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An Interpretable Soft-Sensor Framework for Dissertation Peer Review Using BERT.

Meng Wang1, Jincheng Su1, Zhide Chen1

  • 1Graduate School & School of Computer and Cyberspace Security, Fujian Normal University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
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This study introduces an interpretable soft-sensor model using BERT and SHAP to analyze graduate dissertation evaluations. The approach accurately quantifies key academic assessment dimensions, improving quality monitoring in big data environments.

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Educational Technology

Background:

  • Graduate education faces challenges in quality monitoring due to complex, subjective dissertation evaluations.
  • Existing automated analysis methods struggle with nuanced disciplinary criteria and lack interpretability for educators.

Purpose of the Study:

  • To develop an interpretable soft-sensor framework for quantifying latent evaluation dimensions in dissertation reviews.
  • To enhance automated analysis of peer-review texts using advanced natural language processing techniques.

Main Methods:

  • Employed a BERT-based model with attention mechanisms for deep semantic modeling of dissertation reviews.
  • Integrated Shapley Additive exPlanations (SHAP) to ensure model prediction interpretability and quantify characteristic importance.
Keywords:
BERT-based modelShapley Additive exPlanations(SHAP)dissertation reviewsinterpretable soft-sensor

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  • Developed an interpretable soft-sensor paradigm combining NLP with substantive review principles.

    • Main Results:

    • The BERT-SHAP model significantly outperformed baseline methods in accuracy, precision, recall, and F1-score.
    • The interpretability mechanism successfully identified key evaluation dimensions prioritized by experts.
    • Demonstrated the model's effectiveness in providing actionable insights for dissertation improvement.

    Conclusions:

    • The developed framework offers a novel, interpretable approach to analyzing complex academic evaluations in the era of big data.
    • This soft-sensor paradigm bridges NLP advancements with essential review principles, enhancing dissertation quality assurance.
    • Provides a scalable and interpretable solution for educators and institutions to monitor and improve graduate education standards.