Explainable Gradient Boosting for GPA Prediction with SHAP Aggregation
Abstract View: 61,
DOI:
https://doi.org/10.32665/statkom.v5i1.6381Keywords:
Grade Point Average, XGBoost, LightGBM, CatBoost, SHAP AggregationAbstract
Background: The Grade Point Average (GPA) is a complicated quantity impacted by psychological factors as well as the learning environment. Previous research has concentrated exclusively on understanding GPA prediction models through a singular predictive framework, resulting in inconsistent assessments of varied significance.
Objective: This study aims to propose a SHAP aggregation framework to explain the importance of features from three gradient boosting models, consisting of XGBoost, LightGBM, and CatBoost.
Methods: The dependent variable in this study is student GPA, whereas the predictor variables encompass emotional intelligence, adversity quotient, metacognitive ability, motivation, self-efficacy, and learning environment. The predictor variable was a composite score obtained from the average of the items of each latent variable. Data were collected through a survey of 270 students with probability sampling.
Results: The SHAP Importance Feature of the three gradient boosting models exhibited varying outcomes regarding the variables with the greatest and second-highest contributions. XGBoost and LightGBM recognized metacognitive capacity as the predominant attribute, while CatBoost highlighted adversity quotient. The SHAP aggregation results indicate that Adversity Quotient is the predominant factor, with a mean importance feature value of 0.0318.
Conclusion: The findings indicate that the SHAP aggregation framework offers a more stable and robust interpretation of variable importance compared to a single model. This methodology can also serve as an analytical framework in educational data mining research.
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