A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments-Reference-Cited by-同舟云学术

A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments

Published:2024 Issue: Volume:12 Page:103687-103703
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Masood Jafar Ali Ibrahim Syed¹^ORCID,Kalyan Chakravarthy N. S.²^ORCID,Asirvatham David³,Marjani Mohsen⁴^ORCID,Abdulkareem Shafiq Dalia⁵^ORCID,Nidamanuri Srinu²^ORCID

Affiliation:

1. Department of IoT, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

2. Center for Data Science, School of Computer Science and Engineering, QIS College of Engineering and Technology, Ongole, India

3. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Malaysia

4. Digital Innovation and Smart Society Impact Laboratory, Faculty of Innovation and Technology, School of Computer Science Taylor’s University, Subang Jaya, Malaysia

5. Digital Innovation and Smart Society Impact Laboratory, Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Malaysia

Funder

Taylor’s University and Iverson

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10613750.pdf?arnumber=10613750

Reference27 articles.

1. Predicting youth at high risk of aging out of foster care using machine learning methods

2. Innovative Approaches to Identifying and Responding to the Needs of High Risk Youth

3. Proactive, early screening to detect behaviorally at-risk students: Issues, approaches, emerging innovations, and professional practices

4. Educational data mining: prediction of students' academic performance using machine learning algorithms

5. A Predictive Model for Students’ Performance and Risk Level Indicators Using Machine Learning