Research on Constructing Online Learning Performance Prediction Model Combining Feature Selection and Neural Network

Abstract

Learning performance prediction can help teachers find students who tend to fail as early as possible so as to give them timely help, which is of great significance for online education. With the availability of online data and the continuous development of machine learning technology, learning performance prediction in large-scale online education is gaining new momentum. Traditional prediction methods include statistical methods, machine learning and neural networks. Among them, statistical methods and machine learning have low prediction efficiency. Although neural network can improve prediction efficiency, it ignores the impact of artificial feature filtering on model performance, and cannot find key factors for performance prediction, making predictions uninterpretable. Therefore, this paper proposes an online academic performance prediction model that integrates feature selection and neural network. Multiple linear regression analysis is used for feature extraction to obtain key influence features, and then deep neural networks is used for prediction. The results show that the F1 score of our model on large-scale data set is 99.25%, which is 1.25% higher than that of other related models.