Identifying at-risk students based on the phased prediction model

Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students' individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%.