Data-Driven Education Evaluation Using Multiple Linear Regression for Enhanced Fairness and Accuracy

Traditional education evaluation models rely on standardized test scores or teachers’ subjective scores, ignoring individual differences among students and comprehensive consideration of multiple factors, resulting in unfair evaluation. This paper uses multiple linear regression (MLR) to construct an education evaluation model, collects student data from different schools and regions and performs data cleaning, missing value filling and standardization to ensure data quality and consistency, providing a reliable basis for subsequent analysis. This paper applies the MLR model, establishes a regression equation based on the relationship between different variables collected and students’ academic performance, and uses the least squares method to train the model and optimize parameters to ensure that the regression model can fit the data well and reduce errors. It uses cross-validation to evaluate the regression model, analyzes the performance of the model on different data sets, uses residual analysis to check the rationality of the model assumptions, and makes necessary adjustments to the model. Regression coefficient analysis can identify each variable’s influence on student’s learning performance, providing educators with a more objective reference. The experimental results show that students’ academic performance is not only affected by test scores, but also closely related to their learning habits and class participation. In the residual analysis, the residuals are concentrated between [Formula: see text]10 and 10, and the distribution of MLR prediction errors is relatively uniform. In terms of the goodness of fit of the model, the average R 2 value of MLR on the test set is 0.86, which is relatively stable. The average MSE of MLR is 0.024, which shows strong prediction accuracy and improves fairness in the evaluation.