Psychological emotions-based online learning grade prediction via BP neural network - Xiao, Jiongen; Teng, Hongqing; Wang, Han; Tan, Jianxing (Frontiers in Psychology, 2022)
With the rapid developments and the reform of educational mode, online education receives wide approval and application. Various types feature data of learning behaviors will be generated in the process of online learning. The feature data reflects learning investments and attitudes. Learning behaviors have high correlation with learning grades, so the deep analysis on the patterns in feature data can provide teaching aids for education managers. The random forest algorithm is applied to select six important factors which have greater influence on learning grades from twelve factors. Eleven of the twelve factors are related to learning behaviors while the rest one factor is related to psychological quality test. The six important factors are utilized to build the learning grade prediction model based BP neural network. The research in this paper helps teachers monitor students’ learning status in real time to discover abnormal learning behaviors and problems in time, so that timely and effective teaching intervention and adjustment can be provided according to students’ abnormal status.