An Integrated Framework With Feature Selection for Dropout Prediction in Massive Open Online Courses

Abstract

Massive open online courses (MOOCs) have flourished in recent years, which is conducive to the redistribution of high-quality educational resources globally. However, the high dropout rate in the course of operation has seriously affected its development. Therefore, in order to improve the degree of completion, it is an effective way to study how to effectively predict the dropout in MOOCs and intervene in advance. Traditional methods rely on manually extracted features, which is difficult to guarantee the final prediction effect. In order to solve this problem, this paper proposes an integrated framework with feature selection (FSPred) to predict the dropout in MOOCs, which includes feature generation, feature selection, and dropout prediction. Specifically, FSPred applies a fine-grained feature generation method in days to generate features and then uses an ensemble feature selection method to select valid features and feed them into a logistic regression model for prediction. Extensive experiments on a public data set have shown that FSPred can achieve the comparable results with other dropout prediction methods in terms of precision, recall, F1 score, and AUC score. Finally, through the analysis of the features of the final selection, the suggestions for the construction of the MOOCs are put forward.