Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to (i) combine these data to obtain a holistic view of a student, (ii) use these data to accurately predict academic performance,...

Full description

Bibliographic Details
Main Authors: Liang Zhao, Kun Chen, Jie Song, Xiaoliang Zhu, Jianwen Sun, Brian Caulfield, Brian Mac Namee
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Academic performance prediction
behavioral pattern
digital campus
machine learning (ML)
long short-term memory (LSTM)
Online Access:https://ieeexplore.ieee.org/document/9118933/

Internet

https://ieeexplore.ieee.org/document/9118933/

Similar Items