Hybrid Machine Learning Algorithms at the Service of Student Performance

Abstract

Adil Korchi and Ahmed Abatal

The ability to alter and improve a student's status in order to get the greatest performance so that they pass their courses is an important component of today's educational landscape. This operation allows for the prediction of a student's performance in one or more disciplines. This has become possible nowadays through the use of Machine Learning algorithms that mine educational data to predict student performance by training the models and testing them with the available data set while using different algorithms.

In this study, we compared 9 algorithms in order to obtain the best models based on students’ performance in well-defined disciplines in order to improve their results and success in their study. We started with the data collection and then we carried out a preprocessing process, after which, we built models to compare and evaluate them. After that, we compared the obtained results showed that the Random forest had the best ranking and this, in almost all the methods used monitored by SVM which had satisfactory results.

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