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August 16, 2026Advanced ElectromagneticsOpen Access

Integration of Marxist Educational Perspectives and Educational Big Data in the Digital Age: Theoretical Logic and Practical Path

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Authors

JPJ. Pei

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Overview

Modeling study demonstrates an equitable educational data governance framework achieves an average education Gini coefficient of 0.2707, suggesting technological dividends can be distributed fairly.

Key Points

  • To establish a value-embedded data governance framework that reconciles educational big data deployment with equitable educational theory.
  • Generated synthetic data using differential privacy to prevent uncompensated exploitation of raw student records.
  • Engineered multidimensional behavioral features and built a two-branch adversarial learning model to minimize sensitive-attribute bias.
  • Implemented an educational resource allocation algorithm constrained by minimizing the education Gini coefficient.
  • Achieved an average education Gini coefficient of 0.2707 following algorithm-guided resource allocation.
  • Demonstrated qualitative reduction in sensitive-attribute bias and shifted big data usage toward equitable support for disadvantaged groups.

Cite This Study

J. Pei (2026) studied this question.

synapsesocial.com/papers/6a817a33f2fb91fc834ade45https://doi.org/10.7716/aem.v15i3.3367
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