ENHANCING SUBJECTIVE ANSWER EVALUATION THROUGH MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

This paper presents a machine learning and NLP-based system for automatically evaluating subjective (essay) answers in educational settings, aiming to provide automated scoring and feedback that aligns with human evaluators while reducing teacher workload. The system uses text preprocessing, feature extraction, and ML algorithms trained on annotated datasets to assess open-ended student responses.

This research introduces a pioneering framework that harnesses machine learning and natural language processing (NLP) to revolutionize the evaluation of subjective answers in educational contexts. Traditional methods of assessing essays and open-ended responses have been characterized by their labour-intensive nature and subjectivity. Our approach streamlines this process by employing NLP techniques for preprocessing, tokenization, and advanced feature extraction, followed by training machine le

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