Annals of Computer Science and Information Systems, Volume 20

DOI: http://dx.doi.org/10.15439/2019F265

Citation: Communication Papers of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 20, pages 3–10 (2019)

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Abstract. Students' dropout is certainly one of the major problems that afflict educational institutions, the losses caused by the student's abandonment are social, academic and economic waste. The quest for its causes has been subject of work and educational research around the world. Several organizations seek strategic decisions to control the dropout rate. This work's goal is to evaluate the effectiveness of the most used data mining algorithms in the education area. An``in vivo'' controlled experiment was planned and performed to compare the efficacy selected classifiers. The Random Forest and SVM algorithms have stood out in this context, having, statistically similar accuracy (80.36\%, 81.18\%), precision (80.79\%, 80.25\%), recall (76.50\%, 77.51\%) and f-measure (78.86\%, 78.81\%) averages. The results showed evidence of significant differences between the algorithms, and also showed that, although the SVM had the best metric of accuracy and recall, it results were statistically similar with Random Forest results.

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