# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 5 Discussion  
In this article, we have reviewed many papers aimed at predicting student behavior in the academic environment. We can draw some conclusions from the analysis of these papers.  
We have noted that there is a strong tendency to predict student performance at the university level, as around 70% of the articles included in this review are intended for this purpose. This may encourage us to consider complementary research efforts to fill gaps in other areas. Thus, we consider that it would be interesting to promote working lines to apply these predictions at school level, which would contribute to identify the low performance of students at early ages. The analysis of student dropout during the early stages of their levels is very interesting, as there are still opportunities to research about helpful predictive tools to enable prevention mechanisms. In this sense, a good approach to research would be to apply the same predictive techniques used for academic performance (and other novel ones) to this case, in addition to considering non-university levels.  
Based on the data collected in this review, the most widely used technique for predicting students' behavior was supervised learning, as it provides accurate and reliable results. In particular, the SVM algorithm was the most used by the authors and provided the most accurate predictions. In addition to SVM, DT, NB and RF have also been well-studied algorithmic proposals that generated good results.  
Recommender systems, in particular collaborative filtering algorithms, have been the next successful technique in this field. However, it should be clarified that success has been more in recommending resources and activities than in predicting student behavior.  
As for the neural networks, they are a less used technique, but they obtain a great precision in predicting the students' performance. We believe that a good line of research with these techniques would be to apply them to other related types of predictions in the educational field, different from the strict students' performance.  
We emphasize that unsupervised learning is an unattractive technique for researchers, due to the low accuracy of predicting students' behavior in the cases studied. However, this fact can be an incentive for research, as it provides the opportunity to further improve these techniques in order to obtain more reliable and accurate results.  
This review can be useful to obtain a wide insight of the possibilities to apply ML for predicting students' performance and related problems. In this regard, Table 1 and Figure 1 may be useful to researchers in planning how to approach the initial stages of their studies. Nevertheless, many researchers will probably tackle this problem in the coming years considering other and new ML tools, since this problem has attracted a high degree of interest nowadays.  
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**Author Contributions:** Search, classification, and analysis of bibliographic resources, J.L.R.-G.; work methodology, writing and editing of original manuscript, J.A.G.-P.; supervision and access to resources, A.D.-D. All authors have read and agreed to the published version of the manuscript.
**Funding:** This research was partially funded by the Government of Extremadura (Spain) under the project IB16002, and by the ERDF (European Regional Development Fund, EU) and the AEI (State Research Agency, Spain) under the contract TIN2016-76259-P.  
**Acknowledgments:** We express our gratitude to the staff of the Service of Library of the University of Extremadura, Spain, for their support and ease in accessing to the different bibliographic resources and databases.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.