A Feature Importance-Based Multi-Layer CatBoost for Student Performance Prediction

Student performance prediction is vital for identifying at-risk students and providing support to help them succeed academically. In this paper, we propose a feature importance-based multi-layer CatBoost approach to predict the students' grade in the period exam. The idea is to construct a multi-layer structure with increasingly important features layer by layer. Specifically, the feature importance are first calculated and sorted in ascending order. In each layer, features with the least importance are accumulated until reaching a given threshold. Then, these selected features are used to construct the first layer by training the CatBoost. Next, this trained CatBoost is utilized to generate a feature that adds to the feature set with their importance within a threshold. After that, all these feature are used to train the CatBoost in the next layer. This process is repeated until all the features are used. The results show that the proposed model has the best performance. Moreover, the statistical test conducted based on 20-runs of experiments validates the significant superiority of our proposed model over the compared models and demonstrates the efficacy of the multi-layer structure in enhancing the proposed model. This indicates our proposed model can help decision makers in enhancing educational quality.