A Literature Review of Student Performance Prediction in E-Learning Environment

Abstract

With the advancement in e-learning technologies, students are able to empower themselves by interacting with eLearning environment so that the instructor may not be the gatekeeper of education. This paper aims at reviewing the student prediction performance of students based on their interactivity with the eLearning activities in MOODLE and MOOCs, this was achieved with the use of the student log files and some additional data about the particular student. The performance prediction was investigated using Decision Tree (C4.5 algorithm), Artificial Neural Network, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithm techniques, in order to find the best technique for the student’s prediction. In addition, the analysis of log files indicates that the rate of interactivity with e-learning environment has a significant impact on their performance as the students with highest interactivity on the MOODLE tend to have higher performance than those with low interactivity rate. From the analysis we can observe that the students spend more time on e-learning MOODLE than MOOCs, and because of that they are missing advantages of the available resources on MOOCs such as watching lecture videos, participating in quizzes, which may assist them in their study.