News

 Statistics

Number of Journals34
Number of Issues1,306
Number of Articles9,427
Article View9,188,652
PDF Download5,620,944
Ghasemi, A., abbasian, M., esmaeili, M. (2024). The meta-analysis of machine learning approaches and futures studies in improving the educational quality of military universities. Electronic and Cyber Defense, 23(106), 115-139.
amirhossein Ghasemi; mohammad abbasian; mahdi esmaeili. "The meta-analysis of machine learning approaches and futures studies in improving the educational quality of military universities". Electronic and Cyber Defense, 23, 106, 2024, 115-139.
Ghasemi, A., abbasian, M., esmaeili, M. (2024). 'The meta-analysis of machine learning approaches and futures studies in improving the educational quality of military universities', Electronic and Cyber Defense, 23(106), pp. 115-139.
Ghasemi, A., abbasian, M., esmaeili, M. The meta-analysis of machine learning approaches and futures studies in improving the educational quality of military universities. Electronic and Cyber Defense, 2024; 23(106): 115-139.

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

مدیریت و پژوهش‌های دفاعی
Article 5, Volume 23, Issue 106, March 2025, Pages 115-139    PDF (1.21 M)
Document Type: Original Article
Authors
amirhossein Ghasemi1; mohammad abbasian* 2; mahdi esmaeili3
1Kharazmi University
2Imama Ali Academy
3Payame Noor University
Receive Date: 28 August 2024,  Revise Date: 02 October 2024,  Accept Date: 25 December 2024 
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
machine learning algorithms; performance assessment; military university; Meta-Analysis; educational improvement
References
رضایی، عباسعلی، و زاهدی، محمدهادی. (1397). نقش فن آوری های نوین در پیشرفت آموزش های الکترونیکی (با نگاهی به فرصت‌ها و چالش‌های پیش رو در دانشگاه ها و مراکز آموزش عالی داخل). پژوهش در نظام‌های آموزشی، 12(40 )، 207-224.

شکاری، عباس، محمدی، زهرا، و محمدی، بهادر. (1396). تاثیر استفاده از فناوری های نوین آموزشی برکیفیت فعالیت‌های آموزشی دبیران. پژوهش در برنامه‌ریزی درسی (دانش و پژوهش در علوم تربیتی- برنامه‌ریزی درسی)، 14(25 (پیاپی 52))، 74-83.

قاسمی ابیانه، امیرحسین، عباسیان، محمد و اسمائیلی، مهدی . (1403). مروری بر ادبیات موجود در زمینه بکارگیری رویکردهای یادگیری ماشین در ارتقای کیفیت آموزشی. مدیریت سرمایه انسانی دفاعی، 4(3).

محمودی، ف.، طهماسب زاده شیخلار، د.، و فرج پور، ف. (۱۴۰۰). ارزیابی میزان توجه به فرایند خلاق در دروس عملی رشته پرستاری از دیدگاه دانشجویان دانشگاه علوم پزشکی تبریز. راهبردهای آموزش در علوم پزشکی، 14(5)، ۲۵۰-۲۵۹.

Abdulellah A. Alsulaimani Department. (2023). Modeling and Classification of Student Performance Based on a Machine Learning Model. Information Sciences Letters, 12(12), 2869–2875. https://doi.org/10.18576/isl/121228

Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics in distance education: A systematic literature review. In Online Learning (Vol. 20, Issue 2, pp. 13–29).

Bulut, O., Cormier, D. C., & Yildirim-Erbasli, S. N. (2022). Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach. In Information (Switzerland) (Vol. 13, Issue 8). https://doi.org/10.3390/info13080400

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Dwijuliani, R., Rijanto, T., Munoto, Nurlaela, L., Basuki, I., & Maspiyah. (2021). Increasing student achievement motivation during online learning activities. Journal of Physics: Conference Series, 1810(1), 012072. https://doi.org/10.1088/1742-6596/1810/1/012072

Evrenoglou, T., Metelli, S., & Chaimani, A. (2022). Introduction to Meta-Analysis. In Principles and Practice of Clinical Trials (pp. 2179–2195). https://doi.org/10.1007/978-3-319-52636-2_287

Gafarov, F. M., Rudneva, Y. B., Sharifov, U. Y., Trofimova, A. V., & Bormotov, P. M. (2020). Analysis of Students’ Academic Performance by Using Machine Learning Tools. https://doi.org/10.2991/assehr.k.200509.104

Galán, J. J., Carrasco, R. A., & LaTorre, A. (2022). Military Applications of Machine Learning: A Bibliometric Perspective. Mathematics, 10(9), 1397. https://doi.org/10.3390/math10091397

Garvasiuk, O. V., Namestiuk, S. V., Tkachuk, S. S., Guz, L. O., Velyka, A. Y., & Lapa, G. M. (2023). THE EFFECTIVENESS OF INTERACTIVE METHODS IN THE EDUCATIONAL PROCESS. Clinical and Experimental Pathology, 22(2). https://doi.org/10.24061/1727-4338.XXII.2.84.2023.13

Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A Systematic Review of Deep Learning Approaches to Educational Data Mining. Complexity, 2019, 1–22. https://doi.org/10.1155/2019/1306039

Kabakchieva, D. (2013). Predicting Student Performance by Using Data Mining Methods for Classification. Cybernetics and Information Technologies, 13(1), 61–72. https://doi.org/10.2478/cait-2013-0006

Mastour, H., Dehghani, T., Jajroudi, M., Moradi, E., Zarei, M., & Eslami, S. (2023). Prediction of medical sciences students’ performance on high-stakes examinations using machine learning models: a protocol for a systematic review. BMJ Open, 13(5), e064956. https://doi.org/10.1136/bmjopen-2022-064956

Mian, Y. S., Khalid, F., Qun, A. W. C., & Ismail, S. S. (2022). Learning Analytics in Education, Advantages and Issues: A Systematic Literature Review. In Creative Education (Vol. 13, Issue 09, pp. 2913–2920). https://doi.org/10.4236/ce.2022.139183

NADTOCHIY, Y. (2022). factors-affecting-the-quality-of-education-given-the-new-innovation-spheres-of-economy.pdf. https://doi.org/10.24234/wisdom.v23i3.861 Yulia

Nafea, I. T. (2018). Machine Learning in Educational Technology. In Machine Learning - Advanced Techniques and Emerging Applications. InTech. https://doi.org/10.5772/intechopen.72906

Oreshin, S., Filchenkov, A., Petrusha, P., Krasheninnikov, E., Panfilov, A., Glukhov, I., Kaliberda, Y., Masalskiy, D., Serdyukov, A., Kazakovtsev, V., Khlopotov, M., Podolenchuk, T., Smetannikov, I., & Kozlova, D. (2020). Implementing a Machine Learning Approach to Predicting Students’ Academic Outcomes. 2020 International Conference on Control, Robotics and Intelligent System, 78–83. https://doi.org/10.1145/3437802.3437816

Sampson, L., Jiang, T., Gradus, J. L., Cabral, H. J., Rosellini, A. J., Calabrese, J. R., Cohen, G. H., Fink, D. S., King, A. P., Liberzon, I., & Galea, S. (2021). A Machine Learning Approach to Predicting New-onset Depression in a Military Population. In Psychiatric Research and Clinical Practice (Vol. 3, Issue 3, pp. 115–122). https://doi.org/10.1176/appi.prcp.20200031

Stefan AD Popenici, S. K. (2017). Exploring the impact of artificial intelligence on teaching and learning in...: Find articles, books, journals and more.

Susiani, K., Suastra, I. W., & Arnyana, I. B. P. (2022). Study of improving the quality of learning in an effort to improve the quality of elementary school education. In Jurnal EDUCATIO: Jurnal Pendidikan Indonesia (Vol. 8, Issue 1, p. 37). https://doi.org/10.29210/1202221786

Villegas-Ch, W., Román-Cañizares, M., & Palacios-Pacheco, X. (2020). Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS. Applied Sciences, 10(15), 5371. https://doi.org/10.3390/app10155371

Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z

Zafari, M., Sadeghi-Niaraki, A., Choi, S. M., & Esmaeily, A. (2021). A practical model for the evaluation of high school student performance based on machine learning. In Applied Sciences (Switzerland) (Vol. 11, Issue 23). https://doi.org/10.3390/app112311534

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? In International Journal of Educational Technology in Higher Education (Vol. 16, Issue 1). https://doi.org/10.1186/s41239-019-0171-0

Акмеше, О. Ф., Кьор, Х., & Ербей, Х. (2021). Use of Machine Learning Techniques for the Forecast of Student Achievement in Higher Education. In Information Technologies and Learning Tools (Vol. 82, Issue 2, pp. 297–311). https://doi.org/10.33407/itlt.v82i2.4178

Statistics
Article View: 202
PDF Download: 60