CLSA: A novel deep learning model for MOOC dropout prediction

MOOCs have attracted hundreds of millions of learners with advantages such as being cost-free and having flexible time and space. However, high dropout rates have become the main issue that hinders their further progress. To solve this problem, this research proposes a pipeline model named CLSA to predict the dropout rate based on learners' behavior data. The CLSA model first uses a convolutional neural network to extract local features and builds feature relations using a kernel strategy. Then, it feeds this high-dimensional vector generated by the CNN to a long shortterm memory network to obtain a time-series incorporated vector representation. After that, it employs a static attention mechanism on the vector to obtain an attention weight on each dimension. When tested on the KDD CUP 2015 dataset, our model reached 87.6% accuracy, which was higher than the previous best model (over 2.8%). The Fl-score of our model reached 86.9%, which was 1.6% higher than the previous state-of-the-art result.

Automated summary

MOOCs have attracted hundreds of millions of learners, but the high dropout rates hinder their further progress. A research proposes the CLSA model to predict dropout rates based on learners' behavior data, which achieved higher accuracy and Fl-score than previous models when tested on the KDD CUP 2015 dataset.