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  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Pioneering Predictive Models for Analyzing Educational Inequality: A Comparative Study of Random Forest, XGBoost, and LSTM

Murteza Hanoon Tuama

Department of Computer Techniques Engineering, Imam Al-Kadhum University College, Baghdad, Iraq.

Wahhab Muslim Mashloosh

Department of Computer Techniques Engineering, Imam Al-Kadhum University College, Baghdad, Iraq.

Yasir Mahmood Younus

Department of Computer Techniques Engineering, Imam Al-Kadhum University College, Baghdad, Iraq.

16 - 22 Vol. 11, Issue 1, Jan-Dec, 2025
Receiving Date: 2024-08-21;    Acceptance Date: 2024-10-01;    Publication Date: 2025-01-27
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http://doi.org/10.37648/ijiest.v11i01.003

Abstract

Educational inequality is a significant barrier to achieving justice in education around the world (Baker et al. 2020), and this study aims to fill this gap. This type of pattern and trend identification can similarly be developed using complex feature predictive models like Random Forest and XGBoost or LSTM methods. The results showed that this choice, together with the high accuracy (R2=0.9995) and the lowest predictive error (RMSE=0.006), made the model a powerful tool for understanding dynamic trends in inequality. Moreover, this study indeed modified one of the beginners' models using advanced approaches such as missing value imputation and data normalization which increased the credibility of used models. It contributes a new angle to the existing literature which is so far accounted through the lenses of comparative models by systematically comparing the three models. Educational policies rationalised in accordance with a data-driven analysis can take place to redistribute educational resources more effectively and ultimately prove to be more equitably beneficial to educational systems.

Keywords: Educational Inequality; Artificial Intelligence; Random Forest; XGBoost; LSTM; Educational Equity

    References

  1. Smith, J., & Taylor, K. (2022). AI-driven banking solutions: Enhancing customer experience. Journal of Financial Innovation, 14(3), 123–140.
  2. Zhang, L., & Wang, H. (2023). Blockchain in banking: Revolutionizing transactions. International Journal of Financial Technology, 9(2), 87–102.
  3. Chen, Y., & Zhao, Q. (2022). Predictive analytics in finance using AI. AI Applications in Banking, 18(5), 210– 225.
  4. Kumar, S., & Mehta, P. (2023). AI in fraud detection: Case studies and applications. International Journal of Financial Security, 7(4), 145–160.
  5. Lee, C., & Patel, M. (2022). Blockchain for secure banking transactions. Journal of Distributed Ledger Technology, 11(3), 65–78.
  6. Johnson, E., & Thomas, B. (2023). Cross-border payments and blockchain solutions. Financial Innovations Quarterly, 6(1), 33–50.
  7. Davis, A., & Lopez, S. (2023). Enhancing banking platforms with cloud technology. FinTech Journal, 13(3), 123–140.
  8. Singh, V., & Gupta, A. (2022). Big data analytics in banking: Opportunities and challenges. Journal of Banking Analytics, 8(2), 47–65.
  9. Ali, R., & Ahmed, T. (2023). Leveraging big data for customer insights in banking. Data Science and Finance Review, 14(1), 88–105.
  10. Carter, H., & Singh, S. (2022). Building resilience in a digital banking era. Strategic Finance Journal, 12(4), 140–155.
  11. Brown, L., & Chen, Y. (2023). Competitive strategies for banks in the fintech age. Financial Strategy Quarterly, 15(2), 45–60.
  12. Wong, K., & Lee, J. (2022). Successful integration of technology in banking: Case studies. Banking Technology Insights, 10(2), 67–82.
  13. Patel, V., & Zhang, T. (2023). Strategic implications of AI in banking operations. AI and Finance Journal, 9(1), 78–95.
  14. Springer, L., & Open, E. (2022). Educational data mining using machine learning models. Smart Learning Environments, 9(1), Article 40561. https://slejournal.springeropen.com/articles/10.1186/s40561-022-00192-z
  15. . IEEE, X., & Explore, A. (2023). Neural networks for predicting student performance. IEEE Transactions on Learning Technologies, 16(1), 72-83. https://ieeexplore.ieee.org/document/8447376
  16. Yao, X., & Zhang, H. (2022). Boosted tree models for education gap analysis. IEEE Transactions on Education, 15(3), 450 462. https://ieeexplore.ieee.org/document/10401207
  17. Liu, Q., Zhao, Y., & Chen, W. (2023). Hybrid AI models for analyzing education disparities. AIP Conference Proceedings, 2963, Article 020013. https://pubs.aip.org/aip/acp/article/2963/1/020013/2921074/A-role-ofmachine-learning-algorithm-in
  18. Zhang, T., & Liu, S. (2023). Advanced gradient boosting for education data analysis: Comparing CatBoost and XGBoost. Applied Sciences, 10(1), Article 90. https://www.mdpi.com/2076-3417/10/1/90
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