A Holistic Framework for Automated Answer Scoring: Unifying ‎Syntactic and Semantic Analysis

  • Authors

    • Deepender School of Computer Applications, Lovely Professional University, Punjab, India
    • Tarandeep Singh Walia School of Computer Applications, Lovely Professional University, Punjab, India
    • Vinay Kumar Department of Statistics, Central University of Haryana, Mahendergarh, India
    • Pavitra Kumari Department of Statistics, Central University of Haryana, Mahendergarh, India
    • Sanju Faculty of Agriculture, Guru Kashi University, Bathinda, Punjab, India
    • Narender Kumar Associate Professor, Dayanand College, Hisar
    https://doi.org/10.14419/f18ev204

    Received date: June 4, 2025

    Accepted date: June 27, 2025

    Published date: July 2, 2025

  • Automated Answer Scoring; RNN; LSTM; Natural Language Processing; RoBERTa; Syntactic ‎Feature; Semantic Feature.

  • Abstract

    In educational and assessment contexts, automated response scoring is crucial, especially for ‎effectively managing extensive assessments. This research integrates syntactic and semantic ‎data to present a novel hybrid model to handle issues in this domain. Recognizing the value ‎of integrating these complementary elements for improved comprehension and assessment ‎accuracy, the model has modular components that extract syntactic and semantic information ‎from responses. The hybrid model ensures a more reliable and flexible scoring system by ‎combining rule-based approaches with machine learning and deep learning techniques in a ‎unique way. The model gains more accuracy and adaptability by leveraging the advantages of ‎each method, which enables it to be used in a variety of educational settings. Experiments ‎were carried out utilizing a Hindi dataset to assess the hybrid model's efficacy. The model's ‎performance was evaluated using key performance indicators, including as accuracy, ‎precision, recall, and F1 score. The findings showed that the hybrid model works noticeably ‎better than conventional scoring techniques, offering superior outcomes in terms of accuracy ‎and flexibility. This suggests that the approach has the potential to completely transform ‎automated answer scoring in educational settings that are bilingual and culturally diverse. All ‎things considered, this hybrid strategy presents a viable way to enhance automated answer ‎scoring systems. This model can improve the fairness and scalability of assessments across ‎many languages and educational settings by combining syntactic and semantic elements and ‎utilizing cutting-edge technologies, which will help create evaluation procedures that are more ‎inclusive and dependable‎.

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  • How to Cite

    Deepender, Walia, T. S. . ., Kumar, V. . ., Kumari , P. ., Sanju, & Kumar , N. . (2025). A Holistic Framework for Automated Answer Scoring: Unifying ‎Syntactic and Semantic Analysis. International Journal of Basic and Applied Sciences, 14(2), 572-576. https://doi.org/10.14419/f18ev204