Published Paper Details:

Evaluating subjective answers remains a major challenge in educational assessment due to the wide variation in students' writing styles, vocabulary, and expression of concepts. Traditional grading methods, such as manual evaluation or rule-based systems, are time-consuming, inconsistent, and unable to capture the true semantic meaning of answers. This paper presents a machine learning-based framework for automated subjective answer evaluation that leverages Natural Language Processing (NLP) techniques to assess student responses more effectively. The proposed system utilizes semantic embeddings and transformer-based architectures to analyze the contextual meaning of answers, enabling it to recognize paraphrased expressions and evaluate the relevance, completeness, and coherence of responses. The model is trained and validated using the ASAP-SAS dataset and further adapted to evaluate teacher-uploaded question papers. Standard evaluation metrics such as Quadratic Weighted Kappa (QWK), F1-score, and Pearson correlation are used to measure performance. The system aims to reduce manual effort, improve grading fairness, and provide timely feedback to students. Experimental outcomes demonstrate that machine learning can offer a scalable, consistent, and intelligent alternative to traditional grading, enhancing both the efficiency and reliability of academic assessments.