Authors : Ganesh Prasad Tamminedi; Sri Abhirama Maganti; Tarush Chandra

Volume/Issue : Volume 9 - 2024, Issue 10 - October

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Google Scholar : https://tinyurl.com/7nhy6ans

Scribd : https://tinyurl.com/34yzdpnn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT205

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The goal of this study, "Automation of Answer Scripts Evaluation," is to create an end-to-end automated process that can quickly and fairly evaluate answer scripts and grade students. Optical Character Recognition (OCR), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) are brought together to build a workflow for automating this tedious, time taking, subjective activity. The paper discusses failures and successes of various models applied in our endeavour.

Keywords : OCR Model, Bert Model, NLP, GPT Model, Optimization, Cosine Similarity, Vectorization, Rubric Model, Evaluating Model, Datasets, Ensemble, Majority Voting, Gradient Descent.

References :

1. AUTOMATION OF ANSWER SCRIPTS EVALUATION-A REVIEW Ravikumar M1, Sampath Kumar S1 and Shivakumar G1
2. Grade Aid: a framework for automatic short answers grading in educational contexts—design, implementation and evaluation Emiliano del Gobbo1 · Alfonso Guarino2 · Barbara Cafarelli1 · Luca Grilli1
3. NLP-based Automatic Answer Script Evaluation Md. Motiur Rahman1 and Fazlul Hasan Siddiqui2*
4. An Automatic Short Answer Correction System Based on the Course Material Zeinab Ezz Elarab Attia1* Waleed Arafa1 Mervat Gheith1
5. Automatic Evaluation of Descriptive Answers Using NLP and Machine learning. Prof. Sumedha P Raut1, Siddhesh D Chaudhari2, Varun B Waghole3, Pruthviraj U Jadhav4, Abhishek B Saste5