A Novel Approach for Analysis and Prediction of Students Academic Performance Using Machine Learning Algorithms

Abstract

Educational data mining has become an efective tool for exploring the hidden relationships in educational data and predicting students’ academic performance. The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. To achieve qualitative education standard, several attempts have been made to predict the performance of the student, but the prediction accuracy is not acceptable. The main purpose of this research is significantly predict the student performance to improve the academic results. In order to accomplish the prediction with supplementary exactness, XGBoost based methods have been adopted. This work introduces a novel hybrid Lion-Wolf optimization algorithm to solve the problem of feature selection. Two level overlap improves the exploitation part. First phase overlap is used for feature selection and second phase used for adding some more important information and improve the classification accuracy. The XGBoost classifier improved the classification accuracy which is most famous classifier based on wrapper method. XGboost model using two different parameter adjustment methods are compared. XGBoost based on hybrid Lion-Wolf optimization performs better than traditional XGBoost on training accuracy and efficiency. Experiments are applied using the dataset and results prove that proposed algorithm outperform and provide better results