ENHANCING SUBJECTIVE ANSWER EVALUATION THROUGH MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

Abstract

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 learning algorithms on diverse datasets of annotated answers. The result is a system capable of providing automated scores and feedback that closely align with human evaluators' judgments, demonstrating effectiveness and reliability across a spectrum of educational domains. Importantly, this automation not only enhances scalability and consistency but also lightens the workload on educators, allowing them to focus on more nuanced aspects of teaching. Beyond its technical contributions, our research addresses ethical considerations and challenges associated with the deployment of automated evaluation systems in educational settings. This comprehensive exploration encompasses concerns related to bias, transparency, and the overall impact on the learning experience. By navigating these ethical dimensions, our study not only advances the technological aspects of automated evaluation but also underscores the importance of responsible implementation within the educational landscape. This dual emphasis on technical innovation and ethical considerations positions our framework as a promising solution for achieving efficient and objective subjective answer assessment in educational contexts. Keywords: Machine learning, NLP, Subjective answer assessment, automatic scoring, feature extraction, consistency, feedback, teaching work load reduction, transparent evaluation