SEPN: A Sequential Engagement Based Academic Performance Prediction Model
Xiangyu Song, Jianxin Li, Shijie Sun, Hui Yin, Phillip Dawson, Robin Doss
Abstract
Students' performance prediction is a crucial task in today's online education. By predicting a student's final grade in an academic examination, intervene can be applied in advance. Recently, many machine learning models have been designed to couple students' online activity with their academic performance. However, it is difficult for these models to effectively make prediction due to the excessive difference in feature selection. While in most cases, too many parameters and heterogeneous features can also be one of the main sticking points. To this end, we propose a sequential engagement based academic performance prediction network. It consists of two main components: an engagement detector and a sequential predictor. The engagement detector leverages the advantages of a convolutional neural network to detect students' engagement patterns through their daily activities. The sequential predictor adopts the structure of long short-term memory and learns the interaction from the engagement feature spaces and demographic features. By comparing with various existing advanced machine learning models, the results show that this method has better performance than the existing ones when involving the engagement detection mechanism.