# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 1 Introduction  
Data analytics encompasses the collection of techniques that are used to examine data of various types to reveal hidden patterns, unknown correlations and, in general, obtain new knowledge [1]. This discipline has a very strong presence among recent trends in information and communication technologies, being of utmost relevance for researchers and industry practitioners alike. Data analytics is often coupled with the term \"big data\", since analysis tasks are often performed over huge datasets. Other fields of study which are very popular nowadays, such as data mining or machine learning, are close to data analytics and share many relevant techniques. Depending on the nature of the data that are being analyzed and the objective that the analysis task should fulfill, several sub-disciplines can be defined under data analytics. Examples of these are text analytics, audio analytics, video analytics and social media analytics. One of the most relevant, and the main focus in this paper, is _predictive analytics_, which includes the variety of techniques used to make predictions of future outcomes relying on historical and present data [2]. The ability to predict future events is essential for the proper functioning of some applications. Notable examples among these are early warning systems (EWS), which are capable of anticipating potential risks in the future thanks to information available in the present, accordingly sending alerts to the person or group of people who may be affected by these risks and/or that are capable of countering them. Their degree of reliability on information technologies greatly varies depending on the context they are applied on. EWS are mostly known for their use to reduce the impact of natural disasters, such as earthquakes, floods and hurricanes. Upon detection of signs that such a catastrophe might happen in the near future, members of the potentially affected population are alerted and given instructions to prevent or minimize damage [3]. However, other kinds of EWS have been implemented in many different contexts. For instance, they are used in financial environments to predict economic downturns at an early stage and provide better opportunities to mitigate their negative effects [4]. In healthcare, early warning systems are used by hospital care teams to recognize the early signs of clinical deterioration, enabling the initiation of early intervention and management [5]. This document revolves around the application of predictive algorithms and EWS in educational contexts, focusing on higher education scenarios, most notably university courses. This falls under the umbrella of learning analytics (LA), a particularization of data analytics which is usually defined as \"the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs\" [6]. Predictors and EWS are used in higher education contexts with the general objective of supporting learning and mitigating some of the most important problems that are observed in these scenarios, such as student underperformance or dropout. Many authors have presented their own solutions in this field of study, with vast differences in terms of specific scenarios where they are applied, input data they work with and problems that they address. With such a vast collection of different approaches, it can be a difficult task to fully understand the current status of solutions in this field, as well as identifying which of them have been applied in real education scenarios with satisfactory results.