• Krishnendu Nair
• Pratik Musale
• Sharan Pillai
• Shounak Vettiyatil
• Om Shinde

Automated answer grading systems have the potential to revolutionize the way teachers evaluate student work. This paper presents a machine learning-based Django solution for automated answer grading that uses natural language processing and deep learning techniques. The system can analyze free-form text answers and provide scores based on various criteria such as accuracy, relevance, and language proficiency. We evaluate the performance of the system using a large dataset of student responses and demonstrate that it achieves high accuracy and consistency compared to human graders. . This technique aims to increase grading's effectiveness and impartiality, lessen human mistakes, and give students immediate feedback. It will be used in many different contexts, including education, hiring, and performance reviews. This technique will examine various ML methods for automatically evaluating descriptive answer sheets, as well as their advantages, drawbacks, and potential uses.

Natural Language Processing, Deep Learning, Bi-LSTM

"Automated Grading of Short Answer Scripts", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.d609-d615, February-2024, Available :http://www.jetir.org/papers/JETIR2402373.pdf

"Automated Grading of Short Answer Scripts", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppd609-d615, February-2024, Available at : http://www.jetir.org/papers/JETIR2402373.pdf