Explainable XGBoost Prediction of Academic Impostor Phenomenon from Perfectionistic Concerns, Fear of Failure, and Self-Doubt
Keywords:
Academic impostor phenomenon, XGBoost, explainable artificial intelligence, SHAP, perfectionistic concerns, fear of failure, self-doubtAbstract
Objective: This study aimed to predict academic impostor phenomenon among Hungarian university students using an explainable XGBoost model based on perfectionistic concerns, fear of failure, and self-doubt.
Methods and Materials: This quantitative, cross-sectional predictive study was conducted among 468 university students enrolled in higher education institutions in Hungary. Data were collected using standardized self-report instruments measuring academic impostor phenomenon, perfectionistic concerns, fear of failure, and self-doubt. After preliminary data screening, descriptive statistics, reliability analysis, and Pearson correlation analysis were performed. The main predictive analysis was conducted using XGBoost regression, with academic impostor phenomenon entered as the continuous outcome variable and perfectionistic concerns, fear of failure, and self-doubt entered as predictors. Model performance was evaluated using root mean square error, mean absolute error, and coefficient of determination. SHAP analysis was applied to interpret the relative contribution of each predictor to the final model.
Findings: The correlation results showed significant positive associations between academic impostor phenomenon and self-doubt, r = .66, p < .001, perfectionistic concerns, r = .58, p < .001, and fear of failure, r = .54, p < .001. The XGBoost model demonstrated the strongest predictive performance compared with the baseline model, multiple linear regression, and random forest regression. The final XGBoost model obtained RMSE = 8.86, MAE = 6.91, and R² = .63, indicating that the model explained 63% of the variance in academic impostor phenomenon. SHAP-based interpretation showed that self-doubt was the most influential predictor, followed by perfectionistic concerns and fear of failure.
Conclusion: The findings indicate that academic impostor phenomenon can be predicted with considerable accuracy using an explainable XGBoost model. Self-doubt was the strongest predictor, suggesting that persistent uncertainty about academic competence is central to impostor-related experiences. Perfectionistic concerns and fear of failure also made meaningful predictive contributions, highlighting the importance of maladaptive achievement evaluation in students’ psychological vulnerability.
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