Comparing Databases for the Prediction of Student’s Academic Performance using Data Science on the Novel Educational Model Tec21 at Tecnológico de Monterrey

Abstract

Many studies have been made on the prediction of student's academic performance using Data Science. The students with poor academic performance as well as dropout students make a huge impact on the graduation rates, reputation, and finances of an educational institution. These studies take the advantage of the digitization of the admission and academic data of the students and the increasing computational power. However, since August 2019 Tecnologico de Monterrey has been doing it using entrance tests called Initial Evaluations. Unfortunately, the Initial evaluations did not provide useful predictions for the students of the fall semester in 2019. Therefore, this study aimed to compare the Initial Evaluations and the admissions data using Data Science models to predict the student's academic performance. The admission data was composed of five databases: Initial Evaluations, Emotions, Curriculum, Admission Exam and Grades of the first semester. A similar methodology to Cross Industry Standard Process for Data Mining was used to compare the models based on admission data and the models based only Initial Evaluations. A large number of experiments were carried out combining different data of admissions, feature reduction techniques and classification models. The experiments showed that the models based on admission data predicts the student's academic performance with higher accuracy than the models based only on Initial Evaluations. Nevertheless, some variables of the Initial Evaluations were relevant to the models based on admission data. Moreover, the accuracy of the experiments was in the range of the results from the related studies. The results of this study indicates that the Initial Evaluations provide useful information for the prediction of student's academic performance in the domain of Data Science.