Identification of Relevant Characteristics for Designing Predictive Models of Student Performance
DOI:
https://doi.org/10.22491/1982-1654.135845Abstract
This work identified a subset of relevant features in a public dataset for each machine learning algorithm, and classified student performances more accurately. The public dataset contains information about student grades, absences, and demographic and social characteristics. In the proposed methodology, this work applied feature selection with the Recursive Feature Elimination (RFE) algorithm and machine learning algorithms based on Decision Tree, Gradient Tree Boosting, and Support Vector Machines (SVM) classification approaches. The model built from SVM reached the highest average of the F1 macro in the cross-validation, 0.8350 in the F1 macro score of the training. Already in the test phase, it reached 0.8365 in the F1 macro score. This model was built with fourteen attributes selected by the RFE. Finally, the analysis of results found that the identification of the most relevant characteristics to predict student performance depends on the learning approach of each model.
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