# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 5 Conclusions  
The present systematic literature review serves the purpose of providing a general overview on how predictive algorithms are being used in higher education environments, as of the time of writing this paper. After a search process that yielded 1382 results, 26 papers were selected as relevant examples of predictors and EWS applied in university contexts. Most of the selected studies were published in 2017 or later, which proves that this field of study is gathering significant research interest as of 2019. The selected predictors and EWS present great diversity in terms of contexts where they were implemented, input data they relied on, prediction algorithms they used, and the specific prediction goal they sought. However, it is important to understand that most of these studies were performed with an experimental mindset. For the most part, these predictors and EWS are not in a mature enough state to be permanently implemented in university courses, with the notable exception of Course Signals EWS [23], a tool that served as main inspiration for many of the EWS that came after it. Increasing the level of adoption of predictors and EWS in higher education should be a priority moving forward. The experimental nature of many of the applications included in this study is reflected by the fact that they are tailored for use in very specific learning contexts. As mentioned in Section 4, this has the advantage of allowing the use of uncommon types of input data, which are not available in environments other than the one that is targeted by the specific predictor or EWS. In addition to this, it is easier to obtain accurate predictions when focusing on a single, isolated context. However, the downside is that these applications are not useful when taken out of their intended environments. One of the keys to the success of Course Signals, besides being developed earlier than most other applications of its kind, is that it relies on activity and performance data obtained from a LMS, information that is available in most current higher education contexts. Thus, this application could be implemented in a multitude of different courses and degree programs across multiple academic years. A short-term objective in this field of research should be developing more tools able to function well in many different educational contexts, which would foster a more widespread adoption of EWS in higher education institutions. Regarding EWS that act as teacher-assisting tools, it was also observed that the help they provide in order to carry out interventions over struggling students is rather limited. These applications perform the necessary predictions and present results in an easily understandable way, but the decision  
Figure 6: Size of the student population per study. of what corrective measures to apply is usually made by the teacher or advisor on their own. One big leap of progress for EWS would be the ability to recommend ways of helping students which could be most effective in each particular case. If these are found to be reliable, the system could even perform them automatically. To follow this line of development, however, the EWS would need to perform some sort of profile analysis for each student.Studies exist that show classifying students regarding their study habits is possible [38]. This could provide valuable information in order to perform effective interventions. Another aspect that may hinder the development of these tools is the availability of data, or lack thereof. Openly available datasets including student-related information are scarce, causing the need for researchers to mine data themselves, usually relying on information that can be obtained from their home academic institutions. This implies that the resulting applications are typically tailored to work only in specific academic contexts. Additionally, creating a well-performing predictive application is extremely difficult for researchers who do not have access to good data sources of their own.