Hybrid Deep Learning and USE Algorithm for Essay Scoring: Accuracy and Performance Analysis

The main challenge in digital education, particularly in the automatic assessment of essay answers in online learning systems, lies in the complexity of natural language understanding and semantic evaluation required to achieve the level of precision equivalent to human judgment. This study aims to develop and analyze the performance of a hybrid model that combines deep learning with a semantic similarity-based approach to essay auto-grading. The methods used include the collection of essay answer data from various disciplines, text processing to extract semantic representations, and the calculation of the degree of similarity between the participant's answers and the answer key using the similarity measure. The evaluation was carried out by comparing the results of automatic assessments with manual assessments by teachers. The results showed that the developed model achieved the highest accuracy level of 90.22% at 0.8 treshold, with a precision of 84.63%, a recall of 100%, and an F1 score of 91.68%. To strengthen the reliability of the findings, statistical validation was carried out using error evaluation metrics. RMSE value is 0.32 and RMAE value is 0.19. These findings show that the model is able to mimic human judgment reliably and consistently, and can distinguish linguistic variations that arise in different types of essay questions. This system offers an effective solution for the automation of assessments in an online learning environment, while maintaining the integrity and objectivity of the evaluation.