The Production of Learning Analytics and Prediction of Academic Performance by Brazilian Researchers: A Systematic Literature Review

• Carlos Artur Guimarães UFRN
• Isabel Nunes UFRN
• Andressa Kroeff Pires UFRN http://orcid.org/0000-0002-4697-8456
• Eric Eduardo da Silva Alencar UFRN

DOI: https://doi.org/10.5753/ctrle.2020.11408

Abstract

Distance learning courses have increased its participation in Brazilian education. Expansion driven by the use of virtual environments, social inclusion, provision in underserved regions and lower cost of execution. Such courses present problematic such as: high dropout rates, failure and retention of students. Prediction is important to identify such problems and propose solutions for mitigation. This work is a Systematic Literature Review (SLR) on the Prediction of Academic Performance of studies published by Brazilian scientists in the research bases: Scopus, Science Direct, Scielo, IEEE and CEIE. Four articles were found that showed techniques, algorithms, practices and controlled studies.

Keywords: Learning analytics, Learning prediction, Academic performance, Systematic Literature Review, RSL

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