Abstract:
AUTOMATED GRADING SYSTEM for essays refers to the utilization of computer
programs to evaluate and score essays written in response to specific prompts. It involves
automating the assessment process, which offers benefits to both educators and learners by
facilitating iterative improvements in students’ writing skills. In traditional grading methods,
evaluators need to manually read and evaluate each paper, which can be a time-consuming
process, especially when dealing with a large number of papers. Automated grading systems
leverage the power of machine learning algorithms to analyze essays and provide accurate
grades. By implementing these systems, institutions can significantly reduce the time required
for grading papers, allowing teachers to focus on other important tasks such as providing
feedback to students. The proposed grading system mentioned in the project aims to use
machine learning algorithms such as Linear Regression, support vector regression (SVR), and
Random Forest (RF) to automate the grading process. By analyzing various features extracted
from essays and incorporating natural language processing techniques, the system aims to
accurately predict scores for essays in a timely and efficient manner. The effectiveness of the
system is evaluated using mean squared error as a performance metric. The results demonstrate
the potential of machine learning models in automating the grading process and providing
reliable feedback to students.
