MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series

Abstract

High dropout rates have been a major problem affecting the development of Massive Open Online Courses (MOOCs). Student dropout prediction can help teachers identify students who are tending to fail and provide extra help in a timely manner, helping to improve the effectiveness of online learning. In recent years, the use of convolutional neural networks for dropout prediction has yielded good results. However, traditional convolutional neural networks use automatic feature extraction, which does not consider the importance of the learner's behavior features and the effect of the time series of behavior on dropout, so it is difficult to guarantee the final prediction effect. To solve this problem, this article proposes a convolutional neural network model FWTS-CNN that integrates feature weighting and behavioral time series. It extracts continuous behavioral features from the learner's log of learning activities, filters key features and ranks them by importance based on the decision tree, then weights the continuous behavioral features based on importance, and finally builds a convolutional neural network model based on behavioral time series and weighted features. Experiments on the KDD Cup 2015 dataset show that the FWTS-CNN dropout prediction model has a high accuracy, which can reach more than 87%, an improvement of about 2% over using the CNN algorithm alone. The FWTS-CNN model integrates the effects of behavioral features and behavior time on dropout, effectively improving the accuracy of dropout prediction.

Keywords

• Dropout prediction
• time series prediction
• feature engineering
• convolutional neural networks
• accuracy