Rapid growth of smart phone industries has led people to use more technology and thus aided in adoption of information and communication technology (ICT) in educational purposes for enhancing students? performance. This chapter shows that students use social media platform or virtual environment for learning, especially in Open University or online learning system. In such environment, the students? drop rate is extremely high. This work primarily aims at reducing students? dropout or students? fails to finish course within prerequisite time using student behavior styles. For addressing research problems, this research aims in building efficient student behavior learning model for improving the performance of student applying machine learning (ML) models. The behavior extraction and study have been carried utilizing decision tree (DT) ML algorithm. Further, a model has been proposed for provisioning student contextual information to different students utilizing VLE platform interaction (collaborative learning) using DT algorithm which considered bagging. The DT with bagging is an ensemble learning (EL) model that depicts bootstrap aggregating (BA), which is modeled for enhancing accuracies and stabilities of every distinct predictive trees. Bagging aids DT in influencing overfitting problems and minimizes its variance. The proposed method is efficient in extracting learning styles and intrinsic behavior of students.
Part of the book: Social Media and Machine Learning