The meta-analysis of machine learning approaches and futures studies in improving the educational quality of military universities

Document Type : Original Article

Authors

1 Kharazmi University

2 Imama Ali Academy

3 Payame Noor University

Abstract

This research aims to conduct a comprehensive review and meta-analysis of previous studies on the use of machine learning to improve educational quality. The study seeks to identify gaps in existing research and provide new insights for optimizing educational methods through the application of machine learning. The research method involves a meta-analysis of prior studies in this field, revealing that most research has focused on the benefits of machine learning, but its challenges and limitations have not been thoroughly examined. The findings of this research indicate that machine learning can significantly contribute to enhancing educational quality. However, challenges such as data privacy issues, the need for advanced technological infrastructure, and potential resistance to adopting new technologies have not been fully explored. Additionally, the cultural and social impacts of using machine learning in educational settings, particularly in military academies, have received less attention. This lack of focus could have significant implications for the success or failure of implementing these technologies. The conclusion of this research focuses on providing practical recommendations and strategies for optimizing the use of machine learning in the education system. These recommendations can assist decision-makers and policymakers in developing and implementing more effective programs and strategies to enhance educational quality. By identifying and addressing existing gaps, this study paves the way for a more effective utilization of machine learning in improving educational quality.

Keywords

Main Subjects

References

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Defensive Researches and Management
Volume 23, Issue 106 - Serial Number 106
Winter 2024
March 2025
Pages 115-139

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History

  • Receive Date: 28 August 2024
  • Revise Date: 02 October 2024
  • Accept Date: 25 December 2024

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