A Student Performance Prediction Model Based on Hierarchical Belief Rule Base with Interpretability

Minjie Liang, Guohui Zhou (), Wei He, Haobing Chen and Jidong Qian
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Minjie Liang: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Guohui Zhou: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Wei He: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Haobing Chen: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Jidong Qian: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China

Mathematics, 2024, vol. 12, issue 14, 1-25

Abstract: Predicting student performance in the future is a crucial behavior prediction problem in education. By predicting student performance, educational experts can provide individualized instruction, optimize the allocation of resources, and develop educational strategies. If the prediction results are unreliable, it is difficult to earn the trust of educational experts. Therefore, prediction methods need to satisfy the requirement of interpretability. For this reason, the prediction model is constructed in this paper using belief rule base (BRB). BRB not only combines expert knowledge, but also has good interpretability. There are two problems in applying BRB to student performance prediction: first, in the modeling process, the system is too complex due to the large number of indicators involved. Secondly, the interpretability of the model can be compromised during the optimization process. To overcome these challenges, this paper introduces a hierarchical belief rule base with interpretability (HBRB-I) for student performance prediction. First, it analyzes how the HBRB-I model achieves interpretability. Then, an attribute grouping method is proposed to construct a hierarchical structure by reasonably organizing the indicators, so as to effectively reduce the complexity of the model. Finally, an objective function considering interpretability is designed and the projected covariance matrix adaptive evolution strategy (P-CMA-ES) optimization algorithm is improved. The aim is to ensure that the model remains interpretable after optimization. By conducting experiments on the student performance dataset, it is demonstrated that the proposed model performs well in terms of both accuracy and interpretability.

Keywords: education; student performance; behavior prediction; belief rule base; hierarchical structure; interpretability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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