A Holistic Framework for Automated Answer Scoring: Unifying ‎Syntactic and Semantic Analysis - Deepender; Walia, Tarandeep Singh; Kumar, Vinay; Kumari , Pavitra; Sanju; Kumar , Narender (International Journal of Basic and Applied Sciences, 2025)
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.