Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence
Wen Xiao, Juan Hu
Abstract
Predicting students’ performance is one of the most important issues in educational data mining. In this study, a method for representing students’ partial sequence of learning activities is proposed, and an early prediction model of students’ performance is designed based on a deep neural network. This model uses a pre-trained autoencoder to extract latent features from the sequence in order to make predictions. The experimental results show that: (1) compared with demographic features and assessment scores, 20% and wholly online learning activity sequences can achieve a classifier accuracy of 0.5 and 0.84, respectively, which can be used for an early prediction of students’ performance; (2) the proposed autoencoder can extract latent features from the original sequence effectively, and the accuracy of the prediction can be improved more than 30% by using latent features; (3) after using distance-based oversampling on the imbalanced training datasets, the end-to-end prediction model achieves an accuracy of more than 80% and has a better performance for non-major academic grades.