Integration of Marxist Educational Perspectives and Educational Big Data in the Digital Age: Theoretical Logic and Practical Path
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Abstract
This paper addresses the contradiction between the capital-driven logic of educational big data application and the public and equitable essence of education emphasized in Marxist educational theory. A value-embedded educational data governance architecture is constructed for the digital age. The study generates synthetic data based on differential privacy to prevent uncompensated appropriation of raw student data, uses multidimensional behavioral feature engineering to move beyond score-centered evaluation, and constructs a two-branch adversarial learning model to reduce sensitive-attribute bias. A resource allocation algorithm constrained by minimizing the education Gini coefficient is then used to support compensatory justice. Results show that the average education Gini coefficient after resource allocation is 0.2707. The technical framework can regulate the disorderly expansion of capital logic in educational data use, shift data application from exploitation toward human development, and ensure that technological dividends are distributed more fairly to disadvantaged groups. The study provides a technical paradigm integrating value rationality and instrumental rationality in digital education governance.
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