# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 2 Methodology  
This article is the result of a qualitative research study of 64 recent articles (almost 90% were published in the last 6 years) related to the different techniques applied for predicting students' behaviour. The literature considered for this study stems from different book chapters, journals and conferences. IEEE, Science Direct, Springer, IEEE Computer Society, iJET, ACM Digital Library,Taylor & Francis Online, JEO, Sage Journals, J-STAGE, Inderscience Publishers, WIT Press, Science Publications, EJER, and Wiley Online Library were some of the online databases consulted to extract the corresponding literature.  
We have excluded papers without enough quality or contribution. The journal papers without an impact factor listed in the ISI Journal Citation Report or not peer-reviewed were excluded. The conference papers corresponding with conferences not organized/supported/published by IEEE, ACM, Springer or renowned organizations and editorials were excluded too. As a result, 35% of the papers analyzed correspond to journal articles; of these, 64% have JCR impact factor and the rest correspond to peer-reviewed journals indexed in other scientific lists.  
For the search processes used for these databases we mainly considered the following descriptors: \"Predicting students' performance\", \"Predicting algorithm students\", \"Machine learning prediction students\", \"Collaborative filtering prediction students\", \"Recommender systems prediction students\", \"Artificial neural network prediction students\", \"Algorithms analytics students\" and \"Students analytics prediction performance\", among other similar terms.  
The literature review provided throughout this article is mainly classified from two points of view: techniques and objectives. We describe the techniques first in this article, since they are applied to reach the objectives considered in each reference. These techniques, in turn, are implemented by means of several algorithmic methods.  
Table 1 summarizes the main features of the literature review, showing four groups of columns: students' level, objectives, techniques, and algorithms and methods.  
* Students' level: Each reference analyzes datasets built from students of a particular level. We consider a classification of wide levels, corresponding to School (S), High School (HS) and University (U).
* Objectives: The objectives are connected to the interests and risks in the students' learning processes.
* Techniques: The techniques consider the different algorithms, methods and tools that process the data to analyze and predict the above objectives.
* Algorithms and methods: The main algorithms and computational methods applied in each case are detailed in the Table 1. Other algorithms with related names or versions not shown in this table could be also applied. The shadowed cells corresponds with the best algorithms found when several methods were compared for the same purpose.  
Figure 1 presents graphically the basic statistics about the techniques, objectives, type of students, and algorithms considered in the literature review. These graphs are built from Table 1 in order to understand better the impact of the literature review that is explained in the next sections.  
A first consideration about predicting students' performance by means of ML is the academic level of the students. This information can be useful to know because the datasets built from the students' behaviour imply latent factors that can be different according to the academic level. As we can see in Figure 1, most of the cases correspond to the university level, followed by the high-school level.