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Related Experiment Videos

BERT-Enhanced HyperGAT with Siamese Networks and Reference Answer Set for Automated Short-Answer Scoring.

Chen Liu1, Xiaofen Wan1, Zhihao Ni1

  • 1Zhejiang Philosophy and Social Science Laboratory for the Mental Health and Crisis Intervention of Children and Adolescents, Zhejiang Normal University, Jinhua 321004, China.

Behavioral Sciences (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces HyperGAT-BERT-RAS, a novel AI framework for grading student answers. It enhances accuracy in text classification and scoring, potentially reducing teacher workload and improving formative assessment.

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Educational Technology

Background:

  • Automated grading systems require robust methods for semantic understanding and similarity assessment.
  • Existing approaches may struggle with the diversity of student responses and the nuances of grading.

Purpose of the Study:

  • To propose and evaluate the HyperGAT-BERT-RAS framework for enhanced automated scoring of student answers.
  • To improve the accuracy and reliability of AI-assisted grading systems.

Main Methods:

  • Integration of HyperGraph Attention Network (HyperGAT) with BERT for superior semantic representation.
  • Development of a Reference Answer Set (RAS) using clustering of high-scoring answers.
  • Application of Siamese Neural Networks (SNNs) for similarity-based scoring.
Keywords:
BERTSiamese neural networkautomated short-answer gradinghypergraph attention networkreference answer set

Related Experiment Videos

Main Results:

  • HyperGAT-BERT achieved 72.95% accuracy in Ohsumed text classification, surpassing baseline HyperGAT by 3.28%.
  • The complete HyperGAT-BERT-RAS framework reached 78.66% accuracy and a 0.7806 F1-score on ASAP-5.
  • The Reference Answer Set (RAS) was identified as a key contributor to performance improvements.

Conclusions:

  • The HyperGAT-BERT-RAS framework shows significant potential for reliable scoring of diverse student answers.
  • This AI-driven approach can reduce teacher grading burden and enhance formative assessment feasibility.
  • Further empirical validation with educators and students is recommended.