FairEduNet Algorithm Design and Teaching Assessment Calibration Considering Equity in Higher Education

Jing Wang

doi:10.3791/70189

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Research Article

FairEduNet Algorithm Design and Teaching Assessment Calibration Considering Equity in Higher Education

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DOI:

10.3791/70189

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July 21st, 2026

Jing Wang¹

¹School of Civil Engineering and Architecture, The Open University of Shaanxi

Summary

The research aims to provide a new approach for teaching assessment calibration and educational equity in intelligent education. Experimental results indicate that the proposed model significantly outperforms comparative models, effectively addressing issues of poor multi-source data integration and fairness bias in existing calibration methods.

Abstract

Current teaching assessment calibration methods face challenges such as low integration efficiency when processing multi-source heterogeneous assessment data, insufficient adaptability across different disciplines and teaching scenarios, and biases in results with weak fairness guarantees. The research aims to construct an interpretable, transferable, and scalable fairness-correction framework for deep modeling and dynamic correction of teaching assessment data. These issues make it difficult to meet the core requirement of balanced teaching in intelligent education. This study proposes a fairness-oriented educational network algorithm that integrates a generative adversarial network and a gradient boosting decision tree. The algorithm constructs a fairness calibration mechanism and combines a bidirectional encoder representation model with a graph attention network to optimize feature extraction and dynamic adaptability, improving multi-source data processing efficiency and fairness calibration accuracy. This approach achieves precise calibration and fairness assurance in teaching assessments. In the indicator test, the model achieved an accuracy of 98.76% in clustering evaluation features, 98.05% in fairness correction, and 0.968 in cross-scenario correction consistency in the validation set. In the loss value testing task, the total loss of the model decreased from 0.1237 to 0.1018 during the teaching evaluation correction task in the validation set. In the indicator test, the model achieved an accuracy of 98.76% in clustering evaluation features, 98.05% in fairness correction, and 0.968 in cross-scenario correction consistency in the validation set. Experimental results indicate that the proposed model significantly outperforms comparative models, effectively addressing issues of poor multi-source data integration and fairness bias in existing calibration methods. Furthermore, it provides innovative algorithmic frameworks for technology developers and reliable technical tools for educational administrators to promote teaching equity. The research contributions lie in: (i) integrating three core modules, including multi-source data preprocessing, dynamic fairness index verification, and explainability visualization; and (ii) proposing a fairness correction paradigm based on the collaborative optimization of generative adversarial networks and gradient boosting trees, which breaks through the limitations of traditional single-model correction and enables decoupled learning of deviation modeling and correction decision-making.

Introduction

Teaching evaluation and calibration form the core of ensuring teaching quality in intelligent education. Through dynamic analysis, error correction, and result calibration, they provide a basis for instructional improvement and personalized learning. Their accuracy and fairness directly affect whether intelligent education can break through technological biases and provide balanced educational support¹^,². However, existing methods have several shortcomings: they rely on a single data source, ignore the actual situation, and lead to data bias and correction bias. Lack of a dynamic fairness adaptation mechanism ....