienced by the students often impact on their academic performance, eventually leading to academic failure or even dropout. The reasons for academic failure and dropout in higher education have been studied worldwide and include a variety of reasons, namely, academic difficulties and financial, personal, or family issues of the students. They can negatively impact students' personal lives with feelings of failure, frustration, or increased anxiety and self-esteem [1]. They may lead to lower earning potential and a limited range of job opportunities. On the other hand, high dropout rates can result in a less educated workforce and an overall lower level of economic productivity, impacting society as a whole [2].  
In recent years, there has been a growing recognition of the need for effective strategies to address academic failure, dropout, and all related issues. Most of these strategies include welcome programs for new students, academic advising, tutoring services, or mentoring programs. Data-driven tools have been developed to leverage such programs, enabling, for instance, the provision of information to tutors or counseling to support students' needs in their success [3, 4].  
In this study, we are interested in understanding the best approach to predict student's performance and dropout risk at the earliest stage of the student's academic path. Our main goal is to develop a system that allows the earliest possible identification of students struggling with difficulties in their academic path or prone to dropping out. Such system will provide valuable information to mentors, tutors, and to management and academic entities responsible for putting into place comprehensive student support mechanisms.  
From the machine learning point of view, this is a classification task where the target variable might assume three different values, depending on the academic situation of the student at the end of the regular period for finishing the degree--either the student finished the degree on due time, or is still enrolled, or has already dropped out.  
Additionally, the best time to predict the students' performance and dropout risk, during the first academic year, is also investigated. Such investigation is performed with three different flavors of the initial dataset, each representing different moments of the students' academic path during the first academic year.  
The datasets are imbalanced in nature, and the imbalanced distribution of the targets varies among the three datasets' flavors used. As is often the case with imbalanced data, the minority classes are also the most relevant. The challenge is to obtain the model that best classifies the minority classes since students belonging to those are the ones that will benefit the most from targeted pedagogical interventions. Therefore, the best strategy for dealing with the imbalanced nature of the data is also investigated.  
In addition to this introductory section, the rest of the paper is structured as follows: Section 2 presents a brief review of the literature concerning the application of intelligent systems in the higher education sector; Section 3 describes strategies for dealing with imbalanced datasets; Section 4 presents the process for data acquisition, the methods used to deal with the imbalanced nature of the data, and the methodology used to build and evaluate the classification models; Section 5 presents the results and discussion; and Section 6 presents the conclusions, which are followed by references.