Hybrid Neural Network Model for MOOC Dropout Prediction

Within the context of Massive Open Online Courses (MOOCs), the application of Human-in-the-loop systems is becoming increasingly prevalent. Given the issue of high dropout rates in MOOCs, developing accurate dropout prediction models is essential to improve educational outcomes and ensure the sustainability of the platform. Researchers usually use interactive information between students and platforms to build predictive models. However, existing methods have the problem that the single construction of behavioral characteristics leads to misclassification of special student groups. Therefore, after in-depth analysis of the behavioral data of MOOC learners, this paper constructs the characteristics of learning activity based on the two dimensions of learning time and number of clicks to reduce the misclassification of special groups. On this basis, we propose a hybrid neural network model (CNN-LSTMAE) to model and predict dropout behavior. CNN-LSTMAE uses the Convolutional Neural Network (CNN) to extract local features of students' behaviors, and uses the Long-Short-Term Memory Autoencoder (LSTMAE) module to perform a high-level abstract representation of local features. Experimental results on the KDD Cup 2015 dataset show that the proposed model has better predictive performance than the baseline methods.