Predicting At-Risk Students Using the Deep Learning BLSTM Approach-Reference-Cited by-同舟云学术

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Predicting At-Risk Students Using the Deep Learning BLSTM Approach

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH)
language:
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Author:

Souai Wiem¹,Mihoub Alaeddine²,Tarhouni Mounira¹,Zidi Salah¹,Krichen Moez³,Mahfoudhi Sami²

Affiliation:

1. University of Gabes,Laboratory Hatem Bettaher (IRESCOMATH),Gabes,Tunisia

2. College of Business and Economics, Qassim University,Department of Management Information Systems and Production Management,Buraidah,Saudi Arabia,51452

3. Albaha University,FCSIT,Albaha,Saudi Arabia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9842388/9842335/09842428.pdf?arnumber=9842428

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predictive Modelling with the Open University Learning Analytics Dataset (OULAD): A Systematic Literature Review;Communications in Computer and Information Science;2024

2. Early Prediction of Learners At-Risk of Failure in Online Professional Training Using a Weighted Vote;Lecture Notes in Computer Science;2023

3. Development of an intelligent system based on metaverse learning for students with disabilities;Frontiers in Robotics and AI;2022-12-06

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