# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 4 Discussion  
Every single study included in this literature review had the same general purpose: using data related to students and their environments to improve academic results or address problems that exist in higher education scenarios. However, the approaches presented by each one of these papers are extremely diverse in many aspects, such as the targeted context, specific analysis goal, types of input data, and used algorithms. The present discussion points out the similarities that exist among some of the studies, as well as the most distinctive particularities that were observed. The selected papers in this review consist of 13 predictors and 13 EWS. The differences between these two types of systems are explained at the beginning of Section 3. The line separating both categories is sometimes very thin, as some of the predictors are able to offer results using only data available early in a course, which would easily allow them to serve as basis for an EWS. Examples of these situations can be observed in the works published by [PERSON] et al. [11], [PERSON] et al. [13], and [PERSON] [15]. In terms of document types, 14 of the selected articles in this review are full papers published in journals, 11 are articles presented at conferences, and the remaining one [26] is a book chapter. Interestingly, all 14 of the full papers were published in different journals, covering the topics of research in education, technology-enhanced learning, and educational psychology. This makes it difficult to highlight specific journals that publish a high number of works related to predictive analysis in education, but, at the same time, it proves that this is a topic of interest for data scientists and educational researchers alike. As for the relevance of the journals themselves, 10 out of the 14 papers were listed in JCR, most of them being classified in either the first or the second quartile in terms of impact. Some of the articles were found in journals that are consistently rated among the most relevant ones in their fields, such as _Computers & Education_[19], _Internet and Higher Education_[33], and _Computers in Human Behavior_[31]. On the other hand, five of the 11 conference articles were presented in different editions of the _International Conference on Learning Analytics & Knowledge_, considered to be one of the main research forums in the learning analytics field. The remaining six were all presented in different conferences, including some long-running ones such as the _International Conference on Information, Intelligence, Systems and Applications_[17] and the _International Conference on Software and Computer Applications_[12]. It is important to note that most of the selected papers were published fairly recently as of the writing of this review. As can be observed in Figure 4, five documents were published in 2017, eleven in 2018, and five during the first half of 2019, with the remaining five studies being published between 2012 and 2016. This suggests that the topic of predictors and EWS in higher education is far from exhausted, and its popularity among researchers is still rising. It is safe to say that further developments and unique studies regarding this field of knowledge will keep appearing in the near future. Another interesting aspect is the diversity of studies regarding the geographical location where they were carried out. While most instances were developed in areas of North America, Europe and Asia, there were examples of predictors and EWS developed on every continent. This suggests that the attention garnered by predictive analysis in higher education is not exclusive to researchers in specific parts of the world. Instead, this topic has worldwide (albeit nonuniform) relevance, as shown in Figure 5.  
Moving on to the contents of studies themselves, the selected papers could be classified regarding their general prediction goal. Table 2 shows a summary of prediction goals, as well as how frequently they appear.