Online At-Risk Student Identification Using RNN-GRU Joint Neural Networks

Online At-Risk Student Identification Using RNN-GRU Joint Neural Networks

Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is...

Full description

Bibliographic Details
Main Authors: Yanbai He, Rui Chen, Xinya Li, Chuanyan Hao, Sijiang Liu, Gangyao Zhang, Bo Jiang
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Information
Subjects:
recurrent neural network (RNN)
performance prediction
virtual learning environment (VLE)
binary classification
gated recurrent unit (GRU)
long short-term memory (LSTM)
Online Access:https://www.mdpi.com/2078-2489/11/10/474

Internet

https://www.mdpi.com/2078-2489/11/10/474

Similar Items