Towards the Selection of Best Machine Learning Model for Student Performance Analysis and Prediction

Educational Data Mining (EDM) has become one of the most important fields now a day because with the development of technology, student's problems are also increasing. In order to tackle these problems and help students, educational data mining has come into existence. In this research paper, a Systematic Literature Review (SLR) has been carried out to get 20 studies (2012-2019) in the field of EDM. From these studies, 11 highly advanced machine learning models have been obtained and we have implemented them on 2 public student databases in order to predict their future outcomes. Feature extraction techniques have been applied and then models have been trained based on these databases to get the required results. Results of different machine learning models have been compared in order to find out the best model among them based on. With these experiments, weak students can be easily identified and proper precautions can be taken in order to help them.