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

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