@article{N. C. A. Lima_A. de A. Fagundes_2020, place={Porto Alegre}, title={Educational DataMining: A Study of the Factors That Cause School Dropout in Higher Education Institutions in Brazil}, volume={18}, url={https://seer.ufrgs.br/index.php/renote/article/view/105950}, DOI={10.22456/1679-1916.105950}, abstractNote={<p>Context:In Brazil, there is a high dropout rate in higher education institutions. Thus, it is clear that evasion is a frequent problem and that it is necessary to analyze the factors that cause it to enable solutions that can mitigate/ reduce this problem. Objetive: (1)perform a correlation analysis (Pearson and Spearman) of the educational factores of the School Census; (2)propose school dropout prediction models taking into account educational and economic factors using regression methods (linear, robust, ridge, lasso, clusterwise regression). Methodology: used the phases of the CRISP-DM methodology. Results: the factors related to not allowing financial assistance are related to as evasion, namely: food, permanence, didactic material, transportation. There are also factors related to the study period. The regression robust and linear regression show fewer errors. Conclusion: the correlations used present the selection of factors in a similar way, thus following a linear distribution. This study can help to create more investment in public policies, as it ratifies factors are related to this dropout problem.</p>}, number={1}, journal={Revista Novas Tecnologias na Educação}, author={N. C. A. Lima, Marília and A. de A. Fagundes, Roberta}, year={2020}, month={jul.} }