Predicting Students' Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School

Abstract

The ability to predict students' performance tendency is very important to improve their learning skills. For this, Educational Data Mining (EDM) is a more active research field. It aims to find useful information from the educational data set using data extraction techniques. The most important EDM tasks for this study are to predict student performance. The overall goal of EDM is to understand how students learn and identify those aspects that can improve teaching and learning aspects. This paper reviews some existing research and identifies other future pathways based on EDM knowledge. Therefore, the purpose of this study is to describe how EDM techniques can help math teachers identify students who are most likely to fail and then take appropriate action, and change strategies for it to improve the performance of their students in this area.The ability to predict students' performance tendency is very important to improve their learning skills. For this, Educational Data Mining (EDM) is a more active research field. It aims to find useful information from the educational data set using data extraction techniques. The most important EDM tasks for this study are to predict student performance. The overall goal of EDM is to understand how students learn and identify those aspects that can improve teaching and learning aspects. This paper reviews some existing research and identifies other future pathways based on EDM knowledge. Therefore, the purpose of this study is to describe how EDM techniques can help math teachers identify students who are most likely to fail and then take appropriate action, and change strategies for it to improve the performance of their students in this area.