Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining

This paper aims to predict the user dropout rate in MOOC learning based on the features extracted from user learning behaviours. For this purpose, some learning behaviour features were extracted from the data of MOOC platforms. Then two machine learning algorithms, respectively based on support vector machine (SVM) and the artificial neural network (ANN), were introduced to predict the dropout rate of MOOC course. The two algorithms were contrasted with some commonly used prediction methods. The comparison results show that our algorithms outperformed others in the prediction of MOOC user dropout rate. The research sheds new light on the feature extraction and learning effect of MOOC programs.