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Article

An Approximate Belief Rule Base Student Examination Passing Prediction Method Based on Adaptive Reference Point Selection Using Symmetry

1
The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
2
The School of College of Computer and Mathematics, Harbin Finance University, Harbin 150030, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2025, 17(10), 1687; https://doi.org/10.3390/sym17101687
Submission received: 26 August 2025 / Revised: 24 September 2025 / Accepted: 1 October 2025 / Published: 8 October 2025

Abstract

Student exam pass prediction (EPP) is a key task in educational assessment and can help teachers identify students’ learning obstacles in a timely manner and optimize teaching strategies. However, existing EPP models, although capable of providing quantitative analysis, suffer from issues such as complex algorithms, poor interpretability, and unstable accuracy. Moreover, the evaluation process is opaque, making it difficult for teachers to understand the basis for scoring. To address this, this paper proposes an approximate belief rule base (ABRB-a) student examination passing prediction method based on adaptive reference point selection using symmetry. Firstly, a random forest method based on cross-validation is adopted, introducing intelligent preprocessing and adaptive tuning to achieve precise screening of multi-attribute features. Secondly, reference points are automatically generated through hierarchical clustering algorithms, overcoming the limitations of traditional methods that rely on prior expert knowledge. By organically combining IF-THEN rules with evidential reasoning (ER), a traceable decision-making chain is constructed. Finally, a projection covariance matrix adaptive evolution strategy (P-CMA-ES-M) with Mahalanobis distance constraints is introduced, significantly improving the stability and accuracy of parameter optimization. Through experimental analysis, the ABRB-a model demonstrates significant advantages over existing models in terms of accuracy and interpretability.
Keywords: student examination pass prediction; belief rule base; educational assessment; evidence reasoning; evolutionary strategy student examination pass prediction; belief rule base; educational assessment; evidence reasoning; evolutionary strategy

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MDPI and ACS Style

Li, J.; Li, K.; Zhu, H.; Yang, C.; Han, J. An Approximate Belief Rule Base Student Examination Passing Prediction Method Based on Adaptive Reference Point Selection Using Symmetry. Symmetry 2025, 17, 1687. https://doi.org/10.3390/sym17101687

AMA Style

Li J, Li K, Zhu H, Yang C, Han J. An Approximate Belief Rule Base Student Examination Passing Prediction Method Based on Adaptive Reference Point Selection Using Symmetry. Symmetry. 2025; 17(10):1687. https://doi.org/10.3390/sym17101687

Chicago/Turabian Style

Li, Jingying, Kangle Li, Hailong Zhu, Cuiping Yang, and Jinsong Han. 2025. "An Approximate Belief Rule Base Student Examination Passing Prediction Method Based on Adaptive Reference Point Selection Using Symmetry" Symmetry 17, no. 10: 1687. https://doi.org/10.3390/sym17101687

APA Style

Li, J., Li, K., Zhu, H., Yang, C., & Han, J. (2025). An Approximate Belief Rule Base Student Examination Passing Prediction Method Based on Adaptive Reference Point Selection Using Symmetry. Symmetry, 17(10), 1687. https://doi.org/10.3390/sym17101687

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