Prediction of students' performance in e-learning environment using random forest

The need for advancement in e-learning technology causes educational data to become very huge and increase rapidly. The data is generated on daily basis as a result of students’ interaction with learning management systems. The data contains hidden information about participation of students in various activities of e-learning which when revealed can be used to associate with the students’ performance. Predicting the performance of students based on the use of e-learning system in educational institutions is a major concern and has become very important for education managements to better understand why so many students perform poorly or even fail in their studies. However, it is difficult to do the prediction due to the diverse factors or characteristics that influence their performance. This paper is aimed at predicting students’ performance by considering the students interaction in e-learning environment, their assessment marks and prerequisite knowledge as prediction features. Random Forest algorithm has been used for the prediction. Results show that the algorithm outperforms the popular decision tree and K-Nearest Neighbor algorithms. In addition to the performance prediction, the research findings also revealed most significant attributes that influences students’ performance