Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques-Reference-Cited by-同舟云学术

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Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques

Published:2024 Issue: Volume:12 Page:122377-122400
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Goran Radic¹^ORCID,Jovanovic Luka²^ORCID,Bacanin Nebojsa²^ORCID,Stanković Miloš S.²,Simic Vladimir³^ORCID,Antonijevic Milos²^ORCID,Zivkovic Miodrag²^ORCID

Affiliation:

1. ITS—Information Technology School, Comtrade Technology Center, Belgrade, Zemun, Serbia

2. Department of Informatics and Computing, Singidunum University, Belgrade, Serbia

3. Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia

Funder

Science Fund of the Republic of Serbia

Characterizing crises-caused air pollution alternations using an artificial intelligence-based framework—crAIRsis

Intelligent Multi-Agent Control and Optimization applied to Green Buildings and Environmental Monitoring Drone Swarms—ECOSwarm

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10640075.pdf?arnumber=10640075

Reference51 articles.

1. Motives for dropping out from higher education—An analysis of bachelor's degree students in Germany

2. Why do open and distance education students drop out? Views from various stakeholders

3. Prediction of University Dropout Using Machine Learning

4. Factors mitigating the decline of motivation during the first academic year: a latent change score analysis

5. Factors contributing to university dropout: a review

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