<![CDATA[Manual essay assessment is time-consuming and subjective. This study proposes an automated evaluation system using a linear regression algorithm to improve efficiency and objectivity. The model analyzes linguistic and structural features such as word count, sentence length, word complexity, and grammatical patterns. The dataset consists of student essay scored by tutors as training references. Natural Language Processing (NLP) techniques are applied to extract linguistic features and map reference scores using linear regression. The system helps instructors provide more consistent and efficient feedback while reducing subjectivity in grading. Additionally, this approach enhances assessment scalability, making it applicable to large numbers of essays. However, the model has limitations, particularly in understanding deep contextual meaning, creativity, and argument coherence. Future improvements may integrate advanced NLP models to enhance comprehension. Despite its limitations, this system presents a valuable step toward automated essay assessment in education]]>
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