Using Stacked Denoising Autoencoder for the Student Dropout Prediction

This paper extended Stacked Denoising Autoencoder to build a deep neural network which initialized the weight of neural network through the encoder's weight and used Dropout to reduce the error rate in fine-tuning stage. The neural network used the information of students in recent years as input data to train neural network, and predicted the possibility of dropout on the students during the semester. The prediction result can be used to counseling and warning students which be dropout likely and then reduced the unnecessary resource of school.