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@article{172478,
        author = {Sejal Jaysing Jagtap and Vaishnavi Nitin Joshi and Sanika Sunil Nikam and Nandini Vijay Randive},
        title = {Subjective answer evaluation using machine learning and natural language processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3566-3572},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172478},
        abstract = {This paper presents an Automatic Subjective
Answer Evaluation (ASAE) system that automates the
grading of subjective answers using machine learning
and natural language processing (NLP)The evaluation
of subjective answers plays a crucial role in the teaching
and learning process. With the growing need for
efficient and accurate grading systems, automatic
evaluation of answers has become essential. However,
existing systems often yield mediocre results, especially
when evaluating short or long subjective answers.
Traditional methods focus on keyword matching
between the student's response and a reference answer,
but these systems fail to deliver optimal results. Short
answers, with their limited number of keywords,
require special attention, particularly in calculating the
weighting score. This study aims to evaluate the
performance of existing frameworks for automatic
grading of long and descriptive answers and suggests
improvements for better accuracy and consistency. By
analyzing the current mechanisms, this research seeks
to enhance the overall effectiveness of automated
answer evaluation systems},
        keywords = {Automated Essay Evaluation, Automatic Scoring, Automatic Grading, String Similarity, ContentBased Similarity, Natural-Language Processing},
        month = {May},
        }