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
