'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Dropout prediction is a problem that is being addressed with machine learning algorithms;
thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict
the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as
which features should be selected and how to measure accuracy while considering whether the features are
appropriate according to the business context in which they are employed. To solve these questions, the
goal of this paper is to develop a systematic literature review to evaluate the development of existing studies
and to predict the dropout rate in contractual settings using machine learning to identify current trends and
research opportunities. The results of this study identify trends in the use of machine learning algorithms
in different business areas and in the adoption of machine learning algorithms, including which metrics are
being adopted and what features are being applied. Finally, some research opportunities and gaps that could
be explored in future research are presented.info:eu-repo/semantics/publishedVersio