CGDC- LSTM: A novel hybrid neural network model for MOOC dropout prediction

Dropout prediction is an important task due to the high attrition rate commonly found on the massive open online courses (MOOC) platforms. Researchers usually use neural networks to establish various prediction models based on the behavioral features of student data. However, the existing methods ignore the periodic feature of learning behaviors and the influence of learning time distribution information on the prediction results, resulting in the potential association relationship between the input data is not learned by the model. Thus, after in-depth analysis of MOOC learners' behavior data, this paper proposes the concept of periodic feature, and found that different learning time has different effects on the prediction results. Based on the gained insights, we propose a hybrid neural network model (CGDC-LSTM) to model and to predict users' dropout behavior. CGDC-LSTM utilizes Convolutional Neural Network (CNN) to maintain the local correlation of students' behavior, and uses a module combining Group Convolution and Dilated Causal Convolution to fit the periodic feature of students, and combines Long Short-Term Memory Network (LSTM) into the model to extract the learning time distribution information to capture the influence of different learning periods on the results. Experimental results on the KDD Cup 2015 dataset demonstrate that the proposed model shows better prediction performance compared to baseline methods.