Data Mining and Machine Learning techniques applied to student dropout: a systematic literature mapping
DOI:
https://doi.org/10.5753/rbie.2024.3296Keywords:
Systematic literature mapping, Student dropout, School dropout, Data mining, Machine learningAbstract
This work presents a Systematic Mapping of the Literature on student dropout, from which we sought to answer the following research question: What tools, machine learning techniques, inducing factors, open databases, and algorithm evaluation metrics have been used to identify the possible causes of student dropout? The mapping protocol was developed based on the guidelines of Petersen (2008) and Kitchenham (2004). Thus, it consisted of defining research questions, selection criteria, search strings, and search sources, among other elements. Among the results, it is worth noting that the R tool was the most widely used, classification stood out among the machine learning techniques and the main works in the area focused on studying factors related to individual student characteristics. Additionally, 15 open databases were identified. Finally, regarding algorithm evaluation metrics, the following stand out: Recall, Accuracy, and Precision. The results of this mapping provide a comprehensive view of the state of the art from research on student dropout, including the most popular tools and techniques, and the most investigated inducing factors. Researchers can use the results of this study to direct research efforts toward the creation of models using the three types of inducing factors and the provision of open bases.
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Copyright (c) 2024 Fernanda Ferreira do Nascimento, Lucas Cesar de Oliveira Dantas, Angélica Félix de Castro, Paulo Gabriel Gadelha Queiroz
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