Predicting Academic Performance using a Multiclassification Model: Case Study

Now-a-days predicting the academic performance of students is increasingly possible, thanks to the constant use of computer systems that store a large amount of student information. Machine learning uses this information to achieve big goals, such as predicting whether or not a student will pass a course. The main purpose of the work was to make a multiclassifier model that exceeds the results obtained from the machine learning models used independently. For the development of our proposed predictive model, the methodology was used, which consists of several phases. For the first step, 557 records with 25 characteristics related to academic performance were selected, then the preprocessing was applied to said data set, eliminating the attributes with the lowest correlation and those records with inconsistencies, leaving 500 records and 9 attributes. For the transformation, it was necessary to convert categorical to numerical data of four attributes, being the following: SEX, ESTATUS_lab_padre, ESTATUS_lab_madre and CONDITION. Having the data set clean, we proceeded to balance the data, where 1,167 data were generated, using the 2/3 for training and the remaining 1/3 for validation, then the following techniques were applied: Extra Tree, Random Forest, Decision Tree, Ada Boost and XGBoost, each obtained an accuracy of 57.41%, 61.96%, 91.44%, 59.65% and 83.3% respectively. Then the proposed model was applied, combining the five algorithms mentioned above, which reached an accuracy of 92.86%, concluding that the proposed model provides better accuracy than when the models are used independently meaning that it was the one that obtained the best result.