A student academic performance prediction model based on the interval belief rule base.

Student performance prediction (SPP) constitutes one of the pivotal tasks in educational data analysis. Outcomes from the prediction enables educators to implement targeted interventions for students. Therefore, developing an effective SPP model is of critical importance. The belief rule base (BRB) is a rule-based modeling approach that integrates expert knowledge and effectively manages uncertain information. Nevertheless, when employing traditional BRB to construct a prediction model, excessive input attributes and reference points may result in a combination explosion. Furthermore, in practical scenarios, the configuration of the model's parameters may be restricted by the limitations of expert knowledge. To overcome these challenges, an SPP model using an interval BRB structure based on the random forest (RF) attribute selection method (IBRB-C) is proposed. The parameters of the IBRB-C model are determined by combining the expert knowledge and the Kmeans++ algorithm. Subsequently, the P-CMA-ES algorithm is applied to optimize the initial model. Ablation experiment is conducted to validate the rationality of the IBRB-C. Finally, case studies on graduate applications and GPA of students demonstrate that the mean squared error (MSE) of the IBRB-C is 0.0024 and 0.1014, respectively. The results of comparative experiments confirm the superiority of the IBRB-C model in predicting student performance.