Using Data Mining Techniques To Enhance The Student Performance. A semantic review.

Abstract

The number of higher education institutions has dramatically increased in recent years, producing many graduates/postgraduates each year. One of the key concerns of the decision-makers is student performance. Educational data mining techniques are a very useful way to explore the uncovered data in the data itself and create a pattern in order to analyze student performance. This study presents an analysis and investigation of recent papers in the field of educational data mining from 2020 to 2022. The goal was to identify the factors that influence student performance as well as the most significant educational data mining methods used by researchers. The study concluded that student behaviors have the greatest influence on academic performance. Furthermore,.the most popular classifiers used to predict student performance are decision trees, multilayer perception, and support vector machines.