Application of data mining to understand some factors that influence student dropout
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Keywords

data mining
student dropout
engineering development

How to Cite

Carrasco Vega, Y. L., & Carril-Verastegui, B. D. (2023). Application of data mining to understand some factors that influence student dropout. Athenea Engineering Sciences Journal, 4(12), 7-13. https://doi.org/10.47460/athenea.v4i12.53

Abstract

The research aims to identify applying data mining to identify the main factors that influence the dropout of university students in public universities in Latin America. A documentary analysis was carried out to contextualize the problem of student desertion, and relevant antecedents on the subject were presented. The study's main findings identified that socioeconomic problems, institutional conditions, and social and cultural environment situations are the main factors influencing student dropout in public universities in Latin America. Finally, it is possible to affirm that data mining is helpful for different engineering applications that contribute to the attention of social problems.

https://doi.org/10.47460/athenea.v4i12.53
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References

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