Structural Robustness of Decision Trees under Educational Data Sampling Variations

Abstract

Decision tree models are widely applied in educational data analysis due to their simplicity and interpretability. However, these models exhibit high sensitivity to variations in training data, where minor sampling changes can result in substantially different tree structures, potentially reducing model reliability and consistency. This study aims to empirically investigate the structural robustness of decision trees under variations in educational data sampling and to evaluate strategies for improving model stability. An experimental framework is implemented using several educational datasets processed through random subsampling, bootstrap resampling, and k-fold cross-validation. Structural robustness is quantitatively assessed using tree edit distance, node similarity ratio, and tree depth variability. The results indicate that small sampling perturbations can cause significant structural divergence, particularly in datasets characterized by high noise levels and feature correlations. Nevertheless, pruning techniques and ensemble-based stabilization methods effectively enhance structural consistency and reduce model variance. These findings highlight the importance of robustness evaluation in educational data mining and provide empirical insights for developing reliable, stable, and interpretable decision-support systems in educational environments.