Eval - Automatic Evaluation of Answer Scripts using Deep Learning and Natural Language Processing

Authors

Keywords:

Natural Language Processing (NLP), Bidirectional Encoder Representations from Transformers (BERT), third generation Generative Pre-trained Transformer (GPT-3), Long short-term memory (LSTM), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Gradient-Boosted Decision Trees (GBDT), Natural Language Toolkit (NLTK)

Abstract

Professors face a lot of difficulties when it comes to correcting handwritten answer booklets manually. It is both time consuming and labour intensive. As a solution to this problem, the paper proposes a system that automatically evaluates answer booklets, thereby saving time and effort. The proposed method involves using Deep Learning and Natural Language Processing techniques to automate the evaluation process. The first step is to extract the handwriting from input image files using an existing GCP OCR (Google Cloud Platform - Optical character recognition) text extract model, which has superior accuracy and performance to other models. It also uses various Natural Language Processing techniques such as BERT (Bidirectional Encoder Representations from Transformers) to extract keywords; and GPT-3 (Generative Pre-trained Trans- former 3) to summarize long answers. This method has been observed to assign marks that are usually identical to hand- evaluated marks. This paper also proposes a web application that simplifies the process of evaluating answer scripts. The web application generates the text extracted from both the student’s answer and the answer key image files, the summary of the student’s answer and the marks obtained based on the extracted keywords.

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References

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Use Case Diagram for Handwriting Recognition model

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Published

16.01.2023

How to Cite

M. S., P. ., M. Chavan, S. ., Bathula, R. ., Saikumar, S. ., & Dayalan, G. . (2023). Eval - Automatic Evaluation of Answer Scripts using Deep Learning and Natural Language Processing. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 316–323. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2541

Issue

Vol. 11 No. 1 (2023)

Section

Research Article

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